How to start your career in Data Science?

I can, I can, I can be a data scientist

“Hey bro, I am working in the field of Human Resource. Can I pursue my career in the field of Data Science or it is not for me?”

“Hello sir, I have 5 years of experience in software development, but I want to break into the path of data science. Is it still possible?”

“I have graduated recently sir and want to start my career in the field of data science. How to do it?”

And many more like this…

The questions might come from a diverse background, knocking on the door of data science, but the answer is straightforward. A BIG YES….

But HOW??

This article is all about answering this question.

But before answering this question, I will tell you the common mistakes which most of the data science freshers commit and regret later.

  1. Deep diving into theory — It will not only slow you down, but you might lose interest in data science. In addition, you don’t remember deep theory for long unless you are not revising it regularly. So, you should create a balance between theory and practical.
  2. Taking the complex popular problem at the beginning — “Facial recognition problem looks so cool, why not hit it first”. It will not only exhaust you but your naive approach without understanding fundamentals will make you out of focus. Hold your horses. Stay Calm and go for a systematic study. It will make your base stronger for complex problems.
  3. Juggle between programming language — “Let’s learn Python as well as R. I heard both are in high demand in the job market”. It might be true. But if you put your legs on two boats, you will eventually fall. Even in the corporate world, data scientists generally focus on training themselves in one particular language and try to excel in it. Another language, if required, could be learned easily if one is an expert in one of the data science programming languages.
  4. Who cares about Statistics — “Just code, why to study Statistics”. Without understanding the logic behind machine learning, simply coding will make you a developer, not a data science practitioner. The foundation of all machine learning lies here. Don’t underestimate the power of common statistics. It helps you understand different machine learning algorithms, as well as important during the interview.
  5. Communication skill!!, I am not a manager— Not only Analytics Manager, but from junior to senior, everyone in the data science world should be good at communication. A good data science practitioner knows how to tell a good story on the data. So, it is not only about winning the Kaggle competition but describing it clearly so that any naive or non-data science person can also easily understand your result.

So, let’s focus on how to start your career in data science.

  1. Understand data science— Talk to multiple people working in the field of data science and before diving into the data science pool, try to understand what it is. Also, there are tons of materials available on the internet. But it will dilute your thoughts and might confuse you more. A good mentor in data science is a boon.
  2. What do you want to do? — Try to understand which role will suit you the most based on your previous work experience or knowledge. There are multiple ways one can look at data science. For example, a data engineer helps in creating data pipelines for efficient data transportation, while a data scientist uses the data to create a machine learning model. For details
  3. Step1 with stats — Take some basic courses of Probability and Statistics, to create a stronger base for a greater understanding of data science.
  4. Courses — There are a large number of courses in data science for all the roles. Udemy, Coursera, Edx, LinkedIn are good sources of data science courses. If you are getting dragged, approach the mentors in the respective field who can help you in creating a study plan. Try to choose a course with a good review. But the most important point is to complete the course without too much lag. The more the lag, the difficult to understand the concepts and obviously it will take a much longer time to cover up.
  5. Languages — Choose one language and stick to it. If the language is familiar to you, nothing could be better. But if it is not, your perseverance and diligence in that language will make you comfortable soon. Python is in much higher demand in the present times. If you are not able to judge any of the languages, go for Python.
  6. Competition/Practical Work — As Einstein told “In theory, theory and practice are the same, but in practice, they are not.” After learning theory, you have to practice your theoretical knowledge on numerous online data science competition platform. Kaggle, Analytics Vidhya DataHack, and many more are good for practicing data science knowledge. For algorithmic coding, one could use HackerEarth and Leetcode.
  7. Certifications — Let your resume shine with certifications. But doing certifications with Udemy, LinkedIn, and other good learning sites will only help in building a good understanding of the subject, however, it doesn’t add so much value as Microsoft Azure, AWS or Google Cloud certifications will carry. Even if you are starting your career with Data Science, you could go for Azure, AWS, GCP certifications as the preparation of these certificates involve basics and advanced concepts. It doesn’t only help in getting good recognition but also understanding concepts in a structured manner.
  8. Open Source — Don’t trust every other resource you got on the internet. Some reliable sources like Medium, Analytics Vidhya, ListenData, towards data science, KDnuggets, MachineLearningMastry, and many more. Also, on youtube some good videos are available. But try to follow some good vloggers like Brandon Foltz, Ken Jee, Lex Fridman, Andrew NG, Jordan Harrod, and many more. There is also a list of data scientists whom you could follow:
  9. Communication skill — While learning, try to be specific to understand what is the problem business is facing and how your analytical solution will help the business to solve the issue. You should be good at storytelling related to data. So, data visualization and telling a story based on that data would not only help the client/audience to understand your view more clearly but help you to project yourself as a pure Data Scientist. Clear view and clear communication is the key.


If you go for any jobs portal, it is colored with multi-level jobs in data science. So, there is a crazy level of opportunity currently in this field. The distance to start a career in data science is only that far, you start taking the first step towards it. With a ray of hope and belief, with a sense of working hard, with the sense of learning more, with a determination to move in the right direction with a unidirectional focus mindset, one can become a good data science practitioner. The article has been initiated with the point to not let you take some common mistakes. The remaining part of this article created guidelines for anyone who wants to start their career in data science.

If you have some more opinions, or you have rowed the same boat of data science with some more or different opinions as been told in this blog, please do let me know in the comments.

Senior Data Scientist in TCS, more than 10 years experience in Data Science, Top 20 Data Science Academician in 2018.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store