Things You Should know Before Starting Your Career as a Data Scientist

It can be intimidating to learn data science. This is especially true when you are just starting out on your journey. Which tool should you learn, R or Python? What techniques should you concentrate on? How many statistics do you need to learn? Is it necessary for you to learn to code? These are just a few of the many questions you'll have to answer along the way.

That is why you decided to write this guide to assist people who are just starting out in Analytics or Data Science. The goal was to create a short, simple guide that would set you on the path to learning data science. This guide will lay the groundwork for you to learn data science during this difficult and intimidating time.
 

What is a Data Scientist?

A data scientist uses data to understand and explain phenomena in their environment and to assist organisations in making better decisions.

Working as a data scientist can be intellectually stimulating, analytically satisfying, and put you at the forefront of technological advances. 

18 Tips you should know before starting your career as a Data Scientist:

Data scientists are becoming more common and in demand as big data becomes more important in how organisations make decisions. Here's a closer look at what they are and what they do—as well as how to become one.

1. Proper Education is the key -
   Professionals with a high level of education are data scientists. Around 75% of them have a Ph.D. or a Master's degree. However, you are not required to have a bachelor's degree from a reputable university. 25% of data scientists have a degree from an 'unranked university.' Allow me to make it easier for you. The vast majority of data scientists hold degrees in computer science, statistics, mathematics, engineering, or social sciences. Only 13% of them have completed a university programme in data science and analysis. 
   What you all need is a quantitative background, and there are plenty of options in that department. As a result, there is no need to enrol in additional academic programmes to acquire the necessary skills. There are numerous online courses available to assist you in improving your skills. Online courses are taken by approximately 40% of data scientists.
    
2. Choose a perfect role for yourself -
   In the data science industry, there are numerous roles to choose from. A data visualisation expert, a machine learning expert, a data scientist, a data engineer, and so on are just a few of the many roles available to you. Depending on your background and work experience, one role may be easier to obtain than another. For example, if you are a software developer, you could easily transition into data engineering. As a result, until and unless you are clear about what you want to become, you will be confused about the path to take and skills to develop. 

What should you do if you are unsure about the distinctions or what you should become? I'd like to suggest a few things:

1. Speak with people in the industry to learn more about each of the roles.
2. Take on mentorship from others – ask them for a short period of time and ask pertinent questions. No one, I'm sure, would refuse to assist someone in need!
3. Determine what you want and what you are good at, and then choose a role that corresponds to your field of study.
    

3. Choose a language or a tool and use that only -
   As I previously stated, it is critical that you gain hands-on experience with whatever topic you choose. A difficult question that arises when getting hands-on is which language/tool to use. This is most likely the most frequently asked question by newcomers. The most obvious answer is to begin your data science journey with any of the mainstream tools/languages available. After all, tools are only a means to an end; understanding the concept is more important. Still, the question is, which is a better option to begin with? There are numerous guides/discussions on the internet that address this specific question. The basic idea is to begin with the simplest language or one with which you are most familiar. If you are not well versed in coding, you should prefer GUI-based tools for the time being. Then, once you've mastered the concepts, you can get your hands dirty with the coding.
    
4. Take a course and complete that -
   Now that you've decided on a role, the next logical step is to devote time and effort to learning about it. This entails more than simply reviewing the role's requirements. Because there is a high demand for data scientists, there are thousands of courses and studies available to help you learn whatever you want. Finding material to learn from isn't difficult, but learning can be if you don't put forth the effort. The issue is not whether the course is free or paid; rather, the main goal should be whether the course clears your basics and brings you to a suitable level from which you can progress further. 
   When you enrol in a course, make an effort to complete it. Follow the coursework, assignments, and all of the course-related discussions. If you want to be a machine learning engineer, for example, you can read Machine learning by Andrew Ng. You must now diligently follow all of the course material provided. This includes the course assignments, which are just as important as watching the videos. Only completing a course from beginning to end will provide you with a more complete picture of the field.
    
5. Join a group to get information or updates -
   Now that you've determined which role you want to pursue and are preparing for it, the next step is to join a peer group. What is the significance of this? This is due to the fact that having a peer group keeps you motivated. Taking up a new field can be intimidating when done alone, but with friends by your side, the task seems a little less daunting. 
   The best way to be in a peer group is to have a group of people with whom you can physically interact. Otherwise, you can connect with a group of people on the internet who have similar goals, such as enrolling in a Massive online course and interacting with your classmates.
    
