What an Artificial Intelligence (AI) Benefits in Today’s World

Over the past few decades, Artificial Intelligence (AI) has received several buzzes. However, it is still one of the top technology trends due to tremendous impacts on our lifestyle, workspace and games.

AI has proven dominance in personal assistants (Alexa, Siri and Google Assistant), image and speech recognition, ride-sharing apps (Uber, Ola, Lyft and more) and navigation apps (Google Maps, Apple Maps).

Between the years 2019 and 2020, AI practitioners' or specialists' recruitments were seen to increase by 32%, and in LinkedIn's emerging job report 2020, the role of an AI specialist was ranked #1. 

This tech trend needs profound knowledge of statistics, helping us to identify the outcomes generated by the algorithm for a particular dataset.

Some of the highest-paying jobs in the AI field include AI Architect, Robotics Scientists and Big Data Engineering.

What AI Actually Means?

When time travelled to the 1950s, Minsky and McCarthy described the tech trend as any task executed by a machine that was previously considered to need human intelligence.

However, according to modern definition, AI is the ability of a digital computer to execute tasks that are associated with skillsets. It is often applied to the developing system projects invested with the intellectual method characteristics of humans like the potential to reason, identify insights, or learn from prior experience.

The History of AI

Artificial and intelligent bots first appeared in ancient Greek myths of Antiquity. Aristotle's syllogism development and its use of deductive reasoning was a crucial point in a human's quest to gain insights into its own intelligence.

Following are significant event timelines in AI:

1943 - 1949

In 1943, Walter Pits and Warren McCulloch released 'A Logical Calculus of Ideas Immanent in Nervous Activity,' the first work on AI in that year. The paper suggested the first mathematical model for developing a neural network.

In 1949, in his book 'The Organization of Behavior: A Neuropsychological Theory, Donald Hebb proposed that neural pathways are developed from experience and connections between neurons become firmer the more frequently they are used.

1950 - 1959

In 1950, Alan Turing published a theory 'Computing Machinery and Intelligence' proposed a test called Turin Test that identifies whether a machine has the potential to exhibit human behaviour or not. The same year, Marvin Minsky and Dean Edmonds, Harvard graduates, developed their first neural network computer named SNARC.

In 1952, Arthur Samuel built a self-learning algorithm to play checkers.

In 1954, the Georgetown-IBM machine translation test automatically translated 60 Russian sentences into English.

In 1956, Allen Newell and Herbert A. Simon developed the first AI program called Logic Theorist that verified 38 of 52 math theorems, followed by discovering new and more proofs. The same year, AI was first adopted by John McCarthy at the Dartmouth Conference, which defined the scope of AI, and is widely considered the birth of tech trend as we know it now.

In 1958, John McCarthy built the AI programming language - Lisp and published 'Programs with Common Sense.'

In 1959, J.C. Shaw and Newell and Simon created the General Problem Solver (GPS) - designed to mimic human problem-solving. The same year, Herbert Gelernter created the Geometry Theorem Prover program, Arthur Samuel coined Machine Learning (ML) while at IBM, and McCarthy and Marvin Minsky founded the MIT AI Project.

1963 - 1969

In 1963, McCarthy established the AI Lab at Stanford.

In 1966, Joseph Weizenbaum developed the first-ever chatbot - ELIZA.

In 1969, the first successful proficient systems were created in DENDRAL, a XX program, and MYCIN, developed to determine blood infections, were made at Stanford.

1972 - 1980

In 1972, the PROLOG, a logic programming language, was created. In Japan, the first humanoid bot - WABOT-1 was built the same year.

From 1974 to 1980 was known to be the first AI winter period, where researchers couldn't pursue their studies to the best extent as they were short of funds from the govt, resulting in a gradual decrease of AI interests.

In 1980, AI came back with much greater force. The first successful commercial proficient system - R1, was developed by Digital Equipment Corp. The same year, the national conference of the American Association of AI was organized at Stanford Univ.

1982 - 1997

In 1982, the Japan Ministry of International Trade and Industry introduced the 5th Gen Computer System (FGCS) project for developing supercomputer and AI development platforms.

From 1987 to 1993, AI entered the second winter period; as an emerging computer tech and inexpensive alternatives, investors and the govt stopped funding for AI research.

In 1997, the machine took a significant twist, with IBM's Deep Blue defeating world chess champion - Gary Kasparov, thus becoming the first computer to do so.

2002 - 2020

In 2002, The introduction of vacuum cleaners made way for AI to enter our households.

In 2005, an autonomous car - STANLEY won the DARPA Grand Challenge. The same year, the US military started investing in robots like iRobot's PackBot and Boston Dynamics' Big Dog.

In 2006, tech giants such as Google, Netflix, Facebook and Twitter started leveraging AI.

In 2008, US tech giant - Google made an innovation in speech recognition and launched the feature in the iPhone app.

In 2011, an IBM computer - Watson won Jeopardy, a game show that solved complex riddles. The computer displayed it could comprehend plain language and solve complicated questions quickly.

In 2014, Google made the first autonomous car which passed the driving test. The same year, Alexa - an Amazon product, was launched.

In 2016, Hanson Robotics developed the first robot citizen - Sophia, capable of reading facial emotion, verbal conversation and face recognition.

In 2018, Google launched the NLP engine - BERT, which minimized translation and understanding barriers by ML applications. Waymo introduced its One Service the same year, allowing individuals to request a pick-up.

In 2020, Baidu introduced its LinearFold AI algorithm to medical and research teams working to create a vaccine during the early phase of the SARS-CoV-2 pandemic.

Why AI?

The significance of AI is to help us make an advanced decisions with profound scenarios. Moreover, the tech trend can assist us in having a meaningful life without hard labour and managing a complex web of interconnected individuals.

Let’s see why AI is so important these days:

Existing Product Improvements

AI adds intelligence to the existing services and products. Several products that we come across or use in our routine life are being enhanced with AI potentials, much like Alexa and Siri, which were added as virtual voice assistant features.

To enhance technologies, automation, transforming platforms, intelligent machines, and bots can be incorporated with massive data. If you look at your workplace and home, AI has upgraded the range from security intelligence and intelligent cameras to investment analysis.

Deeper Data Analysis

Leveraging neural networks with several hidden layers, AI will analyze more and deeper data. Developing a fraud detecting system with five hidden layers was once impossible, but not anymore, thanks to supercomputer power and big data.

To train a deep learning algorithm, you need massive data sets to gain insight directly from data.

Automates Iteration Learning

Instead of automating manual works, AI executes frequent, massive, computerized ones. Though humans are vital for the system set-up and queries, AI needs automating iteration learning and discovery through data.

Progressive Learning Algorithms

To enable the data to do all the programming work, AI modifies through advanced learning algorithms. AI finds data regularities and structures so that algorithms can obtain skills. Like an algorithm undergoing self-study to play chess, it can teach itself what product to recommend following online.

Top-notch Accuracy

Through deep neural networks, AI obtains top-notch accuracy. For instance, your interactions with Google and Alexa are based on Deep Learning (DL). These products are getting more precise when leveraged regularly.

In the medical industry, AI methods from DL and object recognition can be leveraged to determine cancer on medical images with enhanced precision.

What are the Different Types of AI?

It is significant to note that AI has enabled robots to differentiate images and texts, understand vocal commands, and much more than a person can do. For instance, Alexa from Amazon, Siri from Apple, and Hello Google from Google are AI applications that can easily understand and perform vocal commands.

Given the rate at which ML, DL, NLP, predictive AI, and other related concepts are emerging, it isn't a little dream to expect that a day would arrive when machines would walk among us, seamlessly offering all human actions.

Current AI systems can do complex calculations at a high rate, followed by the ability to process large data sets and generate precise predictions.

In terms of development, AI is divided into four parts:

Reactive Machines

This type of AI is solely reactive, without developing memories or creating judgments based on prior experiences. These devices are designed to execute specific duties. Programmable coffeemakers and washing machines, for example, are built to fulfil particular functions but lack memory.

Let's take the example of IBM's Deep Blue chess computer. It defeated international grandmaster Garry Kasparov six times in a row. The technology could recognize chess board pieces and understand how they moved.

Its superior intelligence allows it to guess all of the opponent's possible movements far faster than a human opponent. As a result, it could compute the best moves for each scenario.

AI with a limited memory

This type of AI makes decisions based on previous experiences and present data. These machines have limited memory and integrated a memory-running application; they cannot generate new concepts.

Modifications in these machines demand re-programming. Limited memory AI is exemplified by self-driving automobiles, where they can monitor the speed and direction of other vehicles.

Mind-Body Theory

These AI computers can socialize and understand human emotions and a cognitive understanding of people based on their surroundings, facial traits, and other factors.

Such powers have yet to be developed in machines. This sort of AI is the subject of a lot of research.

Self-Awareness

This is a type of AI where machines will be equipped with technologies to be self-aware of their surroundings. This phase is also a continuation of the Mind-body Theory phase, in which devices will be aware of themselves for a reason.

This will elevate the machine's intellect to an entirely different level. Though AI researchers have a long way to travel before these machines are in practical use; however, present AI scientists are focusing on enhancing these computers' ML potentials. The potential of devices to respond similarly to humans is increasing each day.

What are the Benefits of AI?

AI has been lingering around us for quite a while now and is a part of our routine life - including web search recommendations to robot attendants at shopping malls.

By integrating AI into every organizational aspect, businesses are getting optimized, gaining a competitive edge, and finally leading to successful profit growth.

AI in business has numerous innovation potential and will continue to transform the world we know today in a wide range of ways.

The top 10 benefits of AI in business are as follows:

1. Global Defense

The most innovative and advanced robots across the globe are being developed with global defence applications. This is one of the reasons why breakthrough techs get first implemented in the military. However, most of them don't see astronomical days.

One example of it is the AnBot, a Chinese developed AI bot with the potential to reach a maximum of 11mph. The idea behind this tech is to patrol areas, and in case a danger spurs up, this bot can deploy an electrically charged riot control tool.

The 1.6m height AnBot can identify individuals having criminal backgrounds. It has contributed to the improvement of security by maintaining a track of malevolent acts around its territory.

2. Solve Complex Issues

Over the years, the AI tech trend has evolved from a basic ML algorithm to an advanced level such as deep learning. The expansion of AI has helped organizations solve complex problems like weather forecasting, cybercrimes, malevolent activity detection, medical diagnosis and many more.

An example of leveraging AI for fraud detection is PayPal, and you may be wondering how?

Well, because of a breakthrough technique - deep learning (DL), the US multinational financial tech giant can now find out the possible suspicious activities accurately. The company processed around $235Bn payments from 4Bn transactions by more than 170Mn users.

ML and DL algorithms do data mining of users' purchase history to determine possible fraud patterns stored in its databases and can tell whether a transaction is deceitful or not.

3. Automation

From tasks involving strenuous labour to hiring procedures, AI can be leveraged to automate nearly anything. There is infinite count to AI-related applications that can be leveraged to automate the hiring process.

Such a system will assist in freeing the workforce from hectic manual tasks and schedules and allow them to shift their attention to complex tasks such as decision-making and strategizing.

A classic example of this type of recruitment is the conversational AI recruiter MYA, which focuses on automating monotonous parts of the hiring procedure, such as candidate screening and sourcing. This technology is trained by advanced ML algorithms and leverages NLP to acquire conversational details.

MYA is also accountable for creating candidate portfolios, executing analytics, and, lastly, shortlisting applicants.

4. Repetitive Task Management

Executing iteration tasks can be a tedious and time-consuming procedure. Leveraging AI for these routine tasks helps us focus on essential tasks requiring immediate actions.

An example is the Virtual Financial Assistant named Erica, used by the Bank of America. This technology implements ML and AI methodologies to serve the customer service demands. Erica does this by developing credit report updates, assisting bill payments and aiding customers with streamlined transactions.

Recently, Erica's potential has been expanded to help end-users make better financial choices by offering them customized insights.

5. Disaster Management

Precise weather forecasting allows farmers to make critical choices about farming and harvesting. It makes shipping seamless and secure. Most significantly, accurate weather forecasting can be used to predict natural calamities that take a toll on millions.

After much research, IBM collaborated with the Weather Company and obtained massive data sets. This alliance gave IBM the chance to access the Weather Company's predictive models, thus offering numerous data to feed into IBM's AI - Watson for prediction enhancement.

The product of this alliance is IBM Deep Thunder, which generates highly personalized data for business clients.

6. Economy

Though AI has been a criticism victim; however, according to the PwC report, it is estimated that AI will contribute around $15Tr to the global economy. By 2030, AI’s continuous advancement will maximize the global GDP by 14 percent.

The report also highlighted that approx $6.6Tr of the expected GDP growth will come from productivity gains of routine task automation and innovative bot developments.

7. Lifestyle Improvements

Since AI's emergence in the 1950s, we have witnessed significant growth in its offerings. We leverage AI-based virtual assistants to interact with other devices to diagnose deadly diseases.

The online shopping platform - Amazon and Flipkart monitors our browsing habits and serves up products it thinks we'd buy. Even Google decides what outcomes to offer us based on our search activity.

8. Productivity

In recent years, AI has been necessary to control highly computing tasks that need maximum time and effort. Businesses these days rely on AI-based applications to increase their productivity and growth.

An example is a Legal Robot that used ML techniques to gain insight and analyze legal documents, find and resolve legal errors, partner with experienced proficient, and many more. It also allows us to compare our contract with others in the same sector to ensure yours is standard.

9. Personalization

Brands that master personalization delivery results in the improved sale by more than 10 percent than other organizations that don't personalize. Personalization can be a tedious and time-consuming task for an individual, but with machines and technologies, these tasks can be simplified.

An example of personalization is the UK-based fashion firm Thread, which utilizes AI to offer personalized clothing suggestions for each customer.

Each week, customers get personalized suggestions that they can vote accordingly. The company uses an ML algorithm called Thimble that utilizes user data to determine patterns and understand buyers' likes.

10. Decision-making

One of the crucial factors that AI must focus on is intelligent decision-making. The technology must help businesses or organizations to make wiser decisions regarding their offered products and services.

A comprehensive AI for CRM - Salesforce Einstein has managed to do effective decision-making. It eradicates the complexity of AI and enables companies to offer personalized customer experiences.

Driven by DL, ML, NLP, and predictive modelling, Einstein is implemented in extensive businesses to identify relevant insights, forecast market behaviour, and make intelligent decisions.

What are AI Applications?

Some of the applications of AI are as follows:

Medical Industry

Medical care is a basic service for all citizens around the world. While doctors and scientists work hard from time to time to bring about a medical revolution, AI does its part by offering great contributions to the sector. The role of AI in healthcare enables machines to interpret the patient’s medical history and predict possible diseases that individuals may be susceptible to in the next few years.

In addition, AI facilitates drug discovery and the development of drugs that can cure harmful and even fatal diseases.

An example of AI in medical industry is IBM Watson Health, a medical company that actively integrates AI into the healthcare industry. It assists in research, data analysis and offers clients with medical solutions.

Customer Service

AI makes it easier to integrate machines into customer service. A computer that is designed to record feedback from various customers visiting major brand stores, shopping centres or showrooms.

ML chatbot technology is one of the best AI applications that simulates the human behaviour and dialogue methods of marketers. The chatbots communicate with customers through online platforms to clear their queries to an extent.

For example, the e-commerce platform Amazon incorporated chatbots into its customer service department. This is where customers are tricked into chatting with bots who want to resolve issues or disagreements related to purchases, orders, etc.

Finance and Stock Market

With the help of AI algorithms, machines can now interpret past stock market developments, analyze the profit and loss of related stocks, and even predict future stock market developments.

The tech been a major contributor in the financial field recently, one of the financial companies, Nomura Group, has successfully implemented AI technology.

In addition, AI is seeking different methods to enter financial processes, such as payment transfers, e-commerce platforms, and many other.

Fitness Apps

Fitness is everything that people desire in the modern world. From the number of steps, you walk in a day to the number of calories you burn, fitness plays a huge role in our lives.

AI has formed an alliance with fitness to promote the launch of fitness equipment, such as fitness bracelets and watches, to help people recognize their health, boost their physical fitness, and achieve goals.

The role of AI is to interpret the data that occurs every day and predict future data based on the data analysis. An example is Fitbit, that trades with fitness bands powered by AI.

About Us

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world.

For more information on how iCert Global can help you to achieve your ML, AI and Deep Learning (DL) Certification goals, please visit our website.

https://www.icertglobal.com/

We provide instructor-led classroom and online training across the globe, followed by Corporate Training for enterprise workforce development.

A Step-by-step Beginner’s Guide to Project Management

Whether you are a freelancer, a marketer, an IT professional, a creative director or someone who's just stepped into a manager's role, project management is significant in achieving business goals. It helps enhance organizational efficacy, improve team performance and affect the bottom line.

Though, not all are project managers, even if it's common in all business aspects. However, understanding its complex terminologies and concepts can aid you in taking your project managing career to the next level.

In today's article, we will see in detail the definition of project management, its vitality, roles and responsibilities of these managers, different project stages, management methodologies and many more.

People familiar with a few of these terms can skip to the main part; this blog mainly focuses on offering beginners an insight into project management, like the main title, a beginner's guide. So, let's begin!!!!!

What is Project Management?

It is a segment in an entire organization’s workflow, where the project is planned, monitored, managed and reported. Without the relevant resources, scope and money, a project will not succeed. Project managers must ensure that all these conditions are tackled, accounted and leveraged when necessary.

