by David Venturi

An overview of every Data Visualization course on the internet

History of Crayola Colors by Stephen Wagner via Tableau Public.

A year ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master’s program using online resources. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead. And I could learn it faster, more efficiently, and for a fraction of the cost.

I’m almost finished now. I’ve taken many data science-related courses and audited portions of many more. I know the options out there, and what skills are needed for learners preparing for a data analyst or data scientist role. A few months ago, I started creating a review-driven guide that recommends the best courses for each subject within data science.

For the first guide in the series, I recommended a few coding classes for the beginner data scientist. Then it was statistics and probability classes. Then it was intros to data science itself.

Now onto data visualization.

For this guide, I spent 10+ hours trying to identify every online data visualization course offered as of March 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings. For this task, I turned to none other than the open source Class Central community and its database of thousands of course ratings and reviews.

Class Central’s homepage.

Since 2011, Class Central founder Dhawal Shah has kept a closer eye on online courses than arguably anyone else in the world. Dhawal personally helped me assemble this list of resources.

How we picked courses to consider

Each course must fit three criteria:

  1. The majority of the course must be focused on explanatory data visualization. Coverage of data preparation, for example, is permitted given it is an important part of the data visualization process. Courses that cover less relevant topics (statistical modeling, for example) are excluded. More on the explanatory distinction below.
  2. It must be on-demand or offered every few months.
  3. It must be an interactive online course, so no books or read-only tutorials. Though these are viable ways to learn, this guide focuses on courses.

We believe we covered every notable course that fits the above criteria. Since there are seemingly hundreds of courses on Udemy, we chose to consider the most-reviewed and highest-rated ones only. There’s always a chance that we missed something, though, so please let us know in the comments section if we left a good course out.

How we evaluated courses

We compiled average rating and number of reviews from Class Central and other review sites to calculate a weighted average rating for each course. We read text reviews and used this feedback to supplement the numerical ratings.

We made subjective syllabus judgment calls based on two factors, with the first given preference over the second:

  1. Coverage of data visualization theory. Are the motivations for visualization choices explained? Does the course only teach the tool? More on this in the next section.
  2. Coverage of chosen data visualization tool(s). Does the course effectively teach common visualization tools (Tableau, ggplot2, Seaborn, etc.)? Do students have opportunities to practice these skills? No preference for tool choice is given.
Tableau and ggplot2 are common data visualization tools.

Why prioritize visualization theory

Mastery of a specific tool is wasteful without knowledge of the fundamentals of effective visualization. Plus, tools are often interchangeable depending on the setting.

More importantly, doing good data visualization is more complex than most people think. Careful thought is required from the planning stages to execution. Choosing the right chart, balancing complexity and clutter, leveraging preattentive properties, and more, data visualization is both an art and a science. It is easy to go wrong, and sometimes horribly (see below).

He’s 243% baby boomer. An example of data visualization gone wrong, courtesy of WTF Visualizations.

Exploratory vs. explanatory visualization

As described by Indiana University professor Yong-Yeol Ahn, the aim of explanatory data visualization is to communicate insights and messages, while the aim of exploratory visualization is to discover hidden patterns.

This article focuses on explanatory data visualization courses. Courses like Udacity’s Data Analysis with R (exclusively an exploratory course) are therefore excluded from this article. The topic is important; there just aren’t enough courses to justify a standalone article. It will be covered briefly in the summary article for this series.

Coding experience sometimes required

Some courses listed below require basic coding skills in the course’s language of instruction. If you have very little programming experience, our recommendations in the first article in this series — the best intro to programming courses for data science — would be a great start. Both Python and R courses are covered.

Review data lacking

Compared to the other articles in this series, there is a lack of review data for data visualization courses that fit the above criteria. There is also no clear best data visualization course yet. The recommendations below are therefore not as conclusive as past articles. As always, but especially here, try to pick the course that best fits your needs.

Our pick for the best data visualization course is…

…which contains the following five courses:

The University of California, Davis’ Data Visualization with Tableau Specialization has the best combination of theory and tool coverage available based on this article’s evaluation criteria. It dives deep into theory like few other courses. There are opportunities to practice Tableau via walkthroughs and a final project, though mastering Tableau is not the main focus. It is a fairly new specialization (late 2016) and the courses only have one 4-star rating between them on the review sites used for this analysis.

