I ranked every Intro to Data Science course on the internet, based on thousands of data points
By: David Venturi
Tags: Big Data, Programming, Startup, Data Science, Tech

Our goal with this introduction to data science course is to become familiar with the data science process. We don’t want too in-depth coverage of specific aspects of the process, hence the “intro to” portion of the title.

For each aspect, the ideal course explains key concepts within the framework of the process, introduces common tools, and provides a few examples (preferably hands-on).

We’re only looking for an introduction. This guide therefore won’t include full specializations or programs like Johns Hopkins University’s Data Science Specialization on Coursera or Udacity’s Data Analyst Nanodegree. These compilations of courses elude the purpose of this series: to find the best individual courses for each subject to comprise a data science education. The final three guides in this series of articles will cover each aspect of the data science process in detail.

Basic coding, stats, and probability experience required

Several courses listed below require basic programming, statistics, and probability experience. This requirement is understandable given that the new content is reasonably advanced, and that these subjects often have several courses dedicated to them.

This experience can be acquired through our recommendations in the first two articles (programming, statistics) in this Data Science Career Guide.

Our pick for the best intro to data science course is…

Kirill Eremenko’s Data Science A-Z™ on Udemy is the clear winner in terms of breadth and depth of coverage of the data science process of the 20+ courses that qualified. It has a 4.5-star weighted average rating over 3,071 reviews, which places it among the highest rated and most reviewed courses of the ones considered.

It outlines the full process and provides real-life examples. At 21 hours of content, it is a good length. Reviewers love the instructor’s delivery and the organization of the content. The price varies depending on Udemy discounts, which are frequent, so you may be able to purchase access for as little as $10.

Though it doesn’t check our “usage of common data science tools” box, the non-Python/R tool choices (gretl, Tableau, Excel) are used effectively in context. Eremenko mentions the following when explaining the gretl choice (gretl is a statistical software package), though it applies to all of the tools he uses (emphasis mine):

In gretl, we will be able to do the same modeling just like in R and Python but we won’t have to code. That’s the big deal here. Some of you may already know R very well, but some may not know it at all. My goal is to show you how to build a robust model and give you a framework that you can apply in any tool you choose. gretl will help us avoid getting bogged down in our coding.

One prominent reviewer noted the following:

Kirill is the best teacher I’ve found online. He uses real life examples and explains common problems so that you get a deeper understanding of the coursework. He also provides a lot of insight as to what it means to be a data scientist from working with insufficient data all the way to presenting your work to C-class management. I highly recommend this course for beginner students to intermediate data analysts!

The preview video for Data Science A-Z™.

A great Python-focused introduction

Udacity’s Intro to Data Analysis is a relatively new offering that is part of Udacity’s popular Data Analyst Nanodegree. It covers the data science process clearly and cohesively using Python, though it lacks a bit in the modeling aspect. The estimated timeline is 36 hours (six hours per week over six weeks), though it is shorter in my experience. It has a 5-star weighted average rating over two reviews. It is free.

The videos are well-produced and the instructor (Caroline Buckey) is clear and personable. Lots of programming quizzes enforce the concepts learned in the videos. Students will leave the course confident in their new and/or improved NumPy and Pandas skills (these are popular Python libraries). The final project — which is graded and reviewed in the Nanodegree but not in the free individual course — can be a nice add to a portfolio.

Udacity instructor Caroline Buckey outlining the data analysis process (also known as the data science process).

An impressive offering with no review data

Data Science Fundamentals (Big Data University)

Data Science Fundamentals is a four-course series provided by IBM’s Big Data University. It includes courses titled Data Science 101, Data Science Methodology, Data Science Hands-on with Open Source Tools, and R 101.

It covers the full data science process and introduces Python, R, and several other open-source tools. The courses have tremendous production value. 13–18 hours of effort is estimated, depending on if you take the “R 101” course at the end, which isn’t necessary for the purpose of this guide. Unfortunately, it has no review data on the major review sites that we used for this analysis, so we can’t recommend it over the above two options yet. It is free.

A video from the first module of the Big Data University’s Data Science 101 (which is the first course in the Data Science Fundamentals series).

The competition

Our #1 pick had a weighted average rating of 4.5 out of 5 stars over 3,068 reviews. Let’s look at the other alternatives, sorted by descending rating. Below you’ll find several R-focused courses, if you are set on an introduction in that language.