Each project has a particular start and completion date. For a project to have a successful delivery, the proven management processes include the following:

• Plan and schedule
• Management of resource
• Risk management
• Task controlling and monitoring
• Reporting

The project management techniques are leveraged in all kinds of industry verticals irrespective of their sizes, each with its own unique methods to complete the tasks and achieve targets. To offer desired deliverables and meet clients' demands, project managers and the team are responsible for the planning and execution of a project.

Why Project Management is Significant?

According to PMI.org, companies that don't value project management have noted 67% of project failures. Moreover, the report highlights that poor project management resulted in an 11% investment wastage.

Project management is significant as it sets your project from being out of scope. Let us look at some of why project management is vital for an organization.

• Minimizes project delivery risks
• Creates plans for project execution and meet business objectives
• Enhances planning by offering realistic work measures
• extract insights from previous projects
• Ensuring alignments between the team and the delivered value
• Offers project direction and leadership
• Ensures that the project sticks to the given schedule
• Ensure seamless interaction between the project team and the end-users

What are the Roles of Project Management?

A project doesn't happen in isolation; instead requires professionals to execute them. Some of the traditional project management roles are as follows:

Project Manager

They are responsible for planning, monitoring, and managing a project. Moreover, project managers also lead their team, manage suppliers, and guide the project towards successful delivery.

Program Manager

The project manager, be it a single entity or more, has to report to a program manager, as they are in charge of a set of related projects.

Account Manager

They are senior managers, also known to be the project sponsors, responsible for the project deliverables. They are the client's communication source and a single point of contact between end-users and the project team.

Team Members

They are skilled professionals responsible for the project execution and the deliverable production.

Suppliers

External teams authorize part of the project when skilled professionals are not available.

Shareholders

These people, either directly or indirectly, are impacted by the project. Their feedback and input are strived to define the deliverables of a project.

Project Portfolio Managers

Like the name, these managers are responsible for the project management belonging to one or more clients across the company.

What are the Roles of Project Managers?

Project managers are the plan coordinators who ensure that significant resources and members are leveraged to their utmost potential for target achievement in the optimal period.

We had seen the roles of project management, now let us know what a project manager does?

Report Management

A potential project manager knows that post-project reports are vital for ensuring all the project prerequisites are delivered and about the future methodology and plan enhancement.

Team Organization

These managers ensure that they have suitable skillsets for the task, and they leverage team members wherever appropriate. Project managers create plans that direct people towards task completion and achieving business objectives and empower the team to realize their capability.

Budgeting

It's essential to deliver a project that meets clients' demands and is delivered on time. By chance you exceed the project budget, your project will be considered a failure. A project manager must know how to prepare, control and ensure that budgets don't override the estimated cost.

Time Management

One of the biggest challenges for a manager or team leader is to bring people together for goal accomplishment, as everyone works at their own pace. The responsibility of project managers is to set realistic deadlines and precise interaction with the team regarding the deadline. A potential manager does know when to transform resources when specific tasks are at risk.

Monitor Project Progress

The initial project outline created by the project managers offers the team a successful route, but it doesn't mean you won't face any hurdles. This is where we need a project manager. They look for corrective measures to ensure that the project is delivered on time and within the budget.

What are the Project Management Stages?

From the initial point to the final, every project undergoes 4 different stages that define the project lifecycle, and they are:

Initiation Stage

The initiation stage is the starting level of a project where individuals involved must have insight into objectives, scopes, risks, and project priorities.

Planning Stage

Here, the project programmes and needed resources are determined. Managers in this stage create an estimated deadline for completing the project.

Execution Stage

Proficient team members join together to transform the project plan into deliverables. During this stage, project managers monitor the progress on a routine basis.

Closure Stage

This is the final stage of the project lifecycle, where the team hands over the deliverables, analyze project performance and finally completes them.

What is Triple Constraint Theory in Project Management?

Triple Constraint theory is defined as the project's success that is impacted by its cost, scope and time. Any modification in any one factor will directly affect the rest. Let us understand it better with an example, shall we? If there are unexpected modifications in the project and your client wants the deliverable at a swift turnaround time, it will directly affect the processes. You must ramp up, maybe charge more and meet the altered deadlines.

As a project manager, you can maintain the concept by balancing these constraints via trade-offs and ensuring everyone's demands are met to create a successful project. Though the idea is a significant part of the project, it doesn't determine success, as projects are made from several functions. That is why few management professionals have added extra 3 constraints to the existing model to better reflect critical project areas.

• Quality - Whether a project is a final deliverable or a tangible product, each project has quality norms. Project managers need a specific quality management practice to control project quality.
• Risk - This is an inherent aspect of any project, and this is one of the reasons why project experts need to develop a risk management plan to define how the risks can be tackled.
• Benefit - Different gifts are obtained from a project; hence managers must ensure that project shareholders get the best financial help.

Why Triple Constraints in Project Management?

If you are more open to the Triple Constraints idea and start paying a little close attention, you can change the way your members react to the concept and to the rest of the project on the verge of completion. When you have a better insight into what to expect, it's a lot easier to complete the tasks.

Understanding the Scope

It's easy to get caught in scope creep; hence, communicating with the team about the project requirements is always better. If you don't go in detail and your team is not familiar with the different project aspects due to lack of communication, the team might likely go beyond what's expected of them as they believe something isn't a project part.

Set Suitable Deadlines

Whether the project is short-run or long-run, setting small targets and deadlines will assist your team to function seamlessly and give them satisfaction as they go past each objective. Observe these deadlines and targets to maintain a progress track and calculate the planned timeline vs the actual one for target completion. This will help in effective accountability and offers progress clarity.

Know the Budget

When you provide the budget to your client, you will be highlighting every detail of expenses, which will help you keep track of the budget. You have to ensure that the team is aware of the budget and how you are doing about it, so you can find where to adjust the cost to make sure you are sticking near to the original expense.

How Triple Constraints Work in a Project?

Project managers can either maximize or minimize the cost, scope and timeline of a project with trade-offs to keep it on schedule and under budget. Let us see how these works:

• Time and scope - If you are running behind the schedule, you can reduce the project scope and time. In reverse case, you can maximize the project duration if shareholders come up with additional project schemes.
• Cost and scope - By minimizing the scope of a project, you can perform fewer practices, which leads to lower expenses. In other case, a massive scope means higher expense.
• Cost and time - In some projects, cost and time are directly dependent such as the cost of renting equipment or labor.

In all these cases, we can see the application of the Triple Constraints for project management; however, several other trade-offs can happen during a project, such as benefit, quality and risk.

With the help of a project management dashboard, a manager can keep a close watch on the progressing project. Metrics like scope, schedule and expenses are easy to keep sight of. A project manager can determine problems and adjust the Triple Constraints to prevent them from developing more significant issues.

The project manager features a real-time dashboard that indicates all crucial data of a project impacting the Triple Constraints.

What are Different Project Management Methods?

In this article, you came across the 4 different project stages. Now, executing these stages is totally up to you and your team. And over the year, these management experts have come up with trial methods that make it easier. 3 popular project management methodologies are:

Lean Project Management

It is a repetitive method that aims at the minimization of waste. This method strives to minimize three kinds of waste: unevenly distributed workloads, unwanted activities, and overburdened team members.

Agile Project Management

This method also follows a repetitive practice, where each project stage is time-boxed, and the entire project aspects are iteratively delivered. Agile project management methodology works for projects where you want to experience swift success and can be built over iterations.

Waterfall Project Management

This follows a linear method to project management for collecting detailed requirements, creating plans, developing solutions, testing, and delivery. The waterfall methodology works excellent when the conditions are accurate and precise.

Is a Career in Project Management Worth It?

The increasing demand for project management in a company means that it's an excellent choice for the enthusiast who wants to be a part of a critical project management role to achieve business objectives.

As a project manager, you can make a real impact on aiding companies to hit their goals and develop a working environment where teams can flourish.

But what is the real reason for choosing a project management career?

If you are that person who loves to manage projects and can excel in working with several shareholders across different teams, then this is for you.

Project managers are business superiors, helping transform intangible values and objectives into a solid outcome. Be it on the construction or software development side, these managers have a great choice to make their mark on the venture and enhance how the task gets done.

If you want to make a huge difference to teams, then project managers are always looking for new-flanged technology and methodologies that can assist teams to work closely more efficiently and productively.

What Are Project Management Certifications?

There are several project management certifications that you could consider, from the PMI or otherwise, and popular among the certifications are:

Project Management Professional (PMP)

The Project Management Professional (PMP) is a globally-renowned project management certification accredited by Project Management Institute (PMI), that indicates the experience, education, skill and competency needed to direct and lead projects.

With predictive, agile and hybrid methods, the certification proves project leadership experience, high-gearing careers for project leaders across different industry verticals and assisting companies in hiring individuals to work smarter and better.

Scrum Certifications

It is a project management scheme that signifies iteration and adaptability and is an Agile methodology. Becoming a Scrum Master means you will be assisting projects to suit the scrum practise for better outcomes. Popular certifications include CSM and PSM.

Agile Certifications

Agile is a project management approach that targets adaptability and swiftness through small-scale and seamless delivery. The popularity of these practices has been witnessed in recent years. Popular Agile certifications are PMI-ACP and SAFe certification.

Certified Associate in Project Management (CAPM)

The Certified Associate in Project Management (CAPM) is an accreditation offered by the Project Management Institute (PMI) for individuals entering the project management profession or those seeking to obtain the fundamentals of project management knowledge.

The certification will help them to work well in a project environment and acknowledge challenges in a more organized manner. 

About Us

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world.

For more information on how iCert Global can help you to achieve your ML, AI and Deep Learning (DL) Certification goals, please visit our website.

https://www.icertglobal.com/

We provide instructor-led classroom and online training across the globe, followed by Corporate Training for enterprise workforce development.

How Machine Learning Became a 2022 Tech Trend?

Machine Learning (ML) has transformed our lives for the past few decades. From taking pictures with a blurry background & focused face to virtual assistants such as Alexa, Google Assistant and Siri answering our queries, we are significantly dependent on applications that execute ML at their core.

ML engineering combines data science and software engineering. A data scientist will scrutinize the obtained data and extract actionable insights; an ML engineer will develop the self-running software that leverages the extracted data and automates predictive models.

These engineers are experienced in basic data science skills like quantitative analysis methods, stats, data structures & modelling, and developing data pipelines, while also having fundamental software engineering skills.

With so much innovation around this emerging technology, it is no wonder that any enthusiast who is looking forward to advancing their career in software technology & programming would choose ML as a base to set their job.

In this article, we shall see what ML and ML engineer is? Its significance, different types, and so on.

What is Machine Learning (ML)?

ML is an AI and computer science branch that focuses on using algorithms and data to mimic how we humans learn, gradually enhancing its accuracy. Because of new computing techs, ML today is not like it used to be. Its potential to automatically apply complex mathematical calculations to big data repeatedly and faster is a recent development.

As ML is a central factor of data science, through statistical methods - algorithms are trained to make predictions, unveiling crucial information within data mining projects. These insights drive decision-making within businesses, affecting key growth metrics.

Why Machine Learning Matters?

Like the title, we will be going through the significance or benefits of ML in business and how organizations move ahead with the implementation of ML.

The main objective of ML technology is to help companies improve their overall productivity, decision-making process and workflow. As machines start learning through algorithms, it will assist ventures in resolving data patterns that help the organization make better decisions without the need for humans.

Below are a few benefits ML technology offers:

Business Transformation

ML technology has been transforming businesses with its potential to offer valuable insights. The insurance and finance sectors use the tech to determine meaningful patterns within big data sets, prevent fraud, and provide customized plans to various customers.

When considering the healthcare industry, fitness and wearable sensors powered by ML tech allow users to take charge of their health, accordingly reducing the pressure of healthcare experts.

This technology is also leveraged in the oil and gas industry to identify new energy sources, analysis of ground minerals, system failure predictions, etc.

The technologies evolve to new heights each day, and ML has been amplifying business or organizational growth. Global companies are heading towards applying ML in their sectors to augment. This futuristic trend highlights how machine learning plays a vital role in business transformation, and excelling in the adequate skills will keep you on the path where opportunities are boundless.

Prompt Analysis and Assessment

Since businesses revolve around a surplus count of data moving in and out of an organization, employees find it tedious to deal with it daily. Thanks to the innovation of ML, the algorithms can aid the workforce in conducting prompt analysis and strategical assessments.

When an employee creates a business model by browsing through many data sources, they get to see essential variables. Similarly, ML can assist you in understanding customer feedback, interaction, and behaviour, thus resulting in seamless customer acquisition and digital marketing strategies.

Instantaneous Predictions

A feature that fascinates the ML enthusiast is the rapid processing of insightful data from myriad sources. This helps in making instantaneous predictions that can be valuable for organizations.

ML algorithms offer meaningful data on various customers' buying and spending patterns, which allows businesses to devise procedures that can reduce loss and maximize profits.

It also helps determine the backlogs of marketing campaigns and customer acquisition policies. With these data, employees can adjust their business procedures and enhance overall customer satisfaction.

An additional benefit of the ML algorithm is the churn analysis, where we can identify those customer segments that are likely to leave the business brand.

What are the Different Types of ML?

Basically, machine learning is divided into 3 areas and they are as follows:

Supervised Learning

Here, labelled data is used for training the data. The input goes through the ML algorithm and is leveraged to train the model. Once it’s done, we can feed unknown data into the trained ML model and obtain a new desired response.

Top-notch algorithms that are used for supervised learning are:

• Naive Bayes

• Polynomial Regression

• Decision Trees

• Linear Regression

• K-nearest Neighbors

Unsupervised Learning

In this type of ML, the training data is unlabelled and unknown. Without labelled data, the input can't be guided to the ML algorithm, where unsupervised learning comes into action.

This data is used in the algorithm for training the model. The trained model searches for a pattern and generates the desired outcome. In this case, it is similar to the Enigma machine trying to break code without human intervention.

Top-notch algorithms that are used for unsupervised learning are:

• Principal Component Analysis

• Fuzzy Means

• Apriori

• Partial Least Squares

• K-means Clustering

• Hierarchical Clustering

Reinforcement Learning

The ML algorithm identifies data through a trial-and-error process in reinforcement learning and then decides what action yields higher benefits. 3 significant components of this ML type are - the agent, the environment, and the actions.

The agent is the decision-maker, the environment consists of everything that the agent interacts with, and lastly, the actions are what the agent does. This type of ML occurs when the decision-maker chooses activities that increase the expected profit over a given period.

What are ML Engineers?

ML engineers are programming experts who research, develop, and create self-running software to automate the predictive models. These engineers develop AI systems that use a colossal amount of data to produce and develop algorithms capable of learning and making decisions.

To develop top-notch performing ML models, the organization requires ML engineers to assess, analyze, organize data, perform tests and optimize the learning processes.

What are the Technical Skills Required for ML Engineers?

We have learned how ML application operates, followed by numerous job opportunities in the IT field for software engineers and data scientists. To be a part of ML technology, you need specific technical and soft skills.

Firstly, we will see technical skills required for an engineer, and they are:

Neural Network Architecture

Neural networks, also called Artificial Neural Network (ANN) or Simulated Neural Network (SNN), are the predefined algorithm sets used for ML task implementation.

They provide models and play a vital role in this futuristic technology. Now, ML seekers must be skilled in neural networks because it offers an understanding of how our brain works and assist in model & simulating an artificial one. It also provides in-depth knowledge about parallel and sequential computations.

Some of the neural network areas that are essential for ML are:

• Boltzmann machine network

• Convolutional neural networks

• Deep auto-encoders

• Long short-term memory network (LSTM)

• Perceptron

Natural Language Processing (NLP)

It is a branch of linguistics, AI & computer science that, when combined with ML, Deep Learning (DL), and statistical models, enables computers to process human language in the form of spoken words and text and understand its whole meaning with writer's intent.

Several techniques and libraries of NLP technology used in ML are:

• Word2vec

• Summarization

• Genism & NLTK

• Sentiment analysis

Applied Mathematics

ML is all about creating algorithms that can learn data to predict. Hence, mathematics is significant for solving data science projects DL use cases. If you wish to be an ML engineer, you must be an expert in the following math specializations.

But why math? There are several reasons why an ML engineer needs math or should depend on it. For instance, choosing appropriate algorithms to suit the final outcomes, understanding & working with parameters, deciding validation approaches, and estimating the confidence intervals.

If you are wondering about the math proficiency level one must hold to be an ML engineer, then it depends on the level at which the engineer works. The below-shown pie chart will give you an idea of how significant various math concepts are for an ML engineer.

Data Modeling & Evaluation

An ML has to work with a colossal amount of data and use them in predictive analytics. In such a scenario, data modelling & evaluation becomes beneficial in dealing with these bulks and estimating the final model's good.

Hence, the following concepts are must learn skills for an ML engineer:

• F1 Score

• Log loss

• Mean absolute error

• Confusion matrix

• Classification accuracy

• Area under curve

• Mean squared error

Video & Audio Processing

This processing concept is different from NLP because audio & video processing can only be applied to audio signals. For this, the following ideas are essential for an ML engineer:

• TensorFlow

• Fourier Transform (FT)

• Music theory

Advanced Signal Processing Techniques

Signal processing targets analyzing, modifying, and synthesizing signals to minimize noise and extract the provided signal's best features. For this, the techniques leverage certain concepts like spectral time-frequency analysis, convex optimization theory & algorithms, and algorithms (bandlets, shearlets, curvelets, wavelets, etc.)

