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18 Most Recommended Data Science Platforms, To Learn Python and SQL

A comprehensive guide to the most popular data science learning platforms by the community

Nathan Rosidi

Data science is one of the hottest careers in today’s market. Companies are always hiring data scientists and there’s always a large number of people trying to become one. But data science hasn’t been around for as long as other technical disciplines, so unlike software development which has, there aren’t as many data science-specific learning platforms to help prepare aspiring and experienced scientists. There’s of course Coursera and Udemy, then a few big brand name platforms like DataCamp. Then there are a dozen or more smaller niche platforms aimed at training data scientists their way.

I’ve evaluated 18 platforms recommended by people in the data science communities. Depending on your learning style and need, there’s a platform for you. The first half of the article is my final assessment of the platforms, ending with a complete list of platforms with all the details that helped me evaluate them. The second half of the article details my approach in evaluating the platforms — understanding the different user types, sourcing platforms, and variables to evaluate. So if you have the time, skip to the end first to understand my evaluation criteria and then read the results. If you’re pressed for time, just go to the next section.

I created 2 graphs to map my variables. They’re those graphs with the quadrants you see in all the whitepapers. Honestly, I’m a bit ashamed that I chose this visualization (it feels like making 3D pie charts in Excel) but I think it will allow you to quickly figure out which platform fits you best.

Graph 1: Learning style and experience

Ask yourself: What is my learning style? Do I like to dive right in and start coding or do I like to watch videos first? Am I totally new at this and need more hand-holding? Or can I jump right in?

Learning style by user experience [Image created by N. Rosidi]

At the extreme end of lecture-based platforms are pure passive lectures like text on webpages or people talking on videos. Oftentimes these platforms ask you to install the software yourself, which I think is lazy since it’s so easy to spin up a server with all the software to offer the full out-of-the-box experience. Users aren’t always advanced — that’s why they’re learning — so offering them an experience where they can access the software, libraries, and datasets easily accessible via browser is now a must-have — at least to me.

Interactive, self-guided learning allows you to work at your own pace and select whatever topic you want to learn or practice. At the extreme end, there’s a fully functional IDE that allows you to explore, interact, and manipulate data like you would in a real professional setting. But it’s not just completely self-guided as there are solutions to help you solve questions and understand concepts. The approach here is to start coding immediately and learn the solution if you are stuck. These platforms are perfect for the experienced user, even if you’re just moderately experienced.

The middle ground is a blend of lecture and interactive self-guided learning. With these platforms, you’re on a “course” and “path” to learn a set of concepts from start to finish. You’re presented with text and/or video and then asked to apply what you learned in an interactive-but-limited IDE where you’re asked questions that build up in complexity. Interactive-but-limited IDE means that the IDE only accepts inputs that answer the question prompted so you can’t go off the path and explore the data or try out different functions and techniques — you know, things a good data scientist would do. This is perfect for someone learning from scratch and is trying to learn something new from the ground up.

Graph 2: Content Focus

Ask yourself: Am I trying to learn python? Or am I trying to learn python to be a data scientist? Am I trying to prepare for an interview? Or am I trying to build my first machine learning model? Or just trying to learn the basics?

Educational topics by technical focus [Image created by N. Rosidi]

Specialty topics are educational content meant for a specific purpose. For example, it’s preparation for a technical interview — either data science or software development. It’s to learn financial modeling with python or to build a gradient boosted decision tree. These platforms serve a niche population but they do it extremely well to the satisfaction of their user base.

General education is merely an introduction to basic concepts that are building blocks to becoming proficient in navigating the language, like how to create pandas dataframes to manipulate data. Once you’ve mastered these concepts, the next phase would be to dive into specific topics that serve your needs.

Platforms are designed with a user in mind. In this case, are you a data scientist or software developer? If you’re reading this, you’re probably a data scientist, but as I’ve mentioned before there are many more educational platforms created for teaching software development. Sometimes these platforms offer education in python but their content and examples aren’t always meant for data scientists. It’s sort of like adding a plug-in to a piece of software (it’s not native and sometimes feels a bit hacky).

In case the graphs aren’t enough for you.

What each platform is known for and how they’re unique

Link to the Google Sheet:

What type of user would benefit most from each platform

Link to the Google Sheet:

Teaching style and features by user experience level

Link to the Google Sheet:

Educational topics by technical focus (Data Science and Software Development)

Link to the Google Sheet:

So depending on your career goals and how you like to learn, there’s a platform for everyone. In your journey, you might end up using a few of these platforms since each platform focuses on a specific strength. Pick the data science platform that allows you to learn what you need to learn for the stage you’re at. I hope this list has been helpful.

See below for my approach to evaluating the platforms…