'How to become a data scientist'- The most searched question from the last five years. The scopes of a data science (DS) career are kissing the sky. At the same time, the non- DS fields are losing their importance. Starting from HR to BFSI, even the transportation and manufacturing industries are now adapting the AI innovations in lightning speeds.

Certainly, old school non-AI and DS jobs are under huge threat. So, the craze of becoming a data scientist, or at least a promising data analyst, is quite justifiable.

Every year, tons of newly certified data scientists step into the data science talent pool. But is this so easy to become a data scientist!!!

This is a crore-priced question…

Plenty of institutes like Learnbay, Simplilearn, Great Lake, UpGrad offer DS courses as well as certificates. but…

Getting certified does not mean you have become a data scientist. Your motto should be becoming a demanding data scientist; only then you can get settled. Otherwise, your career can ruin.

You will find so many answers with the steps of becoming a data scientist. So, planning a learning path is not that hard, but getting into the right plan and properly executing it is hard. And most importantly, staying away from any kind of deviations (like lucrative University degrees, lightning job guarantees, etc.).

Getting certified does not mean you have become a data scientist. Your motto should be becoming a demanding data scientist; only then you can get settled. Otherwise, your career can ruin.

You will find so many answers with the steps of becoming a data scientist. So, planning a learning path is not that hard, but getting into the right plan and properly executing it is hard. And most importantly, staying away from any kind of deviations (like lucrative University degrees, lightning job guarantees, etc.).

To stay away from any destruction and to identify the best fit as well as an industry-proficient learning plan, first of all, you need to bust all the myths.


Believing in a randomly advertised myth is the best mistake that most DS aspirants make.


Myth buster 1: You don’t need a Data Science Degree to get recognition as a promising Data Scientist.

Yes, a DS degree is needed. Rather. Talent acquisition teams of top MNCs and FAANG companies keep their eyes on the data science talents with post-graduate and even PhD digress in disciplines like Statistics, physics, economics etc. Masters degree holders in computer science also enjoy a good degree of preference in this regard. The reason will be cleared with the other burying myth, which I will list in this answer.

Myth buster 2: The IT experience is not the best ever advantage to become a data scientist.

No doubt, most of the top-rated data scientists own an MCA or any other computer-related higher studies (22%), but business and economic background PhD holders are competing very hard (21%). The most shocking statistic is that engineering background owns the percentage level of only 9% in this regard. (source: Big Data Made simple). Even statistics and different hard natural sciences backgrounds hold greater percentages than the IT/Engineering one (16% and 11%, respectively).

So, IT expertise is not the mandatory criterion.

Myth buster 3: Not the Programming skills, rather well-balanced industrial knowledge and statistical proficiency is the key to success

I agree programming is needed, but that becomes the secondary skill in the core data scientist role. You need to be a ninja in the statistical approach of computer science along with the master level proficiency in amalgamating the same with Industry-specific business practices. For statistical skill achievement. You need to dive deeper into the approaches like correlational calculus, advanced level matrix, permutation and combination, probability theories,

Studying data science with domain specialisation is the magic key to a successful data scientist career. 

Until you gain an in-depth idea about the business practices, targeted outcomes and customer demands of a specific domain, you can't provide a profitable data-driven solution.

Myth buster 4: Eye-catchy Data Science Certificates do not direct you to a data science career transition success- it’s your project that plays behind the scene

Becoming a data scientist needs an amazing level of project experience. Even though you are attending the interviews for your very first data science job, your project portfolio should be strong enough, and the outcomes of the same must address at least one of your domain trending BI problems. Completing a domain-specific project that offers future proof business problem solutions is an important benchmark for becoming a successful data scientist.

For freshers, internships can be the alternatives as they lack domain experience as well as the professional knowledge for doing industry-grade capstone projects.