Reinforcement Learning

Reinforcement learning is an ML area that takes suitable action by employing several machines and software to increase rewards in a particular scenario. Though it plays a vital role in understanding and learning DL & AI; however, it is beneficial for ML beginners to have an insight into the fundamental concept of reinforcement learning.

What are the Soft Skills Required for ML Engineers?

While ML engineering is a technical job, soft skills such as problem-solving, collaboration with others, communication, time management, etc., are what lead to successful completion and delivery of the project.

Here are some of the soft critical skills an ML engineer must possess:

Team Work

ML engineers are often at the core of AI initiatives within a company, so they naturally work with software engineers, product managers, data scientists, marketers and testers. The potential to work closely with others and contribute to a supportive working environment is a skill many recruiters seek in ML engineers.

Problem-solving

The potential to solve an issue is a significant skill required for both software & ML engineers and data scientists. ML focuses on solving challenges in real-time, so the potential to think creatively and critically about the problem and develop solutions accordingly is a fundamental skill.

Open to New Learning

The fields of ML, AI, DL and data science are drastically evolving, and those who have earned a degree and working as an ML engineer find ways to learn new things through workshops, boot camps and self-study.

Whether learning the latest programming languages or mastering new tools, the most effective ML engineers are open to new learning skills and constantly refreshing their learnt toolkits.

Communication

ML engineers must possess excellent communication skills when communicating with shareholders regarding the project objectives, timeline, and expected delivery. We know that ML engineers collaborate with data scientists, marketing & product teams, research scientists, and more; hence, communication skill is crucial.

Domain Knowledge

To develop self-running software and optimize solutions leveraged by end-users and businesses, ML engineers should have an insight into the requirements of business demands and the type of issues the software is solving. Without domain knowledge, an ML engineer's recommendation may lack accuracy, their task may overlook compelling aspects, and it might be strenuous to evaluate a model.

What are the Programming Skills Needed for ML Engineers?

Machine learning is all about coding and feeding the machines to carry out the tasks. ML engineers must have hands-on experience in software programming and related subjects to provide the code.

Let's see the programming skills an ML engineer is expected to have knowledge on:

ML Algorithms & Libraries

ML engineers are expected to work with myriads algorithms, packages, and libraries as part of a daily task. ML engineers must be skilled with the following ML algorithms and libraries:

• Knowledge in packages & APIs - TensorFlow, Spark MLlib, scikit-learn, etc.

• Decide and choosing of hyperparameters that impact the learning model & the result.

• Algorithm selection provides the best performance from support vector machines, Naive Bayes Classifiers and more.

• Expert in model handling like decision trees, neural net, SVMs and deciding which is suitable.

Unix

ML engineers require most servers and clusters to operate are Linux (Unix) variants. Though they can be performed on Mac & Windows, more than half of the time, they are required to run on Unix systems only. Therefore, having good knowledge of Linux & Unix is vital to being an ML engineer.

Computer Science Fundamentals & Programming

Engineers must apply the concepts of computer science and programming accurately as per the situation. The following ideas play a significant role in ML and are a must on the skillset list:

• Algorithms: search, sort, optimize, dynamic programming

• Computer architecture: memory, bandwidth, cache, distributed processing and more.

• Data structures: queues, trees, stacks, graphs and multi-dimensional arrays

• Complexity & computability: big-O notation, P vs NP, approximate algorithm, etc.

Distributed Computing

Being an ML engineer means working with massive data sets and focusing on one isolated infrastructure, and spreading among system clusters for data sharing. In such a situation, these engineers must know the concept of distributed computing.

Software Engineering & System Design

ML engineers must have sound knowledge of the following areas of software programming & system design, as all they do is code:

• Top-notch measures to circumvent bottlenecks & develop user-friendly outcomes.

• Algorithm scaling with data size.

• Interacting with different working components and modules using library calls, REST APIs and database queries.

• Fundamental software design methodologies and coding like testing, requirement analysis and version management.

What are the Key Programs for Mastering ML Engineers?

In addition to an in-depth knowledge of programming languages such as SQL, C++, Python and Java, several ML engineers are also experts in the following tools:

• AWS ML

• IBM Watson

• TensorFlow

• R

• MATLAB

• Google Cloud ML Engine

• Weka

• Hadoop

• Apache Kafka

About Us

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world.

For more information on how iCert Global can help you to achieve your ML, AI and Deep Learning (DL) Certification goals, please visit our website.

https://www.icertglobal.com/

We provide instructor-led classroom and online training across the globe, followed by Corporate Training for enterprise workforce development.

Scrum Methodology Being the Ultimate Option for Project Management

There are numerous Agile methodologies like Lean project management, Scrum, Kanban, Six Sigma and many more to choose from. If you are new to the field of project management, it can be a lot to grasp. You may have heard about a common approach of Agile project management, 'Scrum.' But what exactly is it? What does it do? Why such a technique in business? The positive sides of Scrum and much more in detail shall be covered in this article.

What is Scrum?

Scrum is an Agile development principle leveraged in software-based development on incremental and repetitive processes. It is a flexible, swift, adaptable and efficient framework that is created to offer value to the end-users throughout the project development.
We can say the framework motivates teams to learn through experience, self-organize while working on an issue, and reflect on their failures and success for continuous enhancement.
The prime aim of the Scrum principle is to meet the demand of customers through an environment of transparency in communication, continuous progress and collective responsibility.
The project development begins from a basic idea of what's required to build, explaining a list of properties ordered by the product backlog that the product owner wants to achieve.
While Scrum is frequently used by software development teams, its principles can be applied to all sorts of teamwork, making it a reason for its popularity.

The History of Scrum Methodology

The term 'Scrum' was coined in 1986, with an article by Ikujiro Nonaka and Hirotaka Takeuchi in the Harvard Business Review (HBR) titled - The New Product Development Game. The article explains how organizations like Fuji-Xerox, Honda and Canon introduced new products globally, leveraging a scalable and team-related method for product development. This methodology highlights the significance of promoting self-organized teams.
The Harvard article led the path to develop several concepts that generated today's Scrum. The term was drawn from Rugby, which refers to how the game restarts after a foul or when the ball has left the game.
For the software development process, Jeff Sutherland and his team at Easel Corp in 1993 invented the Scrum methodology by combining the 1986 article with object-oriented development concepts, actual process control, repetitive development and incremental, software procedures, productivity enhancements, and the development of dynamic systems.

Why Scrum?

Scrum is one of the most popular-gained agile approaches evolving in all aspects. The entire project is divided into smaller chunks to deliver some features to the testing teams. With the Scrum methodology, an organization can provide small working software products after each interaction and shareholder feedback to enhance or modify the project according to their demands.
Scrum provides several benefits to its end-users, and the most significant ones are listed below:

• Update and review as per client's demands.
• Frequent collaboration among teammates leads to interpersonal relations and loyalty among them.
• Involve in the sprint review discussions with shareholders to hence the team outcomes.
• Offering swift delivery of software product in short turnaround time
• Work completion using the definition of done addresses the creation, incorporation, testing and documentation with production.
• Simple to learn, but following the process might be complex.
• Conducting routine Scrum retrospective permits the teams to enhance work efficacy with the methodology factors.

Organizational Benefits

• Early detection of fallacies leading to minimized budget and work
• Better quality results in maximized sales profit and reduces customers' negative perceptions and support expense.
• Delivers Scrum performance against deadlines, quality, and budget significant for an organization.
• Involves shareholders in sprint review meetings to minimize the unforeseen problems in the early phase of the project cycle.

Customer Benefits

• Defects recovery in the early phases to satisfy the customers.
• Quality ensures client satisfaction that generates benefits of excellent references and repeats business.
• Responsibility of the Product Owner (PO) for customer changes to adopt better business engagement.
• Short cycle delivery of product results in loyalty with shareholder satisfaction.

Product Manager Benefits

• PO's responsibility is to ensure client satisfaction by fulfilling their demands.
• Prioritizing the demands to operate on a project for successful delivery.
• Confirmation regarding the entire team member strategy before starting with the work.
• Product manager plays a crucial role of a PO in the Agile principle, maintaining their focus on product development.
• Active participation in sprint planning meetings and user stories review before landing on sprint.
• The product manager is a facilitator who clarifies each team's individual doubts.
• Involves effective communication with shareholders, overseeing resources and expenses, followed by product updates according to market value and client needs.

Financial Benefits

• Regular feedback from shareholders and clients in review meetings helps make early rectifications resulting in low cost and time.
• If the execution expense is less, teams enhance the margin with resources and minimize the investment.
• If sprint goes to failure mode, it fails easier within the iteration, thus reducing failure cost.
• The involvement of workforce efforts results in better outcomes with minor defects.

Product Owner Benefits

• Clear communication and updates on product backlog items to achieve targets.
• Assists the PO to ensure the product backlog is clear, visible, and transparent to move the work to the next phase.
• Assists in managing the product backlog progress and status.
• Minimizes the team work's development and ensures they have a basic idea on product backlog items.
• Increasing product values that result from development teamwork.

Development Team Benefits

• Removal of blockers to meet the sprint goal.
• Enhances the team working through sprint review meetings.
• Assists the team to manage their task more efficiently for boosting efficiency.
• Helps the team with the skills to develop a product increment.
• Enables the unit to operate on continuous delivery of product in incremental iterations.

What is Scrum Process?

Scrum is based on defined principles and roles involved in software development. This flexible practice rewards the 12 agile principle applications in a context agreed by product team employees.
The methodology is performed in temporary blocks that are short and periodic, known as Sprints. Sprints usually range from 2-to-4 weeks, which is for feedback and reflection. Each Sprint is a unit in itself, delivering a product increment, a variation of the final product that can be shipped when requested. 
Since transparency is a critical factor of Scrum; hence teams and shareholders review each Sprint's outcomes together. This ensures that everyone follows the same priorities and deliverable patterns. Any modifications, if necessary, can be done right away.
The Scrum process has a starting point with a list of requirements leading to a project plan. The project client prioritizes these requirements, considering value balance and the cost. Moreover, the market requires quality, swift delivery at less cost. To achieve short development cycles, an organization must be flexible and agile in product development. It is a streamlined practice to implement and is famous for its quick results.

What are Scrum Events?

The Scrum process motivates professionals to work with what they possess and continually evaluate what is working and what is not. Effective interaction is vital and is carried out through meetings known as Events.
Following are the Scrum Events:

Sprint

It is the fundamental work unit for a Scrum team. Being a crucial aspect, it marks the difference between Scrum and other methodologies for Agile development.

Sprint Planning

Its goal is to define what is going to be done and how it's done in the Sprint. This meeting is held at the starting of each Sprint and is explained how it will address the project coming from the product backlog phases and deadlines. Each Sprint is composed of different aspects.

Daily Scrum

Its goal is to evaluate the pattern and trend until the end of the Sprint, synchronizing the activities and creating plan for the next 24-hours. It is a short meeting that happens daily during the Sprint period. In these meetings, the team reviews work progress on the previous day and today, followed by what sort of help these team members need. The Scrum Master should strive to find solutions for the obstacles that arises.

Sprint Review

Its objective is to highlight what task has been accomplished with regards to the product backlog for future deliveries. The finished Sprint is reviewed, and there must be a precise and tangible product progress to present to the end-users.

Sprint Retrospective

The completed targets of the finished Sprint are then reviewed by the team, note down the bad and the good to avoid mistakes. Sprint retrospective serves to implement enhancements from the point of view of development. Its goal is to determine potential process enhancements and generate a plan to implement for the next Sprint.

What are Scrum Artifacts?

Scrum artifacts are information that stakeholders and teams leverage to detail the product being created, actions to generate it, and the actions executed during the project.
The following artifacts are defined in Scrum Process Framework:

Product Backlog

Product backlog refers to what has to be done. During this artifact, the development team collaborates with the business owner to prioritize the task that has been backlogged. The product backlog may be calibrated during the backlog refinement process.

Sprint Backlog

This task list must be accomplished before delivering selected product backlog items. These are differentiated into time-based user stories.

Product Increment

Sprint backlog refers to what has been completed during a Sprint, all the product backlog items, and developed during prior Sprints. The increment reflects on how far progress has been made.

Burn Down

The burn-down chart visual represents the amount of task that needs to be completed. This chart has X-axis that displays time and Y-axis that shows a task. It portrays a downward trend, as the number of unfinished tasks over time burns down to zero.

What is a Scrum Master?

A scrum master is a professional who leads the team using Agile project management throughout the project course. They act as a coach to facilitate effective communication and collaboration between leadership and team employees for successful results.
A good scrum master is dedicated to scrum values and foundations but remains flexible and open to opportunities for the team to enhance their process flow.

What are the Responsibilities of a Scrum Master?

In an agile world, a team would manage its own tools and practices. However, several teams making a sudden jump to agile often rely on a scrum master. For a proficient scrum master, certain duties must be followed. Some of the essential roles and responsibilities are as follows:

• Eradicating hurdles so that the team can follow Scrum practices and focus more on the task.
• Manage the process flow in coordination with the scrum team.
• Work as a servant leader and a facilitator who promotes self-organization
• Safeguard the tram from external and internal problems.
• Conduct retrospective meetings.

What is the Career Scope of Scrum Master?

According to Scrum Alliance, a certified scrum master (CSM) course will help increase the probability of the overall success of a project by deploying the Scrum methodologies most appropriately. 

By understanding the application, value and practices, one can work as a servant leader rising beyond a standard project manager and aiding your company to achieve targets. This course is beneficial for professionals that are in a position of managing massive teams across various departments.

According to a recent survey by Payscale.com, the average salary of a CSM professional is $17,755.23 (INR 1,331,456) per year.

How to be a Certified Scrum Master (CSM)?

To earn your CSM certification, you must have a thorough knowledge of the lifecycle and framework of Scrum. Then you will have to attend an in-person, 2-day training course conducted by a Certified Scrum Trainer (CST) or Endorsed Scrum Trainer (EST).
The next step after course completion is to demonstrate your progress by taking an online CSM exam conducted by Scrum Alliance. To attain a passing score, you must answer 37 out of 50 questions correctly with a 1-hour time limit.
Once you pass the exam, you will be asked to accept the License Agreement and complete your membership profile in Scrum Alliance.

About Us

Becoming certified is a procedure that requires dedication and commitment. A good certification that best displays your career potential and offers you a cutting-edge over others will certainly demand more from you. 
Keep in mind that if the certification was a piece of cake to get held off or did not have any professional challenges, then what is the exact reason limiting people from pursuing it? Choose wisely and conquer the certification course to stand out of the crowd in terms of economy and career.
iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world. 
For more information on how iCert Global can help you to achieve your Scrum Certification goals, please visit our website.
https://www.icertglobal.com/
We provide instructor-led classroom and online training across the globe, followed by Corporate Training for enterprise workforce development.
 

Why Data Science in 2022?

With the emergence of IoT, social media, smartphones and other tech advancements, data expansion in business had significant growth. The data growth has led small-scale and large-scale companies to think of how to leverage information for business benefits. Meanwhile, people started seeking different options to develop their data skills, advance their careers and gain job security.

In today's business, data science has become a central part, given the colossal volume of generated data. It is one of the most discussed topics in the IT field. Its popularity has grown recently, and organizations have started implementing data science practices to grow their ventures and enhance customer satisfaction.

Today we will see what data science is, its futuristic roles, how different is it from data analytics and more.

What is Data Science?

The term 'Data Science' was coined in 2008 by Jeff Hammerbacher and DJ Patil when working for Facebook and LinkedIn. Its primary focus is to derive insight and knowledge from any data form, whether structured or not. Data Scientists leverage their skills in a wide range of industry verticals like technology, academia and finance.

Data science being an interdisciplinary area leverages scientific methods, systems, algorithms and procedures to extract actionable insights from organized or non-organized data and apply them across various application domains. It combines different fields such as AI, statistics, scientific methods and computer algorithms to analyze big data.

The technique encompasses data preparation for analysis, including cleansing, aggregation and data manipulation for executing advanced analysis. Data scientists and analytic applications can then review the outcomes to reveal patterns and enable business luminaries to accumulate desired insights.

How Data Science Is Changing the World?

Let’s see some of the scenarios where the application of data science is changing our world.

Identification of Target Audience

The success of every business begins with customer satisfaction. It is a company's responsibility to offer their customers with things they need for ultimate venture growth. But it might be nearly impossible to achieve if we are not familiar with the pain points.

From Google Analytics to people surveys, most organizations will have at least one customer data source to be gathered. But if it isn't leveraged correctly, let's say, to determine demographics, the data isn't valid. The significance of data science is based on the potential to obtain the current information that is not necessarily advantageous on its own and integrate it with other points to provide actionable insights about target audiences.

A data scientist can assist in finding the crucial groups with accuracy through a rigorous analysis of disparate data sources. Companies can customize services and products to customer groups and help profit margin growth with this profound insight.

Receiving Package in a Day

Have you wondered how Amazon understands which product to deliver in which locations? The answer is data science.

Data science assists the brand in maintaining the products in stock so that they can be shipped quickly. Delivery agencies such as UPS leverage data science to estimate the potential barriers, traffic and weather patterns to decide the most suitable route.

Global Warming Prevention

Data plays a critical part in determining the effects of climatic change. By incorporating myriad overlapping data from the satellites, scientists can observe the globe's condition. The multiple satellite data, merged with the insight from companies analyzing deforestation and similar ones, will help them get answers about climatic change.

Define Business Objectives

Identifying business objectives is more straightforward when said than done. It is never a one-time procedure when you start an organization only to keep it aside afterwards. If you wish to constantly grow the business, you need to revise and redefine the objectives night and day.