Govind Acharya, Hunter Whitney, and Suk Brar are the instructors. Acharya is a Principal Analyst at UC Davis. Whitney and Brar are respected industry professionals. Between them, they have decades of data visualization experience that is clearly conveyed through the course content. The videos are well-produced.

The estimated timeline for the specialization on Coursera is 22 weeks with weekly commitments ranging from three to eight hours per week. These estimates are assuredly too high, as noted by several reviewers and my experience with Coursera. Free (auditing each course individually) and paid (paying for the Specialization) options are currently available.

Several prominent reviewers on Coursera noted the following:

They not only tell you how to do the visualization design but also tell you why (the physiology, the principles). I would highly recommend this class.
Great course — guards against some subtle pitfalls in visualization preparation.
Although a very basic introduction to the use of Tableau, the course provides a broad and interesting background that should prove useful to anyone seeking to enhance their understanding of visualization fundamentals.
University of California, Davis’ Coursera page.
Govind Acharya and Hunter Whitney are instructors for the Data Visualization with Tableau Specialization.

Visualization theory and R, learned by doing

  • Data Visualization with ggplot2 by DataCamp

…for which there are three parts:

Another great option is DataCamp’s Data Visualization with ggplot2 series, especially if you want to learn R and, more specifically, ggplot2. A substantial amount of theory is covered, which is fitting given that ggplot2 is inspired by The Grammar of Graphics. Tool coverage and practice are impressive as well — you will know R and its quirky syntax quite well leaving these courses. There are no reviews for these courses on the review sites used for this analysis.

The instructor for all three courses is Rick Scavetta, who is a biologist, workshop trainer, freelance data scientist, and cofounder of Science Craft. DataCamp’s hybrid teaching style leverages video (starring Scavetta) and text-based instruction with lots of examples through an in-browser code editor. The video, text, and code content is polished nicely.

Together, the estimated timeline for all three courses is 16 hours. The first chapter of each course is available for free. A DataCamp subscription, which is currently $29 per month or $300 per year, is required for full access.

The following endorsement is from Hadley Wickham, Chief Scientist at RStudio and ggplot2 creator:

I thoroughly recommend “Data Visualization with ggplot2” by Rick Scavetta. It gives you an excellent introduction to ggplot2. You’ll learn both the underlying theory, and get hands on practice in DataCamp’s online learning environment.
DataCamp’s logo.

A practical intro to Tableau with an excellent instructor

Tableau 10 Series by Kirill Eremenko and the SuperDataScience Team on Udemy, which includes:

Taught by Kirill Eremenko, SuperDataScience’s Tableau 10 Series is an effective practical introduction. It focuses mostly on tool coverage (Tableau) rather than data visualization theory. Eremenko is one of the most well-regarded instructors in these guides with consistently positive reviews across of his courses. The A-Z course is a prerequisite to the Advanced Training course. Together, the courses in the series have a 4.6-star weighted average rating over 3,724 reviews.

The series has seventeen hours of video content. The cost of each course varies depending on Udemy discounts, but these are are frequent, and can be purchased for as little as $10.

Several prominent reviewers noted the following:

This was great. I use Tableau daily but it was an awesome refresher on some of the items i don’t use and a great study aid for sitting the Tableau Certified Professional Exam. Good job Kirill and the Team!

Kirill is a tremendous teacher and students taking this course will clearly see why he has dozens of courses and thousands of students — he’s able to teach complex skills, in a real world business context and do so incrementally thereby combining the often complex task of teaching both fundamentals and context specific applications simultaneously.

The competition

Let’s look at the other alternatives, sorted by descending rating.

Interactive Data Visualization with Python & Bokeh (Ardit Sulce/Udemy): Tool focus (Python and Bokeh). Includes a section on creating web applications. Seven hours of video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.6-star weighted average rating over 103 reviews.

Information Visualization (IVMOOC) (Indiana University/Independent): Covers theory and multiple tools in great detail. Impressive real-life project. Registration did not work when attempted despite emails to the course administrators. A full twelve-week graduate course. Free. It has a 4.5-star weighted average rating over 2 reviews.