6. Not just the theoretical part, focus on the practical part also -
   While taking courses and training, you should concentrate on the practical applications of what you're learning. This will not only help you understand the concept but will also give you a better understanding of how it would be applied in practise. Here are a few things you should do if you are taking a course:

• To understand the applications, make sure you complete all of the exercises and assignments.
• Work on a few open data sets and put your knowledge to use. Understand the assumptions, what the technique does, and how to interpret the results even if you don't understand the math behind it at first. You can always gain a more in-depth understanding later on.
• Examine the solutions proposed by people who have worked in the field. They'd be able to find you faster with the right approach.
   

7. Always follow correct resources -
   To never stop learning, you must immerse yourself in every source of information you can find. Blogs run by the most influential Data Scientists are the most useful source of this information. These Data Scientists are very active and frequently update their followers on their findings and post about recent advancements in this field. Every day, read about data science and make it a habit to stay up to date on the latest developments. However, there may be many resources and influential data scientists to follow, and you must be careful not to follow the wrong practises. As a result, it is critical to use the appropriate resources.
    
8. Build a network but don’t waste much time on that -
   At first, your sole focus should be on learning. Doing too many things in the beginning will eventually lead to you giving up.
   Once you've gotten a feel for the field, you can progress to attending industry events and conferences, popular meetups in your area, and participating in hackathons in your area – even if you only know a little. You never know who, when, or where will come to your aid! Actually, a meetup is extremely beneficial when it comes to making your mark in the data science community. You get to meet people in your area who are actively working in the field, which provides you with networking opportunities as well as establishing a relationship with them, which will help you advance your career significantly. A potential networking contact could:

• Provide you with insider information about what's going on in your field of interest, as well as mentorship support
• Assist you in your job search by providing either tips on job hunting through leads or direct employment opportunities.
   

9. Work on your communication skills -
   People rarely associate communication skills with rejection in data science positions. They believe that if they are technically superior, they will ace the interview. This is, in fact, a myth. Have you ever been turned down during an interview because the interviewer said thank you after hearing your introduction?
   Try this activity once; have a friend with good communication skills listen to your introduction and provide honest feedback. He'll undoubtedly show you the mirror! When working in the field, communication skills become even more important. You should be able to communicate effectively in order to share your ideas with a colleague or to make your point in a meeting.
    
10. Basic Database and Knowledge is important -
    Data does not appear in the form of tables by magic. Beginners typically begin their machine learning journey with data in the form of a CSV or an excel file. But something is unmistakably missing! It's a SQL query. It is the most fundamental skill for a data scientist.
    Because organisations are still figuring out their data science requirements, knowing data storage techniques as well as the fundamentals of big data will make you far more appealing than someone with hi-fi words on their resume. These organisations are looking for SQL professionals who can assist them with their day-to-day tasks.
     
11. Model Deployment is your secret ingredient -
    Many beginner-level data science roadmaps do not even include Model Deployment, which is a recipe for disaster. Once you have completed the data science project, it is time for the intended user/stakeholder to reap the benefits of your machine learning model's predictive power. In a nutshell, this is model deployment. This is one of the most important steps in business, but it is also one of the least taught. Let's look at an example. An insurance company has launched a data science project that uses accident vehicle images to assess the extent of the damage.
    The data science team works around the clock to create a model with a near-perfect F1 score. They have the model ready after months of hard work, and the stakeholders are pleased with its performance, but what happens next? Remember that the end-user in this case is the insurance agent, and that this model must be used by multiple people who are NOT data scientists at the same time. As a result, they will not be running Jupyter or Colab notebooks on GPUs. This is where a complete model deployment process is required.
    This task is typically performed by machine learning engineers, but it varies depending on the organisation in which you work. Even if it is not a job requirement at your company, it is critical to understand the fundamentals of model deployment and why it is necessary.
     
12. Keep Practicing -
    As we all know, the only constant is change. Artificial intelligence and machine learning are evolving at a rapid pace and are not static. We cannot compare the current situation to that of a few years ago. To stay in the rat race, it is critical to keep learning with the advancement of technology. There are numerous ways to improve your skills, including online data science courses, conferences, and many others. To learn how to apply data science to problems, you should practise problem-solving and coding as much as possible.
     