A data scientist leverages ultra-modern business analytics to obtain insights from past business trends. The data then undergoes mining, conducts quantitative and statistical analysis, and then sorts and examines data.

Once data scientists extract insight from the data, they offer the company actionable advice for better objective definition, thus helping you enhance the overall business performance and set for better profits.

Empowering the World

The developing countries are swiftly collecting data sets based on several subjects such as weather patterns, disease epidemics and daily living conditions. To take the efforts further, tech giants such as Facebook, Amazon, Google, and Microsoft support analytics programs in fields where data can be converted into actionable insights.

These countries will be equipped with several techniques for enhancing agricultural performance, eradicating the sudden change in life, risking climatic factors, controlling epidemics and improving overall life expectancy and quality.

Booking a Ride

We use different apps such as Ola, Uber, Lyft and many more to book our ride and feel it quite impressive as the ride is just a few clicks away. But what's much more remarkable is the data science techniques doing all the work. It knows how crowded a route is, the nearest driver available, what vehicle they possess, and the weather, offering you an ETA and a ride price.

Recruiting Top Talent for the Company

A day in the recruiter's life can be hectic, with many resumes to select suitable candidates for a specific role. This tedious process has become more streamlined and seamless, thanks to data science.

With the amount of candidate information available through job hiring websites, social media, and corporate databases, data scientists can make their way through these data areas to determine the best fit individuals for the company.

By mining, the large volume of information, in-house resume and application processing, data-driven aptitude tests and games, these scientists help your hiring team make more precise selections.

Workforce Training

Data scientists not only assist managers in making well-versed decisions but train the entire workforce in learning and implying the best practices for better organizational performance. This means that your employers don’t need to be an expert in various scientific analyses; instead, you help them have better insight on business analytics to follow the data they operate with.

By making the analytics data available to the entire employee team, they can refer to critical information anytime and constantly enhance their efforts. They can also target the core competencies and contribute more to its growth.

Better Decision-making

An expert data scientist is similar to a strategic planner to a company's top-tier management by ensuring that the workforce increases their analytical potentials. The data scientist communicates and demonstrates the data value to enable enhanced decision-making procedures across the entire company by estimating, tracking, and recording performance and other workflow data.

Is Data Science Hard to Learn?

Data science is a rigorous concept, having a steep learning curve - time-consuming for cleansing data, importing massive datasets, developing databases and maintaining dashboards. According to LinkedIn.com, the commonly seen skill for a data science job is SQL, with Spark and Hadoop catching an eye. You will have to learn a programming language like Python, R or SAS, followed by brushing up on mathematics.

It is advisable to learn coding from scratch, as a minute parametric change can disrupt the outcomes, and there's a small margin for error. Other related fields where you are required to specialize the deep learning (DL), machine learning (ML) and natural language processing (NLP).

Each process of data science can be tedious and challenging. First, an organization need to obtain accurate data from a various external and internal source and ensure it is structured. Once the data is in a readable format, they have to develop complex algorithms and models to extract meaningful data and convey them to answer critical business queries and influence shareholders.

What Actually Data Scientist Do?

Most data scientists have pioneered training in computer science, statistics and mathematics. Their expertise is widespread, extending to data mining, visualization and information management. Moreover, it is common for data scientists to have previous experience in data warehousing, infrastructure designing and cloud computing.

Some of the roles and responsibilities of data scientists are:

• Alleviating fraud and risk: The scientists are trained to determine data that contain fallacies. They develop statistical, path, network and big data procedural practices for predictive fraud susceptibility prototypes and leverage those to develop alerts that ensure responses when unusual data is identified.
• Customized user experience: With skilled data scientists in your company, the marketing and sales teams gains the ability to understand their customer-base on a very granular level because of actionable insights extracted from big data.  With this insight, your company can create cutting-edge customer experiences.
• Significant product delivery: One of the merits with having an experienced data scientist in an organization is: when and where their products sell best. This helps in offering the suitable products at the suitable time and help your firms in developing new-flanged products to meet customer demands.

Is Data Scientist the Highest Paying Job?

Data Scientists are highly paid employees of most companies, and it is not a secret that these professionals can bring an immense amount of value. The salary of a data scientist depends on various factors such as experience, industry, job designation, company size, location and qualification.

According to glassdoor.com, the salary of data scientists is:

• India - INR 11 Lakh
• US - $110K
• UK - £46,953
• Canada - CAD 87,248

Top-notch companies that hire data scientists are Amazon, Walmart, IBM, Accenture, Deloitte, TCS, Mu Sigma and more.

Are Data Science and Business Analytics the Same?

Data Science and Business Analytics, though both seem like a similar job role at first, there are several differences.

Data Science and Business Analytics involve knowledge & information gathering and modelling. However, the difference is that Analytics is specific to business-oriented concepts such as profit, cost and so on; on the other hand, Science answers questions such as geographic influence, customer business demands and seasonal factors.

Let’s see some of the basic difference between both the concepts.

i. Coining of Term

The term 'Data Science' was introduced in 2008 by Jeff Hammerbacher and DJ Patil when working for Facebook and LinkedIn, respectively.

Business Analytics as a concept has been leveraged since the 19th century when it was introduced by Fredrick Winslow Taylor.

ii. Concept

Data Science leverages the interdisciplinary field of algorithm building, data inference and systems to obtain data insights.

Business Analytics uses statistical concepts for extracting business data insights.

iii. Industrial Application

The top 5 industries where Data Science is leveraged are:

• Academia
• Financial
• Technology
• Internet-based
• Hybrid fields

The top 5 industries where Business Analytics is leveraged are:

• Retail
• CRM
• Technology
• Hybrid fields
• Financial

iv. Coding

Coding is widely used in Data Science. The field mixes traditional analytics principles with in-depth computer science knowledge.

Business Analytics does not involve much coding as it is more statistics oriented.

v. Language Tools

The language tools used in Data Science are:

• C/C++/C#
• Stata
• MATLAB
• Scala
• Haskell
• SAS
• R
• SQL
• Java
• Python
• Julia

The language tools used in Business Analytics are:

• SQL
• C/C++/C#
• Scala
• Java
• R SAS
• MATLAB
• Python

vi. Statistics

In Data Science, statistics is leveraged at the end of analysis following coding and algorithm building.

In Business Analytics, the fundamental analysis is statistical oriented.

vii. Work Challenges

In Data Science, the business decision-makers do not leverage the outcomes. It cannot apply findings into the decision-making process of a company. There is no accuracy on the questions that need answers with the provided data set. The top challenge among Data Science is its difficulty in data accessing and the prerequisite of IT coordination.

Similar to Data Science, Business Analytics cannot apply findings into a company's decision-making process, no accuracy on the questions that need answers with the provided data set, difficulty in data accessing, and the prerequisite of IT coordination. Other work challenges seen here are the lack of significant domain expert input, data inaccuracy, privacy concerns, fund shortage to buy relevant data sets from external sources, and tool limitations.

viii. Data Types

Data Science uses 2 types of data: big data and traditional data. Traditional Data means structured data stored in a database. In contrast, big data include a wide variety of Data - text, images, mobile data, numbers and audio, Velocity - retrieved and computed, and Volume - measured in Tera, Peta and Exabytes.

Business Analytics predominantly uses structured data. This historical Data helps understand the factors that may impact your company.

ix. Future Trends

The future application of Data Science is Artificial Intelligence (AI) and Machine Learning (ML).

The future trend of Business Analytics would be in Tax Analytics and Cognitive Analytics.

x. Disciplines

Data Science provides data insights that assist companies in increasing their operational efficacy, determining new market choices, enhancing sales and marketing efforts, and many more - giving a competitive edge in the market. Some of the disciplines involved in this field are:

• Predictive analytics
• ML and Deep Learning (DL)
• Business Intelligence (BI)
• Data and Warehouse engineering
• Statistical analysis
• Data visualization & mining

Business Analytics includes determining business requirements, leveraging previous data, finding solutions - new system development, strategic planning, and process optimization. Some of the disciplines involved in this field are:

• Data analysis
• Solution assessment
• Elicitation and Analysis prerequisites
• Workflow modelling
• Business modelling

xi. Job Opportunities

Data Science skillsets are required in most job sectors and are not restricted to tech-related industries. However, you get an opportunity in these high-paying, in-demand professions at tech giants an advanced degree is a prerequisite.

The in-demand profession includes:

• Data Engineer
• BI developer
• Data scientist
• Applications architect
• Data analyst
• ML engineer

Recruiters in Business analytics generally look for hiring the following professionals:

• IT business analyst
• Business analyst manager
• Data business analyst
• Computer Science data analyst
• Data analysis scientist
• Quantitative analyst
• System analyst

xii. Salary

Data scientists enjoy high-pay salaries and job expansion. According to 2020 BLS data, the average wage earned by Data Scientists was $126,830 per year, with the highest 10% making in 2020. According to LinkedIn, the average salary of Data Scientists in India is INR 850K, and in the US, it is $125,044. Based on experience, first-level Data Scientist earns around INR 611K and $98,122 per year, while most experienced workers make up to INR 20L and $168,372 per year.

The average salary of a Business analyst in India is approx. INR 612,656 per year and in the US is approx. $70,489 per year. Based on experience, first-level Business Analyst earns around INR 363,813 and $ 60,055 per year, while proficient workers make up to INR 1,284,643 and $90,431 per year.

Is Data Science the Right Career for You?

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world. Visit our website to find out the different technology courses.

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You may not have heard about the triple constraint theory, but you might be surprised if you said you are operating under it anytime you run a project, and it is something that works without you knowing.

The concept is known in several names, such as Iron Triangle, Project Management Triangle, and Project Triangle, which gives us an insight into how significant the Triple Constraint is when managing a project.

Though the concept seems to be confusing and somewhat complex, however, in reality, it's a simple procedure. When Triple Constraint is leveraged with efficient project management software, it offers you the potential to drive the project to success.

What is Triple Constraint Theory?

Triple Constraint theory is defined as the project's success that is impacted by its cost, scope and time. Any modification in any one factor will directly affect the rest. Let us understand it better with an example, shall we? If there are unexpected modifications in the project and your client wants the deliverable at a swift turnaround time, it will directly affect the processes. You must ramp up, maybe charge more and meet the altered deadlines.

As a project manager, you can maintain the concept by balancing these constraints via trade-offs and ensuring everyone's demands are met to create a successful project. Though the idea is a significant part of the project, it doesn't determine success, as projects are made from several functions. That is why few management professionals have added extra 3 constraints to the existing model to better reflect critical project areas.

• Quality - Whether a project is a final deliverable or a tangible product, each project has quality norms. Project managers need a specific quality management practice to control project quality.
• Risk - This is an inherent aspect of any project, and this is one of the reasons why project experts need to develop a risk management plan to define how the risks can be tackled.
• Benefit - Different gifts are obtained from a project; hence managers must ensure that project shareholders get the best financial help.

Significance of Triple Constraint Theory

Triple Constraint theory is considered during project performance because the team will be aware of boundaries within which they are needed to operate. Even when these conditions change, the team will adapt to the changes and eliminate the most optimal possible outcomes within the provided circumstances.

Let’s see in detail the concept of Triple Constraints theory.

Scope

The scope of a project is defined as the needs starting from the very beginning to its final stage. It is basically what you agree with the end-user before initiating project work. If there are modifications to the scope, it will consistently impact the expense and schedule of the project. 

To maintain top quality with the fast turnaround time, it is suggested that the project scope remains the same throughout.

Generally, the scope is selected during a project's planning phase, which will allow the managers to include all the goals and prerequisites of the project. It also ensures that individuals involved in the project are aware of their expectations and know how to implement them.

This will aid in avoiding the scope creep that takes place when prerequisites change over time. Scope creep is pernicious to all the project aspects, and if you allow it, the creep can sneak up on you, bringing an entirely different modification to the project requirement.

Therefore, it is significant that managers must take a step and define the aspects of projects, stopping them in a document, before quitting the work.

Time

Another factor that contributes to successful project completion is time. Depending on the demands, the managers need to ponder the project duration required to complete as per the client's expectations. When considering the schedule, you must consider all the end-users needs, brainstorm with the POCs involved, and arrive at a conclusion as to how long the project might take to finish off work.

When calculating time, one thing to keep in mind is that project might get expensive when we try to rush it for a quick delivery than having a more extended project with a longer schedule. When trying to cut down the time, quality is the one that suffers, which may lead to client dissatisfaction and later result in rework.

There are other factors to be considered when taking a project, such as research and meetings with shareholders & clients, which are time-consuming programs. Hence considering time beyond task hour is essential, and the more accurate your time estimation is, the better are these timelines determine the project course.

The following are some of the methods you could follow for effective project development:

• Create project overview
• Noting of different methods or activities involved
• Defining activities along with the priorities
• Estimate the time needed for each method
• Create a detailed schedule of the project and delivery time
• Control and update the schedule 
• Tracking of updates and project progress

Cost

The estimated deadline and resources will be a fundamental factor in calculating the project expense. The expense is primarily a budget you have to offer the client, stating the charges for completing the project. The budgeting must be accurately done, highlighting the process and employees involved to make the project a success.

It should also include the number of hours the project will take for completion, along with extra hours needed for researching and meetings. Other than that, the budget must have the time required for team members to complete their work at the expense of the materials and equipment required.

Two things you must not sacrifice when budget plannings are honesty and openness towards your client. You must ensure that the client is aware of what they are being charged for. It is also necessary to be transparent regarding the works and procedures that might not have been included in the expense.

Following are a few methods for you to efficiently calculate the project budget:

• To estimate the expenses, leverage data from previously done similar projects
• Find out the expense of your materials and resources
• Use different parameters and analyze by measuring previous and new data available
• Work in a reverse mode and estimate the expense by tracking budget spent on previous projects (lowest to highest)
• Communicate with your suppliers and calculate their expenses
• Include quality analysis cost

Performance of Triple Constraint

Project managers can either maximize or minimize the cost, scope and timeline of a project with trade-offs to keep it on schedule and under budget. Let us see how these works:

• Time and scope - If you are running behind the schedule, you can reduce the project scope and time. In reverse case, you can maximize the project duration if shareholders come up with additional project schemes.
• Cost and scope - By minimizing the scope of a project, you can perform fewer practices, which leads to lower expenses. In other case, a massive scope means higher expense.
• Cost and time - In some projects, cost and time are directly dependent such as the cost of renting equipment or labor.

In all these cases, we can see the application of the Triple Constraint for project management; however, several other trade-offs can happen during a project, such as benefit, quality and risk.

With the help of a project management dashboard, a manager can keep a close watch on the progressing project. Metrics like scope, schedule and expenses are easy to keep sight of. A project manager can determine problems and adjust the Triple Constraint to prevent them from developing more significant issues.

The project manager features a real-time dashboard that indicates all crucial data of a project impacting the Triple Constraint.

How You Can Manage Better with Triple Constraint

If you are more open to the Triple Constraint idea and start paying a little close attention, you can change the way your members react to the concept and to the rest of the project on the verge of completion. When you have a better insight into what to expect, it's a lot easier to complete the tasks.

Understanding the Scope

It's easy to get caught in scope creep; hence, communicating with the team about the project requirements is always better. If you don't go in detail and your team is not familiar with the different project aspects due to lack of communication, the team might likely go beyond what's expected of them as they believe something isn't a project part.

Set Suitable Deadlines

Whether the project is short-run or long-run, setting small targets and deadlines will assist your team to function seamlessly and give them satisfaction as they go past each objective. Observe these deadlines and targets to maintain a progress track and calculate the planned timeline vs the actual one for target completion. This will help in effective accountability and offers progress clarity.

Know the Budget

When you provide the budget to your client, you will be highlighting every detail of expenses, which will help you keep track of the budget. You have to ensure that the team is aware of the budget and how you are doing about it, so you can find where to adjust the cost to make sure you are sticking near to the original expense.

Conclusion

The Triple Constraint is a simple procedure that will help project managers avoid fallacies, poor decisions, and risks and help the team achieve their goals efficiently.

It is significant to keep track of time, expense, and scope when managing a project. Even a minor change in any of these areas can cause a change in others. By keeping track of all and operating hard to maintain the practices that you set at the initial phase of the project and developing the process with the client, you will manage the Triple Constraint. Therefore, work with your team to ensure that every project aspect works for you.

About Us

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world.

Visit our website to find out more about the course.

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Our company conducts both Instructor-led Live Online Training sessions and Instructor-led Classroom training workshops for learners across the globe.

We also provide Corporate Training for enterprise workforce development

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Quality defects can have significant costs associated with them: money, resources, time, and reputation. The programs leveraged by organizations to eradicate quality flaws can be big-budget and time-consuming.

During these times, organizations have a hard time maintaining their quality to stay competitive in their field; that’s when quality management tools come into action. These tools are productive approaches to streamlining business practices to offer better quality and swifter outcomes. 

More than 91 percent of companies saw an increase in their operating income after successfully executing a quality management tool. Quality handling is one of the fastest ways to increase production returns.

The Zero Defects approach in quality management works to minimize the flaws to imaginary and, as a result, create better value.
 

Introduction to the Concept of Zero Defects

The Zero Defects concept was first coined by Mr. Philip Crosby in "Absolutes of Quality Management" and is widely adopted by Six Sigma methodologies as one of its theories. However, the concept also had a fair share of criticism, with some debating about the non-existence of Zero Defects.

Others have struggled hard to prove that Zero Defects in quality management don't mean perfect; instead, a method for eradicating waste and minimization flaws.
 

What is Zero Defects?

