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?

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