Indiana University offers Information Visualization (IVMOOC).

Tableau for Beginners — Get Certified Accelerate Your Career (Lukas Halim/Udemy): Tool focus (Tableau). Four hours of video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.5-star weighted average rating over 649 reviews.

Analyzing and Visualizing Data with Power BI (Microsoft/edX): Tool focus (Power BI). Tailored for business users invested in the Microsoft ecosystem. Part of the Microsoft Professional Program Certificate in Data Science. Estimated timeline of two to four hours per week over six weeks. Free with a Verified Certificate available for purchase. It has a 4.5-star weighted average rating over 117 reviews.

Analyzing and Visualizing Data with Excel (Microsoft/edX): Tool focus (Excel). Tailored for business users invested in the Microsoft ecosystem. Part of the Microsoft Professional Program Certificate in Data Science. Estimated timeline of two to four hours per week over six weeks. Free with a Verified Certificate available for purchase. It has a 4.5-star weighted average rating over 972 reviews.

Microsoft offers two data visualization courses on edX: Analyzing and Visualizing Data with Power BI and Analyzing and Visualizing Data with Excel.

Data Visualize Data with D3.js The Easy Way (Infinite Skills/Udemy): Tool focus (D3.js). Four hours of video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.4-star weighted average rating over 262 reviews.

Data Visualization with Python and Matplotlib (Stone River eLearning/Udemy): Tool focus (Python and Matplotlib). Six hours of video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.4-star weighted average rating over 92 reviews.

Data Analysis: Visualization and Dashboard Design (Delft University of Technology/edX): Tool (Excel) and business focus. Estimated timeline of four to six hours per week over six weeks. Free with a Verified Certificate available for purchase. It has a 4.2-star weighted average rating over 5 reviews.

Big Data: Data Visualisation (Queensland University of Technology/FutureLearn): Balanced theory/tool focus. Exposure to a variety of tools. Starts August 2017. Estimated timeline of two hours per week over three weeks. Free with an “upgrade” available for purchase. It has a 4-star rating over 1 review.

Data Visualization and Communication with Tableau (Duke University/Coursera): Tool (Tableau) and business focus. Part of the Excel to MySQL: Analytic Techniques for Business Specialization. Estimated timeline of six to eight hours per week over five weeks. Free and paid options available. It has a 3.67-star weighted average rating over 9 reviews.

Data Visualization (University of Illinois at Urbana-Champaign/Coursera): Theory focus. Part of the Data Mining Specialization. Estimated timeline of four to six hours per week over four weeks. Free and paid options available. It has a 3.14-star weighted average rating over 22 reviews.

Data Visualization is offered by University of Illinois at Urbana-Champaign on Coursera.

Data Visualization and D3.js (Udacity): Balanced theory/tool focus. The D3.js instruction feels “incomplete” and “out of place.” Estimated timeline of seven weeks. Free. It has a 2.83-star weighted average rating over 6 reviews.

Data Management and Visualization (Wesleyan University/Coursera): Balanced theory/tool focus. Covers multiple tools (Python and SAS). Part of Wesleyan’s Data Analysis and Interpretation Specialization. Estimated timeline of four to five hours per week over four weeks. Free and paid options available. It has a 2.67-star weighted average rating over 6 reviews.

Applied Plotting, Charting & Data Representation in Python (University of Michigan/Coursera): Balanced theory and tool focus. Free and paid options available. It has a 2-star weighted average rating over 4 reviews.

The following courses had no reviews as of March 2017.

Data Visualization in Tableau (Udacity): Theory focus with excellent coverage. Brief tool coverage (Tableau). Primarily text-based instruction with multiple choice quizzes. Part of Udacity’s Data Analyst Nanodegree and Predictive Analytics for Business Nanodegree. This course is likely bound for a top three spot when updated with videos to complement the text. Estimated timeline of three weeks. Free.

Building Data Visualization Tools (Johns Hopkins University/Coursera): Tool focus (R and ggplot2). Part of JHU’s Mastering Software Development in R Specialization. Estimated timeline of two hours per week over four weeks. Free and paid options available.

Data Visualization for All (Trinity College/edX): Theory focus. Estimated timeline of three hours per week over six weeks. Free with Verified Certificate available for purchase.