Quality management talks about practices that ensure that the outputs produced are suitable. But to attain zero defects in the deliverable production is technically nearly impossible as complex projects, irrespective of the size, will have some flaws.

According to Six Sigma, Zero Defects are defined as 3.4 defects per million opportunities (DPMO), permitting a 1.5 sigma process transition. To enhance the quality of manufacturing or developing procedures, the Zero Defects concepts can be considered a perfection quest. 

Though true perfection might not be obtainable, the pursuit will push quality and enhancements to the point that is acceptable even under rigorous metrics.
 

Application of Zero Defects

Automotive sectors use Zero Defects to handle and enhance quality problems. It targets reducing the number of flaws in manufactured services and products without the need for specific rules to follow.

This makes the organizations customize how they want the management tool to work for themselves, which is why Zero Defects can be so effective.

It is leveraged in Six Sigma methodologies to minimize the variation and standard deviation, which will indirectly reduce the flaws and bring them near zero.
 

Principles of Zero Defects

The principles of Zero Defects, according to Philip Crosby, are:

• Quality is the accordance to requirements

Each product has a requirement, and it defines what end-users expect to see. A particular service or product is said to attain the utmost quality when it meets customer prerequisites. This should not be confused with the highest product standards. For example, it will be unrealistic if we say the old mobile phones lack quality compared to the latest smartphone. They both must meet different quality standards to pass the quality test irrespective of their model, size or other internal/external features.

Suppose we visualize the above scenario with the Zero Defects case. In that case, the primary mobile phone version is a quality product if it meets the fundamental customer requirements such as making phone calls and sending and receiving messages. The product is said to accord to the quality and has near to Zero Defects.

• Defect prevention

Here, the top priority is the quality management of the product or service; hence, flaw prevention is made a part of the company procedure. It should be in practice rather than in quality scrutiny and rectification. This is why because Zero Defects management is always less tiring and strenuous. It is more accurate and budget-oriented, as it targets preventing fallacies than identifying and rectifying them later.

• Quality benchmark means zero defects

The following principle of Zero Defects depends on the nature of demands. A demand highlights what the customer or the product itself is needed. Every service and product unit that does not meet the request will not satisfy the prerequisites and is nowhere near the word excellence. But, in some scenarios, the teams that do not meet the conditions could still satisfy the customer demands, so these requirements must be reviewed and modified accordingly to reflect reality.

• Quality measurements in terms of money

Mr. Crosby expects that every flaw indicates a personal expense, including inspection time, rework procedures, and overall budget or revenue. Being extra to the list includes the cost of discarded materials, customer dissatisfactions and labour. When the defects are identified accurately, and appropriate measures are taken, the impact of these costs can be made precise.

This, in turn, offers a clear-cut justification on expense leading to a step forward for quality enhancement. Companies must find a method to estimate a Zero Defects management procedure that aids in maintaining management commitment, employee encouragement or rewarding, and ensuring organizational targets are made measurable. Keeping these in mind, the practices can be made solid, thus helping in making genuine decisions on relative returns.
 

Theory & Execution of Zero Defects

Zero Defects methodology ensures that there is no waste accumulation in a project. The procedure eradicates unproductive and is of no value to a project, thereby creating process enhancements and lower costs.  

The motto of the Zero Defects theory is 'Doing it right the first time, that eventually helps eliminate expensive and time-consuming resolutions later in the project management practice.

Generally, the methodology is based on 4 elements for its execution in an actual project are they are:

• Quality is a state of accordance with requirements. Hence Zero Defects in a project mean the satisfaction of prerequisites at that period.
• Right the first time means that quality must be merged into the project process at the initial phase than keeping it aside and solving it later.
• Quality measure in terms of money means that individuals in charge of the project must consider waste, production, and revenue in terms of economic impact.
• Performance must be considered by the accepted procedures, as near to perfection as possible.
   

How to Achieve Zero Defects?

There are no magic procedures in achieving Zero Defects; however, there are some techniques to use if you are ready to introduce the concept in an organization, and they are:

• Manage process transitions efficiently.
• Acknowledgment on customer expectations about product or service quality
• Prioritizing continuous enhancement and efficacy
• Introduce and learn Japanese system of Poka-Yoke (prevention of inadvertent flaws).
• Monitor your progress.
• Estimate your quality efforts.
• Develop quality into performance expectations.
   

Advantage & Disadvantage of Zero Defects

One of the top advantages of attaining a zero-defect phase is minimizing cost and waste when manufacturing products according to customer demands. Zero Defects mean higher user satisfaction and enhanced customer loyalty that consistently results in better profits and sales.

The concept target might lead to a situation where a team is attempting a perfect practice that cannot realistically be obtained. The resources and time allocated on reaching Zero Defects may negatively affect the project performance and strain workforce satisfaction and confidence.

Moreover, there can also be negative suggestions when you ponder the entire supply chain. Other industrialists might have a different viewpoint on the Zero Defects concept.

Ultimately, the pursuit for Zero Defects is a prime target in itself, and many organizations find that merits exceed the demerits. By attempting for rigorous but accepted defects practices, they can develop reasonable procedures and build an ecosystem of continuous service enhancement.
 

Conclusion

Implementing the Zero Defects concept in an organization allows them to understand and streamline their production workflow to very minor detail. The minimal expense and maximum revenue on implementations make it suitable for any company seeking to launch a new business practice without any training and recalibration investment.

It is essential for company employees and other individuals to get trained and certified in popular quality management courses to properly acknowledge the Zero Defects approach and how it adds value to your company.
 

About Us

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world.

Visit our website to find out more about the course.

https://www.icertglobal.com/ 

Our company conducts both Instructor-led Live Online Training sessions and Instructor-led Classroom training workshops for learners across the globe.

We also provide Corporate Training for enterprise workforce development

Data Science & BI courses

• Data Science with R Programming Certification
• Apache Kafka Certification
• Power BI Certification

Quality Management Training

•  Lean Six Sigma Yellow Belt (LSSYB) Certification Training Courses
•  Lean Six Sigma Green Belt (LSSGB) Certification Training Courses
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In recent years, there has been a massive advancement in both data and technology, opening new doors to accommodate the growing demands of the industry vertical. System administration is one of the areas that might affect business performance. The job performance relates to system performance either it can improve or play havoc with the version.

Like system admins, Hadoop admins are a trending job in the big data domain. As the volume of data generated worldwide keeps on increasing, open-source processing systems such as Hadoop are gaining immense popularity in the industry.

With the rising adoption of Hadoop across various industry verticals due to its potential to scale and process the colossal amount of data, organizations require Hadoop admins to take care of the Hadoop clusters.
 

Who is Hadoop Admin?

A Hadoop admin is a core part of the Hadoop implementation procedure, where they are responsible for maintaining the Hadoop clusters running seamlessly in production. They are in charge of clusters and other resources in the Hadoop ecosystem.

The job of Hadoop admins is not visible to other clients or IT groups. They are responsible for developing and formulating the architecture, development, and engineering of big data. These admins must ensure that there are no flaws in the cluster installation. They must alleviate issues and improve the overall cluster performance.
 

Why Hadoop Admin?

Hadoop has become a top-notch priority in the IT sectors worldwide. Probably, the execution has a production of vast clusters with a more significant number of nodes, and they need an admin to manage and monitor their performance.

Their routine programs involve the tracking of entire Hadoop jobs which is scheduled. Clusters are activated towards failures offered and given test performance, and the admin must keep track of the cluster workflows.

This procedure makes the business sector obtain accurate data regarding the nodes with the help of the interface.
 

Roles and Responsibilities of Hadoop Admins

Administrating Hadoop clusters introduces numerous challenges to the Hadoop admins with running data tests via several machines. Hadoop deployment often fails as the admins attempt to replicate the procedures tested on one or two different devices across complex clusters.

Let us see the roles and responsibilities of Hadoop admins in an organization. 

DBA responsibilities include:

• Managing and optimizing disk space for data handling
• Backup and recovery procedure of database
• Performance observation and fine-tuning on the data pattern changes
• Software installation and configuration
• Data modeling, design and execution of data based on recognized practices
• Checking the connectivity and security measurements of data
• Automating manual tasks for swift performance
• Installing patches and upgrading software

The task of Hadoop admin covers batch works as part of data warehousing - involving the development, testing, and monitoring, which are:

• Loading of colossal amount of data in a timely manner
• Performing primary key execution
• Ensuring referential integrity
• Accomplishments of data restatements

Now, let us see the routine work done by a Hadoop administrator in an organization.

The key activities include:

• Configuring NameNode to ensure high availability
• Analysis of storage data volume and assigning the space in HDFS
• Required software and hardware deployment in the Hadoop ecosystem, and the expansion of existing ones
• Implementation in a Hadoop cluster and its maintenance
• Deployment and management of Hadoop infrastructure on a current basis
• Installing of Hadoop in Linux
• Monitoring the Hadoop cluster to check whether it is up-to-date and constantly running
• Management of resources in a cluster ecosystem - new node development and eradication of non-functioning ones

Other activities of the admins include:

• Checking the connectivity and security of cluster
• Operating as a central person for Vendor escalation
• Capacity planning
• HDFS file system management and monitoring
• Coordinating with application teams, installing the OS and Hadoop-related updates
• Troubleshooting
• User creation in Linux and its components in the ecosystem, and also setting up Kerberos principles
• Effective communication with organizational-level teams such as application, BI, database, infrastructure and network teams
• Managing and reviewing log files
• Administrating HDFS and offering significant supports
   

Essential Skills to be a Hadoop Admin

• The potential to install and execute the Hadoop cluster, add and eradicate nodes, monitor workflows and all the critical parts of the cluster, configuration of name-node, recovery of backups and many more.
• In-depth knowledge of Unix based file infrastructure
• The expertise of general operations, including troubleshooting and sound understanding of network and system.
• Networking proficiency
• Experience with open-source configuration deployment and management tools such as Chef, Puppet, etc.
• Strong fundamental knowledge of the operating system – Linux
• Understanding Core Java is a plus point for efficient job performance
   

Hadoop Admin Career Path

Today, Hadoop has become the talk of the town, with global companies readily adopting Hadoop and its related big data solutions, irrespective of their humungous size.

Due to a significant increase in big data and data analytics, the demand for big data skillsets is growing. Several job profiles come within the Hadoop admin career path, some of which are:

• Data analytics administrator
• IT Hadoop administrator
• Hadoop system admin
• Web engineer
• Cluster admin
• Hadoop architect
• Data engineer
• Data science tools & application engineer
• Data management analyst
• IT storage admin
• Tech support admin and many more.

These careers and roles can differ depending on the business size and job role. Moreover, the salary of a Hadoop admin makes a considerable difference in their presence in the company. Many experienced admins receive the best pay scale; hence, the Hadoop admin gets handsome money.
 

Potential Problems with Hadoop Admin Job

Few potential issues associated with Hadoop admin task on a company’s routine operation include:

• Hardware: Since Hadoop tackles a vast among of data, however, over the period, storage infrastructure fails to perform as expected. Hence a close watching of HDFS prevents data loss.
• Human error: While handling complex systems like Hadoop, man-made fallacies are common. A minor flaw can create a huge problem, making a day in Hadoop admin life tiresome. Hence, establishing preventive measures is an add-on work.
• Resource Exhaustion: It is a crucial factor used to estimate the task failure, identify users and correct the procedures. Repetitive failure of tasks is a drain on the capacity.
• Configuration issue: If you dealing with Hadoop, then configuration issues sum up to 80 percent. Hence, the performance may have a lot to pay because of configuration flaws.
   

Future Scope of Hadoop Admin

Being a Hadoop admin isn't rocket science, and neither is a cakewalk. Individuals who have a fundamental understanding of statistics, computation and programming languages are good to go. Taking a comprehensive data course is beneficial as it offers you complete knowledge and is not just limited to Hadoop.

Apart from aspirants, IT professionals such as software architects, IT managers, Java developers, DBAs and many more who are interested in Hadoop admin can take up the big data courses, as these courses provide an ocean of job opportunities and are one of the most searched terms on leading job websites.
 

Final Call

Hadoop administration is a rewarding career, opening to plenty of job opportunities in today's big data market such as Yahoo, Facebook, Quantcast, netseer, etc.

The core objective of Hadoop admins is to understand the concept of big data and Hadoop distributions. Several other factors must be considered when these admins are involved in business performance. Though they aren't limited, their works are not visible to other IT sectors. Moreover, it helps to continue the safety of Hadoop clusters.

If you are looking for up-skilling with the Hadoop administration, this is the right choice. Don't let this moment go in vain.
 

About Us

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world.

Visit our website to find out more about the course.

https://www.icertglobal.com/ 

Our company conducts both Instructor-led Live Online Training sessions and Instructor-led Classroom training workshops for learners across the globe.

We also provide Corporate Training for enterprise workforce development

Data Science & BI courses

• Data Science with R Programming Certification
• Apache Kafka Certification
• Power BI Certification

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•  Lean Six Sigma Yellow Belt (LSSYB) Certification Training Courses
•  Lean Six Sigma Green Belt (LSSGB) Certification Training Courses
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We live in a value-driven society, where we wake up each day to gather resources such as data, time and money. Regardless of what industry you work for, you are required to accumulate a colossal amount of data related to workforce performance, competitors, job applicants, customers, daily workflows and services.

Modern businesses are indeed flooded with data, but the question is: how well do you understand all of them? Do you have any system like business analytics to follow and gain actionable insights from it?

Without skilled professionals such as data scientists, processing and analyzing data primarily in a large-scale organization can become a tiring task. 

In today's article, we will see what data science is? The roles and responsibilities of data scientists and how data science drives modern businesses.
 

What is Data Science?

It is an interdisciplinary area that leverages scientific methods, systems, algorithms and procedures to extract actionable insights from organized or non-organized data and apply them across various application domains. It combines different fields such as AI, statistics, scientific methods and computer algorithms to analyze big data.

Data science encompasses data preparation for analysis, including cleansing, aggregation and data manipulation for executing advanced analysis. Data scientists and analytic applications can then review the outcomes to reveal patterns and enable business luminaries to accumulate desired insights.
 

The Roles & Responsibilities of Data Scientists

Most data scientists have pioneered training in computer science, statistics and mathematics. Their expertise is widespread, extending to data mining, visualization and information management. Moreover, it is common for data scientists to have previous experience in data warehousing, infrastructure designing and cloud computing.

Some of the roles and responsibilities of data scientists are:

• Alleviating fraud and risk: The scientists are trained to determine data that contain fallacies. They develop statistical, path, network and big data procedural practices for predictive fraud susceptibility prototypes and leverage those to develop alerts that ensure responses when unusual data is identified.
• Customized user experience: With skilled data scientists in your company, the marketing and sales teams gains the ability to understand their customer-base on a very granular level because of actionable insights extracted from big data.  With this insight, your company can create cutting-edge customer experiences.
• Significant product delivery: One of the merits with having an experienced data scientist in an organization is: when and where their products sell best. This helps in offering the suitable products at the suitable time and help your firms in developing new-flanged products to meet customer demands.
   

Ways of Driving Business in Today’s World

Knowledge is power in the business field, and data is the fuel that creates this power. Being able to control the data power through data science is highly valuable. More and more businesses are leveraging this robust data to make evidence-based decisions, understand their customers, and promote workforce training.
 

Investing in data science technology will start driving your modern business in the following ways:

Recruiting Top Talent for the Company

A day in the recruiter's life can be hectic, with many resumes to select suitable candidates for a specific role. This tedious process has become more streamlined and seamless, thanks to data science.

With the amount of candidate information available through job hiring websites, social media, and corporate databases, data scientists can make their way through these data areas to determine the best fit individuals for the company.

By mining, the large volume of information, in-house resume and application processing, data-driven aptitude tests and games, these scientists help your hiring team make more precise selections.
 

Better Decision-making

An expert data scientist is similar to a strategic planner to a company's top-tier management by ensuring that the workforce increases their analytical potentials. The data scientist communicates and demonstrates the data value to enable enhanced decision-making procedures across the entire company by estimating, tracking, and recording performance and other workflow data.
 

Finding Opportunities

When communicating with the company's analytics team, data scientists catechized the existing procedures and assumptions for developing additional analytical algorithms and practices. Their work needs them to constantly enhance the value extracted from the company's data.
 

Identification of Target Audience

The success of every business begins with customer satisfaction. It is a company's responsibility to offer their customers with things they need for ultimate venture growth. But it might be nearly impossible to achieve if we are not familiar with the pain points.

From Google Analytics to people surveys, most organizations will have at least one customer data source to be gathered. But if it isn't leveraged correctly, let's say, to determine demographics, the data isn't valid. The significance of data science is based on the potential to obtain the current information that is not necessarily advantageous on its own and integrate it with other points to provide actionable insights about target audiences.

A data scientist can assist in finding the crucial groups with accuracy through a rigorous analysis of disparate data sources. Companies can customize services and products to customer groups and help profit margin growth with this profound insight.
 

Define Business Objectives

Identifying business objectives is more straightforward when said than done. It is never a one-time procedure when you start an organization only to keep it aside afterwards. If you wish to constantly grow the business, you need to revise and redefine the objectives night and day.

A data scientist leverages ultra-modern business analytics to obtain insights from past business trends. The data then undergoes mining, conducts quantitative and statistical analysis, and then sorts and examines data.

Once data scientists extract insight from the data, they offer the company actionable advice for better objective definition, thus helping you enhance the overall business performance and set for better profits.
 

Workforce Training

Data scientists not only assist managers in making well-versed decisions but train the entire workforce in learning and implying the best practices for better organizational performance. This means that your employers don’t need to be an expert in various scientific analyses; instead, you help them have better insight on business analytics to follow the data they operate with.

