Tableau
For the design exercises (and possibly other things), you must at least try Tableau.
Tableau is a commercial data analysis and visualization tool. The company generously provides it for use in classes through the Tableau for Teaching Program.
Here, we will give you a brief guide on how to get started with it, and pointers to some resources. We expect students to figure out how to work with Tableau enough for class.
A warning: Tableau is a big, complex system. While it is designed to be “user friendly”, it is still a powerful tool. It takes some time to learn. Our experience is that students in the class can usually learn enough about how to use it quickly to do the assignments that we ask.
Rather than providing training in class, we point you towards the many great resources on learning Tableau on the web.
Please use Piazza to help your classmates conquer Tableau. Yes, it’s big, but there are 50+ smart students in class! If you find a great resource, post about it. If you run into a hurdle, ask - someone else is probably running into the same problem!
The course staff are not Tableau experts.
Getting Access to Tableau
Tableau (the company) has provided us access to Tableau (the software) in twothree ways:
You can download the desktop version and use the class license. Information on how to do this is on Canvas (since I can’t put it on the open web).
Tableau makes the software free for students, so you can also your own student license. This has the advantage that it doesn’t expire when class ends.
(recomended) You can use the online version. We will invite everyone in class to our “class instance” of Tableau online. Look for an email (we give Tableau your email address, it sends you a note and asks you to register). You will receive this soon - we expect to send out the invitations on Monday morning 9/17 (before class).
The online version has the advantage that there is no software to install. In theory, it also makes it easier for people to collaborate and share. For the work we are doing in class, the online version should be sufficient.
We may also be able to share the datasets in the online version (so you don’t have to upload the standard data sets yourself). We are still working on this.
Why Tableau?
You might be (justifyably) wondering… why do we force people to use Tableau? Normally, I don’t like to promote any particular tool, particularly not an expensive commercial one.
Some reasons (in no particular order):
Tableau is actually an example of how the theory we discuss in class (thinking in terms of data types and encodings) manifests itself in practical ways. Note how Tableau doesn’t give zillions of chart types: it lets you directly control encodings.
It emphasizes the choice in building vs. using tools. As Computer Scientists, we often prefer to write programs - which has upsides and downsides. I really want students to experience good tools so they can better understand this tradeoff.
Tableau actually is quite good at what it does. While it definitely is a “power user tool” it represents a really well-thought out approach for helping people do visual analysis of data. Even novices can explore different design choices to explore data.
Under the hood, it is pretty amazing (from a systems perspective). It handles very large datasets without effort. It can often produce charts faster than my python scripts can merely read the data.
It actually produces good charts (in terms of the technical “art”). It includes a lot of design experience, best practices, and research in how it makes choices. It has numerous ways to encourage you to “do the right thing.” This frees you from the details (what font do I pick? what spacing for a bar chart?) and lets you focus on finding good encodings for the data.
The biggest downside is that it is truly a power-user tool. It is designed so that experienced users can do amazing things, not necessarily to make it easy for a first time novice. That said, I find it to be an impressive balance of not being too inaccessible, but also
Getting Started with Tableau Online
After you log in to Tableau Online, you can create a Workbook and connect to an Excel or Text (CSV) file that you upload from your computer. The first row in the file should contain the column headers. Then you can simply drag the fields from the Data pane on the left (representing columns in the Excel or Text file) out to “Columns”, “Rows”, or “Color”, “Size”, “Text”, etc, under “Marks”. These fields are broken up into dimensions and measures. Dimensions are categorical fields (usually discrete), and measures are metrics or numbers you want to analyze (often continuous). You can change the method of aggregation of the measures to “Sum”, “Average”, “Median”, etc, by using the dropdown menu of the fields. In case you know the data you want to look at, but don’t know the best way to view the data, you can select different dimensions and measures while holding down the control key and click on the “Show Me” button on the right to display a list of common chart types that can help you start your analysis. For more detailed instructions and sample datasets, you could watch the “Getting Started” video from the official Tableau 2020.3 Free Training Videos website. You may also find YouTube videos such Edureka, freeCodeCamp.org, or Penguin Analytics useful as additional examples.
The way I like to learn these things is by diving in and fiddling around with it. Some things to do (in rough order)…
- Make sure you can create a new workbook (use the “create” button )
- Connect to Data (initially, you might want to upload a file, in the future, you may want to connect to one of the data sets we provide).
- The Key to Tableau is making sure it understands the type of your data. If Tableau understands your data, it will make it easy to create visualizations that show it.
- Tableau has the concept of “dimensions” and “measures” - which are a little more complicated than just “categorical vs. numeric” variables. Understanding this concept is important. If a variable is the wrong type, Tableau won’t let you do things. Tableau has “green pills” (measures) and “blue pills” (dimensions) that you can drag into visualizations.
- Creating appropriate dimensions and measures from your data is important. For some things, this requires right clicking on a variable and either “convert to measure” (to change its type), “change data type” (if it is misinterpreting numbers as strings), or even computing fields.
- Often you need to explicitly tell Tableau how to interpret your data correctly, for example, taking a set of discrete choices and aggregating them by counting (or a set of continuous values and aggregating them by counting buckets).
- You can use different variables by dropping them different places on the workspace. Rows and columns are an obvious place. This lets you put mutliple variables into a visualization. For any of the “pills” in the visualization (for example, in the columns, rows, color, …) you can change its settings.
I find that playing with things usually reveals many of the things I want to do.
Resources for Learning About Tableau
Everyone has different tastes in how they like to learn these kinds of things. If you find something useful - a website, a video, etc, even a tip you on what you figured out, please share it with your classmates on Piazza!
Official Tableau Documents
Tableau recommends the Data Analytics for University Students guide. Although, that seems to just be a way to get to Getting Started as a Creator. This page discusses the desktop, but online is very similar.
Linked In Learning
LinkedIn Learning is a source of commercial video courses on various topics. The University has a subscription. You may need to sign in via the University single sign-on in order to access courses. It seems to figure out how to connect between the University credentials and the regular LinkedIn signons.
LinkedIn Learning has a lot of useful video classes, usually with high production values.
They have a number of courses on Tableau. We have not watched them ourselves. If you do look at them, please put a post on Piazza with your thoughts.
Some courses that may be relevant: