Becoming a Data Scientist with Online Education (Zero to hero)

10 Steps for people who want to pursue a career in Data Science.

Photo by Ben White on Unsplash

Everyone keeps talking about Data Science being the “sexiest job of the 21st century”. But is it …really?

NO!… (Sorry to disappoint)… It’s not sexy …if you’re not in love with “Data” and… not a “data-driven” decision-based person.

Data Science is a blend of maths, statistics and computer science. It also involves programming, database management and modelling. Without a good grounding in several of these you are unlikely to succeed, If you are giving up too easily.
Combination of three majors.

Step 1. Satisfy the necessities

Before you start, ask yourself this question

“Am I good with Maths? Specially Multivariable Calculus and Linear Algebra?”

If yes, BRAVO! If not, That’s the first thing you have to do today. If your math background is up to multivariable calculus and linear algebra, you’ll have enough background to understand almost all of the probability /statistics/ machine learning for the job.

Here are some useful links for you to follow:

You also need some programming background to begin. Programming is not as much hard like it sounds. It’s the coolest subject in the world to learn.

Love Coding :)

Personally, I prefer Python for Data Scientists. But why Python is so Important right? Check out..

How do I learn to Code?

You can learn to code in so many different ways now that we have something called the internet. (I’ll leave it to you)

Photo by Rajeshwar Bachu on Unsplash

If you came from a Development background such as Computer Science or Software Engineering, You do not need to worry about this at all!

How do I learn Python?

Here some (a bit long) list of resources:

Python Kindle Guide:

Why is Python a language of choice for data scientists?

Let’s measure the pros and cons.

More weight: Advantages!

Is Python the most important programming language to learn for aspiring data scientists and data miners?

Read this full answer on Quora. and this Kindle Book

After mastering python you can move into learning R as well since it’s widely used by Statisticians since R is powerful for dedicated Statistical tasks but Python is more versatile as it will connect you more to production level work.


Step 2. Plug Yourself Into the Community

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As I mentioned in my previous article, You are nothing without the network. Install Meetup and Eventbrite to see data science-related events near you and keep some time during your week to attend one or two networking events. learn about data science live, and meet data scientists and other aspirational data scientists.

If you really want to know the science behind networking and the importance of building official relationships, Read this book.

Also, Start reading data science blogs and following influential data scientists. Here’s one for example:

What are the best, insightful blogs about data, including how businesses are using data?

( This is important. Knowledge of How business is using data is a nice-have skill for a data scientist. )

Jeff Hammerbacher from Quora has answered to this question like no other can and I quote:

Popular Culture

Databases and Data Infrastructure

Here’s the best A-Z Ultimate Guide to Data Warehousing

Machine Learning and Data Mining

Data Visualization

Random Updates

What is your source of machine learning and data science news? Why?

Here are some data science news aggregators:

Step 3. Setup and learn to use your tools

Photo by Arif Riyanto on Unsplash


  • Install Python, iPython, and related libraries (guide)
  • Install Jupyter / Anaconda
  • Watch the above-mentioned tutorials and Python Data Science courses.


  • Install R and RStudio (It’s good to know both Python and R)
  • Learn R with swirl
  • RShiny
Data Camp is a platform where you can learn R and Python for Free that includes thousands of Tutorials and Training Courses

R and Python Collaboration

This is somewhere similar to “Beyond the Wall in Game of Thrones”, Everyone knows it exists but afraid to go to. But learning how to combine these two can give you a massive leverage.

ATOM Editor for Python/R


Step 4. Learn Probability and Statistics

Photo by Chris Liverani on Unsplash

Be sure to go through a course that involves heavy application in R or Python. Knowing probability and statistics will only really be helpful if you can implement what you learn.

Step 5. Complete Harvard’s Data Science Course

See the following link to start a course or two.

Step 6. Do all of Kaggle’s Getting Started and Playground Competitions

Photo by Dimon Blr on Unsplash

According to William Chen, A Data Scientist, and a Quantitative Researcher at Two Sigma, “I would NOT recommend doing any of the prize-money competitions. They usually have datasets that are too large, complicated, or annoying, and are not good for learning. The competitions are available at Competitions | Kaggle

Start by learning scikit-learn, playing around, reading through tutorials and forums on the competitions that you’re doing. Next, play around some more and check out the tutorials for Titanic: Machine Learning from Disaster for a binary classification task (with categorical variables, missing values, etc.)

Afterward, try some multi-class classification with Forest Cover Type Prediction. Now, try a regression task House Prices: Advanced Regression Techniques. Try out some natural language processing with Quora Question Pairs | Kaggle. Finally, try out any of the other knowledge-based competitions that interest you!

Step 7. Learn Some Data Science Electives

Data science is an incredibly large and interdisciplinary field, and different jobs will require different skillsets. Here are some of the more common ones:

Step 8. Do a Capstone Product / Side Project

Photo by Annie Spratt on Unsplash

Use your new data science and software engineering skills to build something that will make other people say wow! This can be a website, new way of looking at a dataset, cool visualization, or anything!

Create public github repositories, make a blog, and post your work, side projects, Kaggle solutions, insights, and thoughts! This helps you gain visibility, build a portfolio for your resume, and connect with other people working on the same tasks.

Step 9. Get a Data Science Internship or Job

Photo by Sebastian Herrmann on Unsplash

Check out The Official Quora Data Science FAQ for more discussion on internships, jobs, and data science interview processes! The data science FAQ also links to more specific versions of this question, like How do I become a data scientist without a Ph.D.? or the counterpart, How do I become a data scientist as a Ph.D. student?

Also, you can follow the steps in this article which was written by me.

Step 10. Share your Wisdom Back with the Data Science Community

Photo by Alfons Morales on Unsplash

If you’ve made it this far, congratulations on becoming a data scientist! I’d encourage you to share your knowledge and what you’ve learned back with the data science community. Data Science as a nascent field depends on knowledge-sharing!

All these resources are thanks to William Chen and Google.

Thank you!

Keen to know more about me? Check out my website and hit me back if you have any questions regarding my articles.

Keen on learning about me and my work? Click here.

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