By making the analytics data available to the entire employee team, they can refer to critical information anytime and constantly enhance their efforts. They can also target the core competencies and contribute more to its growth.
 

Enhancing Products and Services

Data analysis services can help you understand product and service positions on the market, which aids you in staying put. You can view your offering position and learn how and when people leverage them and the time and location they sell most.  

You can also determine the areas for enhancement, especially by getting insights into your competition. You can constantly enhance the offerings by viewing past data, comparisons, and other analyses.

We live in a world where data is the greatest asset in every company; hence, data scientists must bring value to our business.
 

Conclusion

The business value of data science depends on the prerequisites of each company. From insights and statistics across working procedures and new candidate recruitments, to assisting senior employer make well-versed decisions, data science allows your company to grow in smart and strategic ways.

Taking the time to leverage data science technology and identify the reason behind your performance is a tool that every venture must find valuable.
 

About Us

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world.

Visit our website to find out more about the course.

https://www.icertglobal.com/ 

Our company conducts both Instructor-led Live Online Training sessions and Instructor-led Classroom training workshops for learners across the globe.

We also provide Corporate Training for enterprise workforce development

Data Science & BI courses

• Data Science with R Programming Certification
• Apache Kafka Certification
• Power BI Certification

Quality Management Training

•  Lean Six Sigma Yellow Belt (LSSYB) Certification Training Courses
•  Lean Six Sigma Green Belt (LSSGB) Certification Training Courses
•  Lean Six Sigma Black Belt (LSSBB) Certification Training Courses

 DevOps Training

• DevOps Certification Training Courses

Business Analysis Training by iCert Global:

• ECBA (Entry Certificate in Business Analysis) Certification Training Courses
• CCBA (Certificate of Capability in Business Analysis) Certification Training Courses
• CBAP (Certified Business Analysis Professional) Certification Training Courses

Over the recent years, the demand for the project management profession has been drastically increasing, thus making the CAPM Certification provided by PMI more significant. Being an entry-level project management certification, passing the exam on the first attempt is not a piece of cake.

People who want to advance their career with CAPM intend to pass the exam on the first try, but it looks like 2 out of 5 participants fail the first try. Though challenging, it is not impossible to clear in the first attempt if you have solid CAPM exam preparation training and a good study guide.

Today, we will explore 10 tips that you need to know to succeed in the exam. So, let's begin.
 

What is CAPM Certification?

The Certified Associate in Project Management (CAPM) is an accreditation offered by the Project Management Institute (PMI) for individuals entering the project management profession or those seeking to obtain the fundamentals of project management knowledge.

CAPM is desirable for those who consider it a preliminary to obtain the appropriate skills and proficiency, which will assist them in working well in a project environment and acknowledge challenges in a more organized manner.  
 

10 CAPM Exam Cracking Tips

Preparing well for the certification exam requires substantial effort, time, and money. When you start preparing for any high-level exams, a significant investment of money, effort and time is needed. The same goes for the CAPM certification exam. To avoid the pain and struggle of repeating the exam, it is always advisable to pass on the first attempt, but many wonder how?
 

Here are the following 10 needed tips to help you to pass the CAPM certification exam with flying colours:

#1: Understanding Exam Content & Structure

The initial step of preparing yourself for the exam is to understand the exam content, structure and various PMBoK chapter weightage. Project Management areas are all merged, and the examination objective is to offer opportunities in all the regions. The participants require a master plan for preparation and allot learning time to each area.

Since the 6th Edition of the PMBoK Guide is approx. 700 pages, it's quite a lot for beginners to grasp or start with, resulting in losing interest in learning. However, if you follow a methodical approach followed by a training course program, you will acquire a sound knowledge overview.

Most importantly, there is no prerequisite of memorizing each input and output of the processes; instead, understand its function. Once it's done, it will not be difficult to reach the correct answer through logic and elimination methods.
 

#2: Make a Plan

Once done with an essential preparation and understanding of the exam structure & content, the next major step is making a plan. In this way, you are learning how to imply project management for passing the CAPM exam on the first try.

Depending on your work routines and time available for studying, you can assign the time to prepare for the exam every week. At the initial stage, you might not be able to estimate how much time you require to cover the portion; hence attempt to read the sample book, check your subject comfort and then calculate the time needed.

Before starting CAPM exam training, you can test your existing knowledge by appearing for a complete exam simulation test. With the achieved score, you will know which area in the CAPM requires more focus.

Later, at the end of the training, take a full sample test and compare the result. You are sure to find an improved score, highlighting the effectiveness of the training and your dedication.
 

#3: Exam Scheduling

Without a proper reference plan, scheduling and booking examination slots can be a risky business. It is essential to self-assess where you stand and what has to be done to improve it before deciding the exam date, especially when you want to pass on the first attempt.

It is worthy of spending some quality time understanding the CAPM exam, your knowledge level and estimating available time for preparation before scheduling the exam.

This initial scheming will drastically increase your chance of getting through the exam on the very first try.
 

#4: Purpose of Pre-test Questions

The CAPM exam contains 150 questions that have to be completed within 180 minutes (3 hours). Out of these questions, 15 questions are pre-tests that will be randomly spread in the test and aren't scored. The thing is that there will be confusion regarding these types of questions as candidates won't know which question is scored and which isn't.

Thus, going with the thought that a particular type of question might be a pre-test can be challenging and cause wastage of quality time. Hence it is advisable to treat each question as scored.

Keep in mind that if a specific question is confusing, you need to have the best guess and swiftly move into another instead of sticking onto it, as you need to be aware that there is a 10 percent chance of it being a pre-test.
 

#5: Careful Reading of Questions

It is seen that candidates lose valuable scores, especially when answering easy questions out of rush. The exam is for 3 hours, which is a lot of time to read questions and choices completely carefully before answering. It is significant to prepare good practice right from the mock and sample exams during the test preparation phase.
 

#6: Depend on Your Strength

Depending on the experience, learning, potentials, and background, we tend to be relatively robust in particular areas. Therefore, during practice tests, note the strength and weaknesses areas. While it's good to practice hard on the topics, integrating your solid regions and scoring well in them is equally significant.
 

#7: Charting Down of Formulas

Before the exam begins, you can chart down the learnt formulas, and other essential topic-related information on exam center provided paper or electronic whiteboard. This will minimize the tension that many of us feel during an exam and help us refer to this charted information for successful scoring.
 

#8: Read PMBoK Guide

While CAPM preparation courses make it easier to understand the concepts methodically, it can't be talked enough about the significance of interpreting the PMBoK Guide. It is necessary to assign adequate time to read the guide as part of the exam preparation.

Since CAPM is for those starting their career in the project management profession, more weightage is provided on the candidate's knowledge of the guide. In contrast, in PMP, which is for expert project personnel, there is an extra weightage on the knowledge application.
 

#9: Reading CAPM Handbook

As part of test preparation, it is very prominent to read PMI'S CAPM handbook, which assists the participants in gaining insights related to various policies and rules. This can be downloaded free from the official website of PMI.
 

#10: Enhance Your Confidence

When you try to answer practice questions, sometimes it may appear more than one correct choice, and through the elimination process, you narrow down your guess to just one answer. This helps keep track of question types you scored right by guessing and revising that specific course area.

Typically, amid practice tests, it is suggested that you target achieving above 70 percent correct answers while reducing any guesswork. This will help you attain confidence during the CAPM preparation stage and significantly increase the chance of passing on the first attempt. Tracking your performance and progress will help you know the areas you lack and need more attention.
 

Concluding Thoughts

CAPM exam preparation training will help you attain your desired goal of CAPM certification. If you are a rookie in project management or job hunting, this certification with good training will advance your career growth. A certificate from globally-renowned organizations is career promoters. 

People who contribute specialized skills to a team can benefit from this certification by setting their duties with project managers. The understanding a practitioner gains from earning the CAPM certification can be applied to on-the-job experiences, which help develop growing competence levels in project management. 

Individuals who carry the CAPM designation after their name enjoy a high level of credibility from PMP credential holders, project managers, employers and peers.

Still confused about whether to go for CAPM certification or not, then don’t waste any time; this is definitely your call; pick it up.
 

About Us

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world.

We offer a 2-day intensive full-time CAPM certification training course. Our course content includes two full-length exam practice test, 300 exam prep questions with solutions and complimentary e-learning access of 3-months, enabling you to achieve your learning objectives.

Visit our website to find out more about the course.

https://www.icertglobal.com/ 

Our company conducts both Instructor-led Live Online Training sessions and Instructor-led Classroom training workshops for learners across the globe.

We also provide Corporate Training for enterprise workforce development

Data Science & BI courses

• Data Science with R Programming Certification
• Apache Kafka Certification
• Power BI Certification

Quality Management Training

•  Lean Six Sigma Yellow Belt (LSSYB) Certification Training Courses
•  Lean Six Sigma Green Belt (LSSGB) Certification Training Courses
•  Lean Six Sigma Black Belt (LSSBB) Certification Training Courses

 DevOps Training

• DevOps Certification Training Courses

Business Analysis Training by iCert Global:

• ECBA (Entry Certificate in Business Analysis) Certification Training Courses
• CCBA (Certificate of Capability in Business Analysis) Certification Training Courses
• CBAP (Certified Business Analysis Professional) Certification Training Courses

For the past few decades, Machine Learning (ML) has transformed our lives. From clicking pictures with a blurry background & focused face to virtual assistants such as Alexa and Siri answering our queries, we are significantly dependent on applications that execute ML at their core.

ML engineering combines data science and software engineering. A data scientist will examine the obtained data extract actionable insights; an ML engineer will develop the self-running software that leverages the extracted data and automates predictive models.

These engineers are experienced in basic data science skills like quantitative analysis methods, stats, data structures & modelling, and developing data pipelines, while also having fundamental software engineering skills.

With so much happening around the breakthrough technology, it is no wonder that any enthusiast who is keen on advancing their career in software technology & programming would choose ML as a base to set their job.

If you are keen on shaping your career with ML but not familiar with critical skills, then this blog is for you. We will be taking you on tour specifying the essential skills required to be an ML engineer. So, keep reading till the end.
 

Technical Skills for ML Engineers

We have learned how ML application operates, followed by numerous job opportunities in the IT field for software engineers and data scientists. To be a part of ML technology, you need specific technical and soft skills. 

Firstly, we will see technical skills required for an engineer, and they are:

Neural Network Architecture

Neural networks, also called Artificial Neural Network (ANN) or Simulated Neural Network (SNN), are the predefined algorithm sets used for ML task implementation.

They provide models and play a vital role in this futuristic technology. Now, ML seekers must be skilled in neural networks because it offers an understanding of how our brain works and assist in model & simulating an artificial one. It also provides in-depth knowledge about parallel and sequential computations.

Some of the neural network areas that are essential for ML are:

• Boltzmann machine network
• Convolutional neural networks
• Deep auto-encoders
• Long short-term memory network (LSTM)
• Perceptron
   

Natural Language Processing (NLP)

It is a branch of linguistics, AI & computer science that, when combined with ML, Deep Learning (DL), and statistical models, enables computers to process human language in the form of spoken words and text and understand its whole meaning with writer's intent.

Several techniques and libraries of NLP technology used in ML are:

• Word2vec
• Summarization
• Genism & NLTK
• Sentiment analysis
   

Applied Mathematics

ML is all about creating algorithms that can learn data to predict. Hence, mathematics is significant for solving data science projects DL use cases. If you wish to be an ML engineer, you must be an expert in the following math specializations.

But why math? There are several reasons why an ML engineer needs math or should depend on it. For instance, choosing appropriate algorithms to suit the final outcomes, understanding & working with parameters, deciding validation approaches, and estimating the confidence intervals.

If you are wondering about the math proficiency level one must hold to be an ML engineer, then it depends on the level at which the engineer works. The below-shown pie chart will give you an idea of how significant various math concepts are for an ML engineer.

Data Modeling & Evaluation

An ML has to work with a colossal amount of data and use them in predictive analytics. In such a scenario, data modelling & evaluation becomes beneficial in dealing with these bulks and estimating the final model's good.

Hence, the following concepts are must learn skills for an ML engineer:

• F1 Score
• Log loss
• Mean absolute error
• Confusion matrix
• Classification accuracy
• Area under curve
• Mean squared error
   

Video & Audio Processing

This processing concept is different from NLP because audio & video processing can only be applied to audio signals. For this, the following ideas are essential for an ML engineer:

• TensorFlow
• Fourier Transform (FT)
• Music theory
   

Advanced Signal Processing Techniques

Signal processing targets analyzing, modifying, and synthesizing signals to minimize noise and extract the provided signal's best features. For this, the techniques leverage certain concepts like spectral time-frequency analysis, convex optimization theory & algorithms, and algorithms (bandlets, shearlets, curvelets, wavelets, etc.)
 

Reinforcement Learning

Reinforcement learning is an ML area that takes suitable action by employing several machines and software to increase rewards in a particular scenario. Though it plays a vital role in understanding and learning DL & AI; however, it is beneficial for ML beginners to have an insight into the fundamental concept of reinforcement learning.
 

Soft Skills for ML Engineers

While ML engineering is a technical job, soft skills such as problem-solving, collaboration with others, communication, time management, etc., are what lead to successful completion and delivery of the project.
 

Here are some of the soft critical skills an ML engineer must possess:

Team Work

ML engineers are often at the core of AI initiatives within a company, so they naturally work with software engineers, product managers, data scientists, marketers and testers. The potential to work closely with others and contribute to a supportive working environment is a skill many recruiters seek in ML engineers.
 

Problem-solving 

The potential to solve an issue is a significant skill required for both software & ML engineers and data scientists. ML focuses on solving challenges in real-time, so the potential to think creatively and critically about the problem and develop solutions accordingly is a fundamental skill.
 

Open to New Learning 

The fields of ML, AI, DL and data science are drastically evolving, and those who have earned a degree and working as an ML engineer find ways to learn new things through workshops, boot camps and self-study.

Whether learning the latest programming languages or mastering new tools, the most effective ML engineers are open to new learning skills and constantly refreshing their learnt toolkits.
 

Communication 

ML engineers must possess excellent communication skills when communicating with shareholders regarding the project objectives, timeline, and expected delivery. We know that ML engineers collaborate with data scientists, marketing & product teams, research scientists, and more; hence, communication skill is crucial.
 

Domain Knowledge 

To develop self-running software and optimize solutions leveraged by end-users and businesses, ML engineers should have an insight into the requirements of business demands and the type of issues the software is solving. 

Without domain knowledge, an ML engineer's recommendation may lack accuracy, their task may overlook compelling aspects, and it might be strenuous to evaluate a model.
 

Programming Skills for ML Engineers

Machine learning is all about coding and feeding the machines to carry out the tasks. ML engineers must have hands-on experience in software programming and related subjects to provide the code.

Let's see the programming skills an ML engineer is expected to have knowledge on:

ML Algorithms & Libraries 

ML engineers are expected to work with myriads algorithms, packages, and libraries as part of a daily task. ML engineers must be skilled with the following ML algorithms and libraries:

• Knowledge in packages & APIs - TensorFlow, Spark MLlib, scikit-learn, etc.
• Decide and choosing of hyperparameters that impact the learning model & the result.
• Algorithm selection provides the best performance from support vector machines, Naive Bayes Classifiers and more.
• Expert in model handling like decision trees, neural net, SVMs and deciding which is suitable.
   

Unix

ML engineers require most servers and clusters to operate are Linux (Unix) variants. Though they can be performed on Mac & Windows, more than half of the time, they are required to run on Unix systems only. Therefore, having good knowledge of Linux & Unix is vital to being an ML engineer.
 

Computer Science Fundamentals & Programming

Engineers must apply the concepts of computer science and programming accurately as per the situation. The following ideas play a significant role in ML and are a must on the skillset list:

• Algorithms: search, sort, optimize, dynamic programming
• Computer architecture: memory, bandwidth, cache, distributed processing and more.
• Data structures: queues, trees, stacks, graphs and multi-dimensional arrays
• Complexity & computability: big-O notation, P vs NP, approximate algorithm, etc.
   

Distributed Computing

Being an ML engineer means working with massive data sets and focusing on one isolated infrastructure, and spreading among system clusters for data sharing. In such a situation, these engineers must know the concept of distributed computing.
 

Software Engineering & System Design

ML engineers must have sound knowledge of the following areas of software programming & system design, as all they do is code:

• Top-notch measures to circumvent bottlenecks & develop user-friendly outcomes.
• Algorithm scaling with data size.
• Interacting with different working components and modules using library calls, REST APIs and database queries.
• Fundamental software design methodologies and coding like testing, requirement analysis and version management.
   

Key Programs to Master for ML Engineers

In addition to an in-depth knowledge of programming languages such as SQL, C++, Python and Java, several ML engineers are also experts in the following tools:

• AWS ML
• IBM Watson
• TensorFlow
• R
• MATLAB
• Google Cloud ML Engine
• Weka
• Hadoop
• Apache Kafka
   

Final Thoughts

Knowing ML and DL concepts is necessary but not enough to get recruited. The technologies are evolving to new heights each day, and ML has been amplifying its growth. Global companies are heading towards applying AI, ML & DL in their sectors to scale up. This futuristic trend highlights how ML plays a vital role in online services, and mastering the necessary skills will keep you on the path where opportunities are boundless.
 

About Us

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world. Visit our website to find out the different technology courses.

https://www.icertglobal.com/ 

Our company conducts both Instructor-led Live Online Training sessions and Instructor-led Classroom training workshops for learners across the globe.

