How to kickstart your Data Science Career?

Business People meeting Planning Strategy Analysis on new business project Concept

Have you been looking to launch your Data Science Career?

We’re sure you have done your research and are ready to take the plunge into Data Science.

If that fits your description, we have for you the Step-by-Step Guide to Becoming a Data Scientist!

Data Scientist: A closer look

Okay, so who really is a Data Scientist?

Most newbies think that anyone having ANYTHING to do with data is a Data Scientist. That’s almost like anything having wheels is a car or anyone who can cook is a gourmet chef!

Let us help clear the air around a Data Scientist!

Think of Data Scientist as someone who analyzes and interprets complicated data. This analysis is based on a tactical approach adopted by the Data Scientist after adjusting for:

  • the needs of the business
  • the input data available 
  • the desired nature of outcome.

A Data Scientist also looks for ways to identify and solve similar problems in the future with the least human intervention. This is how a Data Scientist saves the day. 

A competent Data Scientist should learn to strike a balance between:

  • being technically sound with plenty of knowledge about the various tools
  • having a thorough understanding of statistical models and distributions
  • exploring analytical techniques like machine learning and deep learning
  • innovating and imagining solutions that have far-reaching impacts on the future

Now that you understand what a Data Scientist does, it’s time to check whether you have what it takes to keep up with a Data Science career.

Data Scientist Aptitude Test

Ask yourself these questions!

  • Do you have an understanding of the Data Science and industry trends?
  • Do you build on this understanding by extensively reading the news, blogs, books, watching videos and listening to podcasts?
  • Do you know-how industries are using Data Science to their advantage?
  • Do you understand how you fit into a Data Science career? Can your skills be polished and matched to a Data Scientist?
  • Do you have the appetite to constantly learn new concepts and techniques?
  • The industry is fast evolving with newer breakthroughs made every hour. Are you ready to keep up?
  • Can you place data-driven techniques in a business context?
Data scientist vs data engineer vs data analyst


If you didn’t scare easy from that test, you’re ready to grab a Data Science career by its horns!

Step up your game with this step-by-step guide:

1. READ, READ, READ! We cannot stress this enough!

Seriously, read!

You need to explore a bunch of different fields like statistics, IT, and machine learning. We never said it’s easy, but you can by dividing your focus among them.

Understand the scope of Data Science! Read about how companies are applying newer techniques to improve business every day.

Go the extra mile….

Understand how they progressed from a concept on paper to a wide-scale application.

We guarantee that mugging up algorithms and concepts won’t get you far enough.

You cannot thrive on rote learning! This is why we’re stressing on backing up all you read with practical applications.

2. Choose the right tool

Do you know which programming tools are making waves?

As a Data Scientist, you should be able to compare and choose the best fit in different scenarios. Start by reading our previous article on the three hot programming languages- R, SAS and Python.

Python’s popularity has hit an all-time high. It’s easy to understand, code and adapt.

Python should be your top priority!

You would be required to learn how to code. Well, just a bit.

It’s common knowledge among Data Scientists that a good coder might not be a great Data Scientist, but a great Data Scientist is surely a good coder. 

3. Refresh your Math and Stats

What core concepts should be on your radar?

You should strengthen your hold on both mathematics and statistics. Many Data Scientists have a background in statistics.

In your Data Science career, you will need some sense of how to best represent your data and outcomes.

You will have to deal with matrices and linear algebra. Data Science also uses a lot of descriptive and inferential statistics.

A lot of applications of Data Science are into predicting success or failure of new products, target customer base, policies and techniques.

You might be better equipped to handle such questions once you brush up on probability.

math for a data scientist

4. Focus on Machine Learning

Machine learning is enabling a machine to learn and develop on its own.

Data science and machine learning go hand in hand. The way machines learn is when they are fed input data. Machine learning depends on the data and algorithms fed.

As such, Machine Learning will surface whenever we talk about Data Science.

Businesses focus on Machine Learning to help predict outcomes and make decisions.

As a Data Scientist, you should be comfortable with Machine Learning and stretch beyond theorizing it.

5. Befriend Deep Learning

As a Data Scientist, you should be super interested in Deep Learning.

Deep Learning is a subset of Machine Learning and is only in its infancy. It analyzes algorithms fed to a machine and improves on it on its own.

Deep Learning is how Facebook recognizes and tags friends in your photos immediately.

Deep Learning uses artificial neural networks to learn and gain decision-making abilities. Neural networks are designed to replicate how humans think.

It is being used popularly in image and facial recognition and chatbots.

6. Know your Databases

There is no Data Scientist who doesn’t work on databases.

Even if you have a Data Engineer in your kitty, a Data Scientist needs to know his databases.

Many organizations use database management systems such as MySQL, MongoDB, Cassandra, PostgreSQL. A working knowledge of these would greatly benefit your role as a Data Scientist.

7. Adding a business context

There is no point of sharpening your skills if you can’t implement them to solve a real-world problem.

The best change to bring to your approach is to apply some context to your knowledge.

Think from the perspective of a business. Be curious….

It is important to ask yourself why you’re working on something? Why did you opt for a particular approach? What are your options? How will this help your business? How can you make it better?

People will look to you for answers!

8. Build a network

Building a community builds on your power.

A little networking never hurt anyone! We’re sure many questions race in your mind as you continue to explore Data Science.

Believe us, a LOT of people have a LOT of questions here!

Building a team of peers ensures you get reliable answers for a lot of your queries.

Be it help with some technical issue or advice on how to advance your career, people who’ve lived your truth are better equipped to answer you.

While there is no dearth of meeting Data Science experts online, you should grow connections offline as well.

The Data Science career network

9. Challenge and compete

Reject the traditional approaches to learning…

Data Science is not traditional, so why should your learning techniques be?

One of the best ways to expose yourself to the practical regime of learning is competing with other Data Scientists.

Kaggle is a popular runway where many Data Science careers take off. Participate and compete in challenges on Kaggle.

Competition platforms for data scientists


10. Work on state-of-the-art problems

Keep up with the speedy evolution!

Reach out to peers to secure internships and projects to work on the latest and most relevant Data Science assignments.

Find real-time data dumps in your online community to work on.

Gaining hands-on knowledge on the practical aspects of Data Science will secure a better future for you and ease your learning curve.

11. Learn how to spin a yarn

Not literally, of course!

This is one of the most overlooked skills when you place a Data Scientist in the cross-hairs.

The ability of story-telling is what bridges the gap between your Data Analysis and your not so technically sound audience.

You should have relevant soft skills.

Being adept in the art of communication, ensures your analysis and findings are correctly shared with your audience.

12. Take your time to build a Data Science Career

Investing in a Data Science career takes time.

The above steps may have made this abundantly clear, Data Science is not an overnight plan. Don’t treat it like one…

Spend some energy in researching the field. This is not a mere certification to add to your profile. Focus on your long term goals when planning for it.

Visit blogs and web pages of Data Scientists that you like. Understand their Data Science career trajectory.

This ends our step-by-step guide that will ensure your success in a Data Science career. Remember what you’ve learned, a competent Data Scientist always follows through in real life.

So now that you’ve read about it, start working on these tips now.

All the best!

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