The Ultimate Data Science Course List

Start Your Data Science Career Here!

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Data Science Library

5 min readNov 4, 2018

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Data science is a booming industry today, often called “the sexiest job in the world”. However, it can be a pain to manually research and find the right learning resources to start your journey. We here at Data Science Library have put together an extensive index of online courses, videos, Medium posts, and books to help you get your journey started. Please enjoy!

Learn how to use Git:

Git is a very complicated piece of software to explain, but there are a few good Medium posts that explain them in-depth such as this one and this one. We highly recommend you give them both a read through before you attempt to go through these 2 listed Git resources.

Git and GitHub for Beginners (video series): This short video series provide beginners with an approachable introduction to Git and GitHub, and how to use them together.
Pro Git (online book): The first few chapters provide a thoughtful introduction to Git, while the later chapters handle more complicated topics like GitHub integration and workflows on large projects. This is one of the most approachable online Git resources, as it teaches the concepts (as well as the code) in an approachable and logical way.

Data Science

A gigantic field, data science encompasses statistics and computer science and business knowledge all in an effort to extract valuable information from data. There are numerous resources to learn about this, but we have compiled some extremely useful videos on the subject. A few of these are by highly reputable universities like Stanford and MIT, so any aspiring data scientist in college should definitely give them a look. It could just be what you will encounter in the near future!

SQL

Structured Query Language, otherwise known as SQL, is an essential programming language for anyone interested in data science to learn. It is the standard language for accessing and manipulating databases. There’s hundreds of places to learn it, so here are a few of them. Definitely learn this first, since it isn’t that difficult to pick up and is surprisingly easy to master!

Data Visualization and Analytics

The beauty of data science is that once you have your information, you can do virtually anything with it. From creating your own personalized scatter plots to developing functions to find trends in your data, these sources will help you master the art of presenting your findings to the world!

D3

From streamgraphs to Voronoi to polar clocks, there’s essentially no limit to the types of data visualizations you can make with the JavaScript library D3.

Here’s how to get started with D3!

Learn D3:

Intro: https://www.dashingd3js.com/introductory-d3-course
Intermediate: https://www.dashingd3js.com/intermediate-d3-course
Official D3 Tutorials: https://github.com/d3/d3/wiki/Tutorials
D3 by Scott Murray: http://alignedleft.com/tutorials/d3
D3 API reference: https://github.com/d3/d3/blob/master/API.md

Here’s another resource for interactive data visualization, taken from a UW Informatics course!

Web Scraping

Web scraping is essentially the act of extracting large amounts of data from a website, and then storing all of that information in a table or spreadsheet on your computer. It is somewhat complicated but can be very useful for making databases for you to use in your own projects, so its worth checking out once your data science skills become more refined.

Machine Learning:

Machine learning is a particular method of data analysis that uses various algorithms and statistical models to automate computers, so that they can act and predict outcomes with little to no human interaction. It sounds complex, and generally is, but with the courses we’ve listed you can get started in diving into this deep topic. The payoff is worth it, as any data scientist worth their salt will tell you!

15 hours of Expert Machine Learning by Data School
Statistical Learning (9-week online course): Taught by Trevor Hastie and Rob Tibshirani of Stanford using their new “Introduction to Statistical Learning” textbook. It covers a wide gamut of supervised learning methods and a few unsupervised learning methods. They cover the math and concepts behind each method and then work through example implementations in R. Although this course skewed a bit heavy on math and light on application for my taste, the textbook is fantastic and they are clearly masters of this material.
Introduction to Statistical Learning: free PDF download of the textbook
Solutions to the textbook exercises: unofficial and incomplete answers on GitHub
Machine Learning applications (links): This is a curated list of links to news articles and research papers about how machine learning has been used to solve interesting, real-world problems.

Natural Language Processing

(recommended by Randy Lao)

Abbreviated as NLP, this is a subset of computer science and information engineering that focuses around artificial intelligence. More specifically, it revolves around getting computers to understand and process human languages so that they can get closer to our level of understanding. It is a fascinating, high-level field that is definitely worth investigating if you have the interest. Some say AI is the future, so here’s your chance to get on the wave while you still can!

Core Concepts:

Solutions to the textbook exercises: unofficial and incomplete answers on GitHub
Introduction to Bag of Words (CountVectorizer, TFIDF, HashVectorizer)
Text Preprocessing (Stopword Removal, Tokenization, Stemming/Lemmatization)
Word Vectors
Regex Tutorial

Common NLP Libraries:

NLP Projects

Click here to learn how to solve 90% of NLP problems!

Thank you for reading! If you liked these resources, please give us a clap. Also, a special thank you to Randy Lao for recommending a lot of these resources and Sanjay Unni for editing this article. Add Randy on LinkedIn here! Add Sanjay on LinkedIn here!