We also provide Corporate Training for enterprise workforce development

Data Science & BI courses

• Data Science with R Programming Certification
• Apache Kafka Certification
• Power BI Certification

Quality Management Training

•  Lean Six Sigma Yellow Belt (LSSYB) Certification Training Courses
•  Lean Six Sigma Green Belt (LSSGB) Certification Training Courses
•  Lean Six Sigma Black Belt (LSSBB) Certification Training Courses

 DevOps Training

• DevOps Certification Training Courses

Business Analysis Training by iCert Global:

• ECBA (Entry Certificate in Business Analysis) Certification Training Courses
• CCBA (Certificate of Capability in Business Analysis) Certification Training Courses

CBAP (Certified Business Analysis Professional) Certification Training Course

Any small-scale organization wants an efficient working environment with minimal waste, streamlined workflow, and high revenue. But often, the question pops up: From where to begin the cleansing?

While most Lean Six Sigma professionals consider 5S methods a tool, it is more than that. It was developed in Japan by Taiichi Ohno, who designed the Toyota Production System & Shigeo Shingo. 

The 5S methodology is a culture that must be implemented in an organization looking for continuous and spontaneous enhancement of working environment and conditions. 

This principle is easy to use and doesn't require any technical analysis. 5S is implemented in organizations ranging from manufacturing to offices, private and public sectors.

Today we will discuss the following topics:

• What is 5S?
• Why leverage the methodology?
• The 5 steps of project approach
• 5S method implementation
• The 6^th S
   

What is 5S?

5S comes under the lean manufacturing umbrella is a system leveraged for creating an organized, clean working environment that makes it easy to determine waste and irregularities so that workforces can do their task efficiently and without injury risks.

Each 's' stands for Japanese word:

• Seiri - sort
• Seiton - set in order
• Seiso - shine
• Seiketsu - standardize
• Shitsuke – sustain

The methodology provides a framework for developing a visual management process. Budget-oriented tools such as operational manuals and floor tape can be leveraged as guides to scrutinize the working conditions and determine areas of potential enhancement.
 

Why Leverage the 5S Practice?

There are numerous benefits to be garnered by leveraging 5S within your company, from obtaining more efficient and organized manufacturing working conditions to driving high-level profits.

So, we can say 5S is the perfect approach to determine the first enhancement projects in your organization to eradicate waste. Though it is a housekeeping technique, it is more of an innovative management method that assists workforces to think lean, leading the way for Lean principle adoption in your firm.

Understanding the approach is one of the base levels of 6 sigma principles and can be tremendously helpful for companies of all sorts. For example, if you determine and then eradicate unnecessary obstacles from the manufacturing workflow, your productivity will directly expand. In turn, this will accelerate value and workforce motivation. These results combine to boost the manufacturing operation, followed by your bottom line.

With this insightful, step-level guide, you will understand the valuable part of the methodology, how to formulate an action plan for its implementation, and how great to maintain this robust tool for enhanced productivity, waste elimination and overall efficacy.
 

The 5 Steps of the Project Approach

The execution of the 5S methodology needs time and resources and will transform the working conditions of many individuals. Therefore, managing the process as a whole change project with five basic procedures.

1. Diagnosis

Determine current issues, areas for enhancement and associated problems in the different points of the defined scope. It is essential to ensure the management support from mid-management who may be reluctant to execute 5S.

Once improvement areas are identified, the next step in the diagnosis level is to estimate the potential shareholders or stakes.

2. Project Preparation

The first step in project preparation is defining the approach's purpose and the expected savings. Once sorting this comes the application areas and improvement areas selection. Here the project may cover only a part of the initial possibilities or not attempt to find resolutions for the issues identified, and choose the pilot, which is the next project approach phase.

The 3rd step in this phase - defining the implementation plans such as pilot duration, duration per improvement type, what will be the implementation team, the roles & responsibilities of the team, monitoring approach and the necessary budget.

3. Pilot

This is the phase where we implement the 5S methodology. The first step in the pilot is to train the project team and the workforce in this area. After that comes the 5S implementations (sort, straighten, shine, standardize and sustain) - performed for the pilot area and then for each deployment phase area, with an initial preparation phase.

The next step is to show the savings for team motivation and display process interest. Here it is necessary to check that the savings have corresponded to the initially estimated savings. Finally, the method improvement, where approach implementations are tested before deploying it in other areas.
 

4. Implementation

As the zones are deployed, the remaining skillsets are trained. Now, the 5S is integrated into the operational system, managerial follow-up and support functions such as training, team meetings, etc. The final step is to measure and communicate savings. This step is crucial to continue demonstrating the approach value, from the operational to top-tier management.

5. Sustainability

This is the last phase of the project approach. Here we have 2 steps that are: Monitoring and Auditing.

Monitoring - the effective integration into operational modes is monitored regularly. Then, the 5S approach is managed in a similar active way, working with 5S performance indicators.

Once done with monitoring, the process is moved onto the auditing stage.
 

5S Implementation Plan

Here we will see why a company requires a 5S methodology and its implementation plan for a better working environment? If you are among those required of an efficient workflow, then this is for you.

Let’s go…

Stage1: Is Your Business Profitable?

Before getting started, let's answer some questions about why 5S can be effective in your organization.

Do your employers have difficulty finding documents or crucial files, be it a digital or physical format? Are there cabinets and files left unlabelled or contain unmarked contents making it hard to differentiate? Do unwanted materials consume your valuable office space? Do your employees know how to keep their working environment clean and organized, followed by full awareness of their roles and responsibilities?

If any of these questions answers are YES, then the 5S methodology is precisely the thing you require.

Stage2: Steps Towards Process Excellence

The 5 steps towards process excellence are as follows:

Sort (Seiri) 

Separation of necessary materials, instructions and tools from those not required from the work area.
 

Set in order/ straighten (Seiton) 

Organizing and sorting resources, materials, files, data, etc., for swift, quick location and utilization. Label all storage locations, devices and tools so that anyone can identify them and return them once the work is completed.
 

Shine (Seiso) 

The setting of new cleanliness standards. It offers a safe working environment and creates potential issues such as loose parts, missing guards, device leaks, etc. Hence to maintain the standards and detect defects, it is necessary to clean the device and workplace regularly.
 

Standardize (Seiketsu) 

Engage the employees to systematically follow the 1st three steps of 5S, i.e., Sort, Set in order and Shine regularly, to maintain the company in perfect condition as a standard procedure.
 

Sustain (Shitsuke)

Make 5S methodology a part of your working culture build commitment so that the practice becomes one of your organizational values allowing others to develop the technique as a habit. Don't forget to merge the method into the performance management system.
 

Stage3: The Action Scheme

By providing training sessions to your workforce, you can implement the 5S plan into the organization. For the proper and efficient implementations, one-day sessions with each team are done to ensure harmony.

Here are the following things that occur during the first one-day session:

• Preparation: click a picture of your current workplace for later comparison.
• Sort: separation of required and necessary materials from those not required.
• Set in order: organizing the sorted materials so that everything is neat and has a specific place for easy finding.
• Shine: to maintain cleanliness, use proper standards by cleaning the area and eradicate unwanted things consuming the workplace space. Make sure that you label the drawers, cabinets and files for a neat presentation.
• Action: prepare an action scheme for materials that you couldn't deal with that day but will be able to the next day or so. This can include material donation, selling, eradicating or even recycling.
• Review: Take a second picture after the entire day's task and review it with the start-of-the-day view.
   

Stage4: Standardize

Amid the second one-day session, which is not supposedly the next day but a week or two later, employees must take the third picture and review it with the first 2 images. Several companies set up peer audits to monitor how the 5S methodology is fulfilled and ensure that the plan moves ahead.

Work together to define ways to organize cleaning schedules, written diagrams or procedure preparation and employee roles & responsibilities to help employers standardize the improvements.
 

Stage5: Sustain

During the 3rd one-day session, a month or two later, review how your workplace looks and arrange another peer analysis. This will ensure that the 5S methodology is sustained by monitoring whether defined arrangements are met and everyone follows the practices.
 

The 6^th S?...

Over the years, there have been discussions of whether or not to include a 6th S - Safety into the 5S methodology. Some argue that Safety is a fundamental part of 5S, while others say it authorizes a much greater focus.

6S targets promoting and sustaining a top-level of Safety and productivity throughout a workspace. It not only assists companies in promoting an efficient workplace but also establishes a sustainable safety culture.

Irrespective of whether you practice 5S or 6S, it remains a system that promotes continuous enhancement within the manufacturing environment by eradicating waste and enabling companies to achieve quality consistency and delivery (QCD).
 

Conclusion

5S is more than a cleansing procedure, offering a foundation for developing a successful Lean initiative. It’s also a budget-oriented solution for smooth internal workflows, preventing fallacies, and building a culture where everyone is responsible for maintaining a safe environment.

The methodology empowers workforces to take responsibility for their working conditions, motivating them to be more productive in waste minimization. This, in turn, enhance productivity, company value and profit.
 

About Us

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world. Visit our website to find out the different technology courses.

https://www.icertglobal.com/ 

Our company conducts both Instructor-led Live Online Training sessions and Instructor-led Classroom training workshops for learners across the globe.

We also provide Corporate Training for enterprise workforce development

Data Science & BI courses

• Data Science with R Programming Certification
• Apache Kafka Certification
• Power BI Certification

Quality Management Training

•  Lean Six Sigma Yellow Belt (LSSYB) Certification Training Courses
•  Lean Six Sigma Green Belt (LSSGB) Certification Training Courses
•  Lean Six Sigma Black Belt (LSSBB) Certification Training Courses

 DevOps Training

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The global pandemic witnessed businesses transitioning to remote working, requiring IT workforces to manage more devices than before. Cyber attackers began exploiting security flaws in a cycle of evident cyber disasters, taking advantage of the never-expected situation.

As the world becomes more virtual, malicious attacks continually make news that has inspired companies to adopt basic cybersecurity practices. But these practices weren't sufficient to stop the drastic growth of cybercrimes due to technological advancements.

Most of the cybercrime takes place in big organizations. According to the 2020 Gone Phishing report, it is seen that 58.2% of workforces from small-scale companies have fallen into the hacker's trap, 71.5% from mid-scale firms, and 67.4% from huge companies have been deceived by cyber attackers.

It was observed that small-sized organizations are worst affected by cybercrime as 60 percent of them go out of business within 6 months of falling into data breach traps. The Global Market Insights unveiled that by 2024, the cybersecurity sector will reach $300Bn, and the loss incurred by it will be more than $2Tr.

The primary motive of malevolent hackers includes the exploitation or theft of crucial organizational data or financial profits. However, not all hackings are detrimental, which brings us to the second type of hacking known as 'Ethical Hacking.'

Here we will highlight the term Ethical Hacking and other associated term lingering around it. When seeing the phrase, never-ending questions arise: What is Ethical Hacking? Does it have anything to do with phishers? Is it beneficial for organizations? What are their roles? And so on. Let's answer a few of the questions, shall we?
 

Defining ‘Ethical Hacking’

Ethical Hacking is an accredited method of avoiding system security to determine potential information breaches and threats in a network. To test the system or network, companies allow Cyber Security engineers to safeguard the crucial data.

Unlike cyber attacks, Ethical Hacking is legal, planned and approved method, scrutinizing the network for loopholes that attackers can exploit. They collect and analyze data to identify different ways for enhancing security footprint so that it can better withstand attacks.

Ethical Hackers are recruited by companies to check the weak points of their networks and systems and develop top-notch solutions to hinder data breaches.

The critical vulnerabilities tested by them are:

• Sensitive data exposure
• Injection attacks
• Components leveraged as access points
• Security setting modifications
• Authentication protocol breach
   

Purpose of Ethical Hacking

Cybercrime breaking records in today's world, the need for Ethical Hackers skyrocketed. Here are some of the purposes of Ethical Hacking:

Prevention of Unauthorized Data Access

Installing just a firewall won't be beneficial in safeguarding our systems or networks from data security threats. Companies must challenge their own security system with crucial probes and assessments to develop an efficient security regime.

Ethical hacking assists you do that by mimicking a malevolent attacker's technique, learning from the experience and problem resolutions. It aids organizations to stick to compliance standards and offer assurance that a user's data are adequately safeguarded.

They identify vulnerabilities in the code by testing the security of applications, emails, instant messaging, databases, etc., and evaluate workforce susceptibility to pretext & social engineering.
 

Criminal attack Prevention

Ventures can incur substantial penalties due to criminal attacks along with a drastic reputational downfall. Fines are imposed because of the failure to adhere to compliance standards like PCI-DSS, HIPAA, GDPR and more.

Ethical Hacking prevents this by alerting the venture about the developing attack techniques, thus helping security professionals to prepare for securing their security systems.
 

Determining Weak Points 

The weak points in the IT system are often exposed to malicious attackers leading to data misuse. To prevent this, Ethical Hackers conduct vulnerability scanning for determining the loopholes. We can also analyze the source code to identify weak points, but the process is monotonous, and, in some cases, we won't have code access.

Another fame-gained method to determine the loopholes is Fuzzing - interfering with a program and its input to crash, unveiling security problems.
 

Secure Network Implementation

This type of hacking enables the company to enhance its network by testing and prodding the architecture to identify vulnerabilities. It lets organizations create a robust tech system by securing network ports, configuring firewalls and permitting administrators to determine and execute the security policies.
 

Different Types of Ethical Hacking

The Ethical Hacking process can be categorized into different types, and some of them are:

System Hacking

System hacking is a method of attaining unauthorized access to data and systems. Black hat hackers primarily leverage prominent ways of password hacking to avoid computer security and obtain system access. 

The ultimate goal of such hacking type is to get system access, escalate privileges, perform applications or hide files. To prevent system hacking, Ethical Hackers provide suitable suggestions to the users.
 

Social Engineering

In Social engineering, with the help of technology, hackers trick you into providing information such as credit card details, personal data, login credentials, etc., or provoke them to take action.

It takes advantage of the victim's emotional vulnerabilities and natural tendencies. Hence you need to maintain strong security regulations and make awareness among the workforce to avoid such baits.
 

Web Application Hacking

Web applications store different kinds of data such as bank information, login details, etc. Cyber attackers seek other ways to steal this information by avoiding application security approaches. They try gaining access through stereotypical ways like:

• SQL injections
• Data leakage
• Cross-site scripting (XSS)
• Broken authentication and access control
• Cross-site request forgery (CSRF)

Ethical Hackers are responsible for determining these security vulnerabilities and recommend appropriate resolutions.
 

Types of Ethical Hackers

There are 3 types of hackers, and they are as follows:

White Hat Hackers

These ethical hackers operate for companies to fill their gaps in the security systems. They acquire legal permissions to manage the penetration test and engage the attackers in a controlled way.

White hat hackers consistently report the weak points found in their penetration tests and allow the company to intensify its security policies.
 

Black Hat Hackers

These are malevolent attackers who make the most of vulnerabilities in a company to obtain unauthorized access. They hack systems and networks without legal permission for harming the company's reputation, data theft and creating functionality augmentations.
 

Grey Hat Hackers

Though they are ethical hackers, they sometimes gain access to a system or network by breaking the law. However, they don’t have malicious intent, unlike Black hat hackers. After gaining system access, white hat hackers, instead of reporting the weak points, alert the admins that they can fix those issues for a small compensation.
 

Roles and Responsibilities of Ethical Hackers

For legal hacking, Ethical Hackers must have an insight into the particular guidelines and follow them accordingly. Here are the crucial rules of Ethical Hacking:

• Hackers must obtain authorization from the company that owns the system. They must obtain complete approval from the client-end to execute any security infrastructure assessments.
• Must report security threats and breaches identified on the infrastructure.
• Identify the assessment scope and provide the company with assessment plans. 
• Breach identification must be confidential. They must sign and respect the NDA, as their aim is to safeguard the system.
• Eradicate all hack traces after checking for vulnerabilities. This prevents malevolent attackers from entering through the identified weak points.
   

Ethical Hacker Skills

An Ethical Hacker must have a deep insight into systems, program codes, networks, security approaches and many more for efficient hacking performance. Some of the skills are:

• Networking skills are vital as breaches and threats mostly evolve from networks. You must know about different devices connected to the network, how are they linked and how to determine if they are compromised.
• Programming insight is necessary for security experts operating in the application security & Software Development Life Cycle (SDLC) field.
• Insight of numerous platforms such as Unix, Windows, Linux, etc.
• Scripting knowledge - It is necessary for professionals handling network and host-related attacks.
• Understanding database - Knowledge of database management systems like SQL will be beneficial for inspecting operations carried out in the database, as attacks mainly occur in it.
• Potential to work with different hacking tools
• Search engine and server knowledge.
   

Ethical Hacking from Scratch to Advance

A person with the skills mentioned earlier can't necessarily be an Ethical Hacker or successful in the cybersecurity field. Instead, if you are a CEH certified professional, you are sure to succeed.

Ethical Hacking can be one of the prominent, exciting and innovative job trends. As the cyber field evolves every day and the business transformation into virtually opened chances for new ransomware, you must have the potential to conduct the probe and familiarize yourself with those.

The first step to being an Ethical Hacker is to start preparing for CEH certification.

Let us see the learning techniques of Ethical Hacking from scratch to advance.
 

Certified Network Defender (CND)

CND is an adaptive security approach developed on a 4-branched strategy - Protect, Detect, Respond and Predict. It is suitable for individuals working in cybersecurity or the network administrative fields in the capacity of network engineer, security analyst or network administrator. 