13. Maintain your resume -
    Let's solve a riddle here: What is the first thing the recruiter notices about you that could be your last? This is your resume! These are the ultimate challenges that you must overcome in order to obtain the most coveted job! Make sure to include these suggestions in your next resume –

• Prioritize skills based on the job role available.
• Mention data science projects to demonstrate your abilities.
• Don't forget to include a link to your GitHub profile.
• Certifications are less important than skills.
• Update your skills and projects at the same time, not just once in a while.
• The overall appearance of your resume is important; ensure that all of your fonts and formatting are consistent throughout.
   

14. Proper Guidance is important -
    Coming to the final and perhaps most important point – finding the right guidance. Data Science and machine learning, as well as data engineering, are relatively new fields, as are their alumni. In this field, only a few people have decrypted their path. There are many ways to become a data scientist. The most straightforward is to pay lakhs of rupees for a recognised certification only to become frustrated with the recorded videos or even follow along with a YouTube playlist and still be an industry-ready professional.
     
15. Understand Business Problems And Be Competitive -
    To become a good data scientist, you should always be curious and ask questions whenever there is a doubt, which not only improves communication among coworkers but also helps you become a good analyst. In order to solve business problems in an organisation, it is also critical to understand business metrics and other statistical issues.
     
16. Add different skills -
    Problem-solving is a fundamental component of data science that aids in the division of large business problems into smaller, more manageable ones. Large organisations frequently seek data science specialists with in-depth knowledge in a specific area. However, if you have multiple skill sets rather than a specialised area that can be beneficial to your organisation, no one can stop you from moving forward in your career. Also, some organisations may require additional skills in addition to your knowledge of data science; in this case, having additional skills will help you advance in your career.
     
17. Start with an entry-level position -
    Being a data scientist is a difficult job that requires extensive analytical skills as well as problem-solving experience. If you want to advance in your career as a data scientist, the best place to start is as an intern, as this will prepare you to face many unknown challenges. Another advantage is that as an intern, you will receive assistance wherever you are stuck, and your coworkers will provide you with numerous pieces of advice based on their own experiences, which will help you advance in your career.
     
18. Prepare for your interviews -
    Once you've landed an interview, prepare responses to common interview questions.
    Because data scientist positions can be highly technical, you may be asked both technical and behavioural questions. Anticipate both and practise your response aloud. Having examples from your previous work or academic experiences on hand can help you appear confident and knowledgeable to interviewers.

Here are a few examples of questions you might encounter:

• What are the advantages and disadvantages of a linear model?
• What exactly is a random forest?
• To find all duplicates in a data set, how would you use SQL?
• Describe your machine learning experience.
• Give an example of a time when you didn't know how to solve a problem. What exactly did you do?
   

What exactly does a data scientist do?

On a daily basis, a data scientist may perform the following tasks:

• Discover patterns and trends in datasets to gain insights.
• Develop algorithms and data models to predict outcomes.
• Use machine learning techniques to improve data quality or product offerings.
• Distribute recommendations to other teams and senior management.
• In data analysis, use data tools such as Python, R, SAS, or SQL.
• Keep up with the latest developments in the field of data science.

Best Data Science Jobs for you

Because Data Scientists' work touches so many different industries and disciplines, the roles Data Scientists can fill are known by a variety of names, including:

• Data Scientist
• Data Analyst
• Researcher
• Business Analyst
• Data Engineer
• Data Architect
• Machine Learning Engineer
• Quantitative Analyst
• Data and Analytics Manager

There are numerous other variations, and these will continue to evolve as data science becomes more widely used. While the list of job titles in data science may appear to be endless, there are four major categories that describe the various roles that Data Scientists most commonly fill. The good news is that nearly all of these jobs are in high demand. If you have data science skills and experience, you are already in a good position for career development and advancement.
 

Salary of a Data Scientist and their job growth in industry

As of March 2021, the average salary for a data scientist in the United States is $113,396. According to the US Bureau of Labor Statistics, demand for data professionals is high, with data scientists and mathematical science occupations expected to grow by 31% and statisticians by 35% between 2019 and 2029. (BLS). This is significantly faster than the overall job growth rate of 3.7 percent.

The rise of big data and its increasing importance to businesses and other organisations has been linked to the high demand.
 

Final thoughts

Data scientists are in high demand, and employers are investing significant time and money in them. As a result, taking the right steps will result in exponential growth. This guide will give you some pointers to get you started and keep you from making costly mistakes.

If you've had a similar experience in the past and want to share it with the community, please leave a comment below!
 

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