Using Tableau for Data Science

Perhaps you’ve heard a growing buzz in data science circles about Tableau. The Seattle-based software firm got its start at Stanford University in 2003 and launched an initial public offering in 2013.

Tableau’s mission is to make spreadsheets, databases, and other information sources simpler for the average person to use. Co-founders Christian Chabot (CEO), Pat Hanrahan (Chief Scientist), and Chris Stolte (Chief Development Officer) began by combining a structured query language for databases with a descriptive language for rendering graphics.

The result? A database visualization language called Visual Query Language or VizQL. VizQL underlies Tableau’s eponymous software application, which queries relational databases, cubes, cloud databases, and spreadsheets, and then generates a variety of graph types. These graphs can be combined into dashboards and shared over a computer network or the internet.

Today Tableau boasts more than tens of thousands of customer accounts, including Red Hat, Nissan, Whole Foods, and Verizon. Allrecipes executives are using Tableau to develop a 360° view of consumers. More than 600 employees at Cornell University perform analysis with Tableau: They manage contributor relations, visualize faculty salary statistics, and track which students are in which classes. Tableau helps Zillow provide self-serve business intelligence that empowers business owners to answer their own questions.

Tableau currently offers four main products:

Given that Tableau aims to make analytics easy for analysts, executives, IT, and everyone else, data scientists may view the software as, at best, beneath them and, at worst, a threat to their livelihood.

Second point first. Popular and powerful as it is, Tableau is but one of many pieces of software used to glean insights from data. So even if everyone from the corner office to the reception desk is proficient in Tableau soon, plenty of programs will remain under the near exclusive purview of the data scientist.

So perhaps the aspiring data scientist should just leave Tableau to the amateurs? Think again. Tableau software complements such data science standbys as R and Python. While Tableau is not the best tool for cleaning or reshaping data, and its relational model doesn’t allow for procedural computations or offline algorithms, it does excel at data exploration and interactive analysis. Tableau even offers courses for data scientists looking to make use of the software.

Sold on learning Tableau, or at least checking it out? You could be up to speed in as little as a few weeks thanks to Tableau’s training options, including:

If you’re the bookish type, you might consider Tableau Your Data!, which goes beyond the basics of Tableau Desktop’s interface and explains best practices for creating effective visualizations for specific business intelligence objectives.

Last updated: June 2020