Anyone looking forward to advancing their career in this domain, then CND is just for you.

A CND will acquire a basic understanding of the data transfer, software & network technologies, so network administrators can understand how the network works, what software is automating and how a subject material is analyzed.

Moreover, the primary network defence, network security control applications, IDS securing, firewall configuration, vulnerability scanning, etc., will assist them in developing better security policies and incident response plans.
 

Certified Ethical Hacker (CEH)

It is a qualification obtained by demonstrating knowledge of assessing system security by looking for vulnerabilities using tools and practices similar to malicious attackers but in a legitimate way.

These hackers will undertake all preventive approaches required to safeguard a network or system against actual attacks that might happen in the future. Industry acceptance of Ethical Hackers has created the idea that this type of hacking is not just a helpful ability but good work.

The next step after being a certified EH is CEH (Practical). It is a 6-hour test that needs you to apply EH techniques such as web app hacking, threat vector identification, OS detection, system hacking, etc., to solve a security audit challenge.

By completing both CEH and CEH (Practical), you can CEH (Master) designation. This is a global-renowned CEH program that offers you a chance of proving to your co-workers, workforce and most importantly to yourself that you are ready to overcome challenges found in daily life as EH.

You won’t have exam simulations; instead, you will test your potential with real-world challenges and time limits, just as you find it in your work.
 

Certified Threat Intelligence Analyst (CTIA)

It is developed in collaboration with threat intelligence and cybersecurity proficient worldwide. It focuses on assisting companies to hire qualified cyber-intelligence-trained candidates to determine and reduce business risks by converting mysterious internal and external threats into quantifiable entities and halting them in their tracks.

Companies these days demand expert-level CTIA capable of extracting data intelligence by executing various advanced approaches. CTIA leverages a 360-degree system for pre-emptive threat detection and prevention methods. These are highly beneficial while creating threat intelligence and, when leveraged correctly, can secure companies from future cyberattacks.
 

EC-Council Certified Security Analyst (ECSA)

It is a program that develops on the previous program - CEH. This certification teaches cybersecurity professionals advanced security methods and Licensed Penetration Tester (LPT) practices. It is an excellent choice for intermediate-level security managers, security architects, penetration testers, and consultants.

ECSA is the 2nd phase of the 3-phase process, where experts begin with CEH, then take ECSA and complete Ethical Hacking certification with LPT. You can show the recruiting company that you are an expert in the skills and practices needed to safeguard their data and systems by gaining this certification.

Similar to CEH, ECSA also has a Practical certification. This tests your potential to perform threat & exploit research, understand it, write your own exploits, customize payloads and make crucial decisions at different stages of a pen-testing engagement that can either make or break the whole assessment.
 

Licensed Penetration Tester (LPT Master)

This is the last certification of EH that you will be acquired after successfully completing CEH and ECSA. LPT turns you into a master in pen-testing practices and tools by offering you the most demanding challenges with a time limit. 

Your pen-testing skills will be challenged over 3 layers (3 challenges each) against a multi-layered network architecture with in-depth defence controls. While selecting your exploits and approach, you will need to make knowledgeable decisions under tremendous pressure.
 

Conclusion

With businesses entering digital platforms once pandemics struck, Ethical Hacking has become a hot trend with increasing demands and interests. While malevolent hackers try different methodologies to breach the network, Ethical Hackers have put a barricade on every system's loophole, preventing cybercrimes to a more significant extent.

If you consider entering the cybersecurity domain or trying to upskill, then this is a perfect time.
 

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iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world. Visit our website to find out the different technology courses.

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If you were to gather the world's prominent business luminaries and ask them to determine the most significant difference underlying the business in the 20th century and 21st, more than half would say it's 'Data.'

With the emergence of IoT, social media, smartphones and other tech advancements, data expansion in business had significant growth. The data growth has led small-scale and large-scale companies to think of how to leverage information for business benefits. Meanwhile, people started seeking different options to develop their data skills, advance their careers and gain job security.

If you have no prior command of data and are trying to bolster your skills, 2 terms you are likely to encounter two terms: Data Science and Business Analytics. Though both seem like a similar job role at first, there are several differences.

Data Science and Business Analytics involve knowledge & information gathering and modelling. However, the difference is that Analytics is specific to business-related issues such as profit, cost and so on; on the other hand, Science answers questions such as geographic influence, customer business demands and seasonal factors.

In a simplified version, we can say that Data Science combines data with algorithm technology & building to answer a wide array of questions. In contrast, Business Analytics is the company data analysis with the statistical concept to obtain insights and solutions.

To have a deep understanding of the differences between the two, let us look at some of the basic concepts: What is data? Definition of Data Science and Business Analytics.
 

What is ‘Data’?

Data is a set of values in different formats such as text, numbers, bits & bytes, and human memory facts. It is meaningful only when Data is placed in a context. We have noticed the interchangeable use of ‘information’ and ‘data.’ Though they seem similar, they have a vast meaning difference.

Information is more of an abstract concept, whereas Data is a solid concept. Computers leverage different kinds of data stored in digital formats such as multimedia, text, and numbers. Professionals dealing with these data are known to be Business Analysts and Data Scientists.

What are Business Analytics?

It is all about determining different ways or methods to enhance a business. Though the medium has evolved over the years, Business Analytics as a concept has been leveraged since the 19th century. 

With the help of data and statistics, Business Analysts will resolve an issue faced by a business or a company analytically. Business Analytics may include analyzing company data, forecasting from previous data, strategical enhancement through optimization, data visualization enhancement via charts and graphs.

Their role is to analyze data and be responsible for sharing their finding with other workforces, supporting them to make changes that you are suggesting.

What is Data Science?

Data Science follows a similar practice to Business Analytics but is much more comprehensive. It is the data examination and research revolving around the developing methods to store, record & analyze data for efficient extraction of relevant information.

The term 'Data Science' was coined in 2008 by Jeff Hammerbacher and DJ Patil when working for Facebook and LinkedIn. Its primary focus is to derive insight and knowledge from any data form, whether structured or not. Data Scientists leverage their skills in a wide range of industry verticals like technology, academia and finance.
 

Data Science Vs Business Analytics

Here are the top comparisons have seen between Data Science and Business Analytics:

1. Coining of Term

The term 'Data Science' was introduced in 2008 by Jeff Hammerbacher and DJ Patil when working for Facebook and LinkedIn, respectively. 

Business Analytics as a concept has been leveraged since the 19th century when it was introduced by Fredrick Winslow Taylor.
 

2. Concept

Data Science leverages the interdisciplinary field of algorithm building, data inference and systems to obtain data insights.

Business Analytics uses statistical concepts for extracting business data insights.
 

3. Industrial Application

The top 5 industries where Data Science is leveraged are:

• Academia
• Financial
• Technology
• Internet-based
• Hybrid fields

The top 5 industries where Business Analytics is leveraged are:

• Retail
• CRM
• Technology
• Hybrid fields
• Financial
   

4. Coding

Coding is widely used in Data Science. The field mixes traditional analytics principles with in-depth computer science knowledge.

Business Analytics does not involve much coding as it is more statistics oriented.
 

5. Language Tools

The language tools used in Data Science are:

• C/C++/C#
• Stata
• MATLAB
• Scala
• Haskell
• SAS
• R
• SQL
• Java
• Python
• Julia

The language tools used in Business Analytics are:

• SQL
• C/C++/C#
• Scala
• Java
• R SAS
• MATLAB
• Python
   

6. Statistics

In Data Science, statistics is leveraged at the end of analysis following coding and algorithm building.

In Business Analytics, the fundamental analysis is statistical oriented.
 

7. Work Challenges 

In Data Science, the business decision-makers do not leverage the outcomes. It cannot apply findings into the decision-making process of a company. There is no accuracy on the questions that need answers with the provided data set. The top challenge among Data Science is its difficulty in data accessing and the prerequisite of IT coordination.

Similar to Data Science, Business Analytics cannot apply findings into a company's decision-making process, no accuracy on the questions that need answers with the provided data set, difficulty in data accessing, and the prerequisite of IT coordination. Other work challenges seen here are the lack of significant domain expert input, data inaccuracy, privacy concerns, fund shortage to buy relevant data sets from external sources, and tool limitations.
 

8. Data Types

Data Science uses 2 types of data: big data and traditional data. Traditional Data means structured data stored in a database. In contrast, big data include a wide variety of Data - text, images, mobile data, numbers and audio, Velocity - retrieved and computed, and Volume - measured in Tera, Peta and Exabytes.

Business Analytics predominantly uses structured data. This historical Data helps understand the factors that may impact your company.
 

9. Future Trends

The future application of Data Science is Artificial Intelligence (AI) and Machine Learning (ML).

The future trend of Business Analytics would be in Tax Analytics and Cognitive Analytics.
 

10. Disciplines

Data Science provides data insights that assist companies in increasing their operational efficacy, determining new market choices, enhancing sales and marketing efforts, and many more - giving a competitive edge in the market. Some of the disciplines involved in this field are:

• Predictive analytics
• ML and Deep Learning (DL)
• Business Intelligence (BI)
• Data and Warehouse engineering
• Statistical analysis
• Data visualization & mining

Business Analytics includes determining business requirements, leveraging previous data, finding solutions - new system development, strategic planning, and process optimization. Some of the disciplines involved in this field are:

• Data analysis
• Solution assessment
• Elicitation and Analysis prerequisites
• Workflow modelling
• Business modelling
   

11. Job Opportunities

Data Science skillsets are required in most job sectors and are not restricted to tech-related industries. However, you get an opportunity in these high-paying, in-demand professions at tech giants an advanced degree is a prerequisite.

The in-demand profession includes:

• Data Engineer
• BI developer
• Data scientist
• Applications architect
• Data analyst
• ML engineer

Recruiters in Business analytics generally look for hiring the following professionals:

• IT business analyst
• Business analyst manager
• Data business analyst
• Computer Science data analyst
• Data analysis scientist
• Quantitative analyst
• System analyst
   

12. Salary

Data scientists enjoy high-pay salaries and job expansion. According to 2020 BLS data, the average wage earned by Data Scientists was $126,830 per year, with the highest 10% making in 2020. According to LinkedIn, the average salary of Data Scientists in India is INR 850K, and in the US, it is $125,044. Based on experience, first-level Data Scientist earns around INR 611K and $98,122 per year, while most experienced workers make up to INR 20L and $168,372 per year.

The average salary of a Business analyst in India is approx. INR 612,656 per year and in the US is approx. $70,489 per year. Based on experience, first-level Business Analyst earns around INR 363,813 and $ 60,055 per year, while proficient workers make up to INR 1,284,643 and $90,431 per year.
 

Final Thoughts

Provided the recent advancements, both can expect a drastic transition in analyzing method data. With the significant growth in big data, organizations will have the chance to explore a wide range of data and assist the management make vital decisions.

This is not just from the financial side but also from the customer demands, geography, etc., contributing to company expansion. In addition to the data and expected trends, a vital factor is skill learning. Both offer workforces numerous scopes to learn and boost themselves. Learning is a crucial factor in keeping up with the recent innovations or developments.

With augmenting data and learning trends, Business Analytics and Data Science opportunities can be considered an enormous trend.
 

About Us

iCert Global is a one-stop solution offering certification training courses in a wide variety of techniques that will give you a head start in this competitive world. Visit our website to find out the different technology courses.

https://www.icertglobal.com/ 

Our company conducts both Instructor-led Live Online Training sessions and Instructor-led Classroom training workshops for learners across the globe.

We also provide Corporate Training for enterprise workforce development

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• Data Science with R Programming Certification
• Apache Kafka Certification
• Power BI Certification

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When it comes to expanding your ventures, the main focus must be efficiency enhancement. Though ineffectual circumventions can work for small-scale businesses, when it is left unresolved, then inefficiencies will stack up, resulting in inflated costs.

Organizations understand the significance of the automation process and adopt it to make their finance department more efficient. Robotic Process Automation (RPA) is an excellent enabler for digital transformation, changing how humans and machines operate in a better working environment.

The process is becoming a mainstay in a different array of industries. Business advisors and luminaries said the benefits RPA offers are driving business growth. However, there is a negative side to leveraging the method, especially when companies are not strategic about the projects they undertake.

As mentioned in the title, we shall discuss the pros and cons of leveraging RPA in a business along with RPA worth and sustainability. Let’s not waste any time further and jump into the topics.
 

Is RPA Worth it?

RPA is a growing technology with wide-range advantages, and executing the process for your company can take a significant investment. Before adding automation to your business prototype, we must understand the objective of automation or what the process is trying to attain.

Long-run results need a more involved method, whether you are seeking for finance process upgrade, automating accounting, managing incoming emails, enhancing customer services and more.

When done perfectly, RPA can enhance efficacy by eradicating the probability of human error and increasing the task's swiftness. However, when planning and data insights go the other way around, automation can be a complete failure for your business and quickly become a sunk cost.

The point here is RPA is one of the best ways to enhance efficacy if leveraged right; hence you must ensure where its potential becomes useful. Look for factors that affect your gains within the business prototype.
 

Is RPA Sustainable?

RPA is a digital application that automates verification, data entry and other similar works. It has been around for over a decade; hence we could say it's a well-established technology. 

It's a flexible technology, having several applications across various industry verticals - works mainly as an interface on top of current solutions to imitate the user's tasks. 

For instance, an RPA robot can read, scan, and send PDF file data to manage inventory systems and supply chains.
 

Positive Sides of RPA Technology

Some of the pros of the automation process are listed below:

1. Easy to Use

RPA doesn't need excellent IT skills or insight on programming and coding. The software is user-friendly, easy to use and easy to understand. The tool allows us to develop robots seamlessly by capturing keystrokes and mouse clicks with an in-built screen recorder component.

Some of the software includes editing and creating robots manually by leveraging the Task Editor.
 

2. Debugging

When considering the developmental phase, the most significant advantage of RPA is debugging. Some tools are required to pause running while the process is being changed and replicated. The rest of its tools permit dynamic interaction while debugging.

Debugging lets, developers validate different situations by augmenting the variable values without pausing the running process. This approach permits seamless developments and resolutions in the production phase without prerequisite transformations to the procedure.
 

3. No Coding Knowledge Required

RPA technology does not need any programming or coding insight. The modern tools are leveraged to automate applications at any company level where the clerical task is executed across an organization. 

Hence, the workforce only requires training in RPA and can quickly develop robots using GUI and different intuitive wizards. This enables swift delivery of enterprise applications, giving it a cutting-edge advantage over the conventional automation approaches.
 

4. Security

When a company runs on RPA, more users demand its product access. Hence, it is crucial to have robust access management features. The tools offer options to allot role-based security abilities to ensure specific permissions. 

To eradicate any malevolent changes, the entire automated instructions, data and audits accessed by the robots are encrypted. RPA tools also provide detailed statistics of user logins, their actions and each performed task - ensuring internal security and compliance with company regulations.
 

5. Improve Decision-making

RPA feature refers to the ability to acquire and apply knowledge as skills. Bots first obtain the data, convert it into information, and change it into actionable details for users. 

AI and Cognitive Intelligence are the most common methods of RPA solutions that assist robots in enhancing decision-making.
 

6. Prevent Disruption

One of IT's significant challenges is the complex transformation procedures that limit tech giants from replacing, redesigning or improving the running process. But with RPA, the transformation process has been seamless and streamlined.

The software bots follow the existing quality, data integrity and security standards to access the system, thus preventing disruption of any kind and maintaining protections and functionality.
 

7. Analytical Suite

RPA software contains an integrated analytical suite that assesses the bot’s workflow performance. The suite assists in managing the bot’s functions from a central console - offering basic metrics, workflow and many more.

The analysis executed by the suite aids users in tracking and determining the operational problems.
 

8. Hosting & Implementation Options

The automation system offers deployment options across cloud, virtual machines (VM) and terminal services. Cloud implementation is one of the best options that captivate users because of its flexibility and scalability. 

Hence enterprises can install RPA tools on desktops and implement them on servers to access data for iteration task completion. The systems can automatically implement bots in groups, where they can run different tasks while processing a colossal amount of data.
 

Negative Sides of RPA Technology

Some of the cons of the RPA technology are listed below:

1. Long-run Sustainability

RPA can be a serious inveigle from the necessary long-run task required to digitize and make processes and administrative tasks more efficient. There is a risk that you may target swift fixes rather than doing it in the correct method from the initial phase.
 

2. Process Selection

It is always best to select rule-based tasks and do not need human judgment. The non-standard procedures are tiresome to automate, and human interaction is necessary for process completion. Hence, there are limited tasks that you can automate with RPA.
 

3. Initial Investment Costs

RPA is still in its transformation phase, and so it can present challenges that might result in undesirable consequences. So, we could say that it isn't easy for companies to decide whether they should invest in automation or wait for it to fully develop.

When thinking of executing RPA technology, a broad business case must be developed; else, it will be futile if returns are only marginal.
 

4. Hiring Skillsets

Several companies think that staff must have significant automation knowledge when working with RPA, as bots might need programming skills & awareness on operating them. It further forces companies to recruit skill-sets or train the existing workforce to meet the criteria.

An automation firm can be a little of a help during initial installation and set-up. But skill-sets can only adapt and control the bots in the long run.
 

5. Potential Job Losses

If a bot can complete numerous tasks at a swift turnaround time, then it is assumed that there will be no need for human operations. It is the primary concern employees have, resulting in a more significant threat to the labour market.

But this is not correct. Amazon, a leading tech giant, has been an excellent example of this concern. The employment rate had a drastic growth when they have increased the number of bots from 1K to over 5K.