Data Visualization with Advanced Excel (PwC/Coursera): Tool focus (Excel). Part of PwC’s Data Analysis and Presentation Skills: the PwC Approach Specialization. Estimated timeline of three to four hours per week over four weeks. Free and paid options available.

Communicating Business Analytics Results (University of Colorado Boulder/Coursera): Theory and business focus. Part of Colorado Boulder’s Data Analytics for Business Bootcamp Specialization. Estimated timeline of four weeks. Free and paid options available.

Storytelling Through Data Visualization (Dataquest): Mostly a tool focus (Python, Matplotlib, and Seaborn). Estimated timeline unclear. Mostly free, but a subscription is required for full access.

Data Visualization Learning Path (O’Reilly): Balanced tool/theory focus. Covers D3.js. Multiple instructors. Fifteen hours of content. Free with a ten-day free trial.

Data Visualization for Developers (Dan Appleman/Pluralsight): Theory focus. Tailored for developers. Two hours of content. Free with a ten-day free trial.

The following four courses are created by Bill Shander of Beehive Media and offered on Lynda. They are listed in chronological order by release date.

Data Visualization Fundamentals (Bill Shander/Lynda): Theory focus. Four hours of content. Free with a ten-day free trial.

Designing a Data Visualization (Bill Shander/Lynda): Theory focus. Covers creating a specific project from concept to data analysis to design and execution. Four hours of content. Free with a ten-day free trial.

Data Visualization for Data Analysts (Bill Shander/Lynda): Theory focus. Tailored for data analysts. Two hours of content. Free with a ten-day free trial.

Data Visualization Storytelling Essentials (Bill Shander/Lynda): Theory focus. Two hours of content. Free with a ten-day free trial.

Visualization in R, From Beginner to Advanced (Nathan Yau/FlowingData): A four-week course. Subscription required.

The following four courses are offered by DataCamp. As noted above, DataCamp’s hybrid teaching style leverages video and text-based instruction with lots of examples through an in-browser code editor.

Data Visualization in R (DataCamp): Balanced theory/tool focus. Covers base R graphics. Estimated timeline of four hours. Subscription required for full access.

Introduction to Data Visualization with Python (DataCamp): Tool focus (Python, Matplotlib, and Seaborn). Estimated timeline of four hours. Subscription required for full access.

Matplotlib is a Python 2D plotting library covered in DataCamp’s Introduction to Data Visualization with Python.

Interactive Data Visualization with Bokeh (DataCamp): Tool focus (Python and Bokeh). Estimated timeline of four hours. Subscription required for full access.

Data Visualization in R with ggvis (DataCamp): Balanced theory/tool focus. Covers R and ggvis. Estimated timeline of four hours. Subscription required for full access.

Wrapping it up

This is the fourth of a six-piece series that covers the best online courses for launching yourself into the data science field. We covered programming in the first article, statistics and probability in the second article, and intros to data science in the third article. The remainder of the series will cover other data science core competencies. Next up is machine learning.

If you want to learn Data Science, start with one of these programming classes
medium.freecodecamp.comIf you want to learn Data Science, take a few of these statistics classes
medium.freecodecamp.comI ranked every Intro to Data Science course on the internet, based on thousands of data points
medium.freecodecamp.com

The final piece will be a summary of those articles, plus the best online courses for other key topics such as data wrangling, databases, and even software engineering.

If you’re looking for a complete list of Data Science online courses, you can find them on Class Central’s Data Science and Big Data subject page.

If you enjoyed reading this, check out some of Class Central’s other pieces:

Here are 250 Ivy League courses you can take online right now for free
250 MOOCs from Brown, Columbia, Cornell, Dartmouth, Harvard, Penn, Princeton, and Yale.medium.freecodecamp.comThe 50 best free online university courses according to data
When I launched Class Central back in November 2011, there were around 18 or so free online courses, and almost all of…medium.freecodecamp.com

If you have suggestions for courses I missed, let me know in the responses!

If you found this helpful, click the ? so more people will see it here on Medium.

This is a condensed version of my original article published on Class Central, where I’ve included further course descriptions, syllabi, and multiple reviews.