Myth buster 5: Python is not everything for DS

Becoming a data scientist can be managed with python if you have your own core statistical expertise, but growing as a data scientist needs knowledge of another programming language too, such as scala, R, C++ etc. Please don't get confused. As told earlier, you need basic knowledge of these programming languages, and becoming a ninja is not needed. As you approach the higher position in DS, you need to work with different coding languages. Even after these, you need to learn database management also. SQL is an important skill that every single thriving data scientist holds.

Myth buster 6: Study of core ML is not required to become a data scientist.

As a data scientist, you need to work on data filtering, wrangling, proper business decision making, monitoring the data-driven automated applications and software, etc. And all such applications run on ML, so until you have adequate knowledge in ML modelling and designing, you can't find or identify the best solution. So, even though you are not targeting ML expert roles, still you need to acquire fair ML knowledge, especially the concepts behind the most used ML algorithms like Linear and logistic regressions, SVM, XGboosts, etc.

Myth Buster 7: You need job assistance even though you have long years of work experience

Although you will not switch the domain, it will just upgrade your career, still, need proper job assistance. Becoming cracking a data science interview is not like cracking normal technical role interviews. You need to develop DS oriented soft skills like communication, critical thinking, business acumen, passion for huge data assessment, etc. You need proper grooming in this regard.


I hope the above myths have already made you realise which type of learning content you should focus on or which type, of course, should you choose. . Below is a comprehensive, sequential learning plan for your further help.


Self-access to know if DS actually suits you or not? Professional counselling seems to be the best option in this regard.

First, brush up on your school level math, then gradually head toward the core DS statistics.

Learn the basics of programming (if you are a novice in this case). It’s best to take part in hackathons to hone your programming skills.

Invest a good time in database management studies and tools like SQL, MySQL. Give this a priority just after the statistical learning.

And yes, whatever you learn, learn from your domain expertise. Suppose you are in the predictive analytics session. You must learn the best practices of predictive analytics in your domain (like marketing, manufacturing).

Provide hardcore focus on DS project building but keep in mind it must benefit your domain.

Share your knowledge with others, earn knowledge from them. Be active in DS communities, attend seminars because continuous learning is a must to sustain in the DS field.

And yes, in the end, I must suggest taking an instructor-led course but only the one that will make you industry-ready. But choose only full-stack courses with proper learning support and job assistance.


You can have a look at the IBM accredited Learnbay data science courses that can offer an end-to-end guide to get matured as a demanding data scientist. The features that make Learnbay courses so promising are as follows:

Higher focus on building core knowledge, not on the trending tools practices. The submodules are planned so well that you learn the concept playing behind the DS and AI tools. So, no need to worry if the tools get changed tomorrow. Your demand will be the same as a data scientist.

The learning environment is so personalised that it treats every single weakness in you as a student. Different courses are there as per your work experiences and so the learning modules. Small batch and live, interactive classes ensure each student gets 1 to 1 attention. Even non-programmer get additional basic programming classes free of cost. With three years of flexible subscriptions, you can switch over batches and instructors.

The job assistance is highly dedicated to students' needs. Remain live until you get a job. It includes mock interview support based on tagged companies and job roles. Apart from your resume assistance, project portfolio maintenance, etc.

Project expertise is the added benefit of Learnbay courses. Forget about practice capstone projects. With the Learnbay course, you can do fresh projects with your own ideas. Experts help you execute your ideas.

Whatever you learn is from hands-on experience- The best way of studying industry-grade data science. Apart from two capstone projects, there are 15+ live MNC industrial projects (domain-specific). Via cloud lab access offered by Learnbay, you can see how FAANG companies are handling data in these projects. This also makes you prepare for your own project.

Usually, DS courses cost approx 2 to 3 lakh when it becomes full-stack, but Learnbay courses are available at the pocket-friendly option of 57,000 to 79,000 INR investment. This is another reason to suggest this course.


And lastly, if you own the ultimate passion for investigating piles of data, no one can resist you to become a top-notch data scientist.