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Five essential things you must know to learn Tableau

A guide with free resources for building your skills

Inka Trail in Peru. Image by the author.
Inka Trail in Peru. Image by the author.

Tableau is an excellent tool for visualizing Data. However, it’s not intuitive software you can learn through discovery. Expect a lot of frustration if you start clicking around the interface without having a solid background in data and visualization principles.

This article builds on my recent article containing a collection of free resources for learning Tableau.

The best free resources for learning Tableau

Here, I help you maximize these and other resources by summarizing the five essential things learners must know before learning Tableau. My recommendations are based on my own experiences teaching new learners data analysis and visualization.

This list should be viewed as a point of departure to help orient your learning activities. Be careful, thinking it is an exhaustive list of recommendations or a short checklist you can complete before working with the software.

1. Data types

Street performer in Kunming, China. Image by the author.
Street performer in Kunming, China. Image by the author.

Make sure you have a clear and deep understanding of the differences between data types, especially between measures (or metrics) and dimensions, and continuous vs. discrete. Every decision in Tableau hinges on having a clear understanding of data. Remember that these concepts apply broadly to data analysis and visualization, not just Tableau.

2. Data structures

Something electrical. Image by the author.
Something electrical. Image by the author.

The most time-consuming work in data visualization is preparing the data. Most data sets are in wide format, but Tableau prefers data in the long format. Tableau provides functionality for pivoting the data but will never tell you when it’s time. Before learning Tableau, ensure you have a solid understanding of wide and long data.

I strongly recommend that every student spend a lot of time reviewing the paper Tidy Data, by Hadley Wickham. You can safely ignore the content on R coding, but everything else on structuring data is essential. The paper is technical and complex for beginners but critical to any serious work with data. Spend time working through the paper and revisiting it regularly as you build experience with data.

I also recommend this article in Toward Data Science by Jonathan Serrano:

https://towardsdatascience.com/long-and-wide-formats-in-data-explained-e48d7c9a06cb

3. Spreadsheet skills

Fire extinguishing system. Image by author.
Fire extinguishing system. Image by author.

Don’t underestimate the value of spreadsheet skills. For several reasons, I teach introductory Tableau courses and ensure my students have a firm grasp of spreadsheet skills. Spreadsheets allow you to see your data, which is essential to understanding its contents and structure. When working with data, you should continuously look at the raw data and the summarized form. Spreadsheets are excellent for looking at your data.

You will sometimes need to make corrections in the raw data, which isn’t possible with Tableau. Additionally, spreadsheet functions for manipulating data are similar to Tableau’s functions for creating calculated fields. Many excellent free resources exist for learning spreadsheets. If you want a single collection of high-quality, free resources, go directly to Leila Gharani’s YouTube channel. She has a fantastic collection of videos covering everything you need to know about spreadsheets.

4. Pivot tables

Jiaotong Teahouse in Chongqing China. Image by the author.
Jiaotong Teahouse in Chongqing China. Image by the author.

Visualizing data is similar to summarizing data using spreadsheet pivot tables. I often create summary tables in Tableau before building my visualizations to ensure all my calculations are correct. While commonly referred to as text tables in Tableau, they are essentially the same as spreadsheet pivot tables. Pivot tables will force you to understand the critical differences between measures and dimensions and the different types of aggregations. Since pivot tables are part of the spreadsheet skills, I refer you to Leila Gharani’s tutorials:

5. Visual encoding

Photo of tools. Image by the author.
Photo of tools. Image by the author.

Tableau is a tool for visualizing data. No matter your technical proficiency with the Tableau, your knowledge of visual communication will be your limiting factor. You need a solid understanding of visually representing quantitative data, especially the differences between measures and dimensions and continuous versus discrete values. Take time to build a conceptual understanding of marks and channels. Here are a few videos to get you started.

Again, remember that Tableau is just a tool, and learning Tableau is not the same as learning data visualization. I can teach you how to use a hammer, but that doesn’t mean you can design and build a house. For that reason, I’ll take the liberty and refer you to one of my earlier articles that emphasize the critical distinction between the role of software and design when doing data visualizations.

Less Software, More Design

Next Steps

Inka hiking trail in Peru. Image by the author.
Inka hiking trail in Peru. Image by the author.

Again, this article is not exhaustive, but a short list of essential things to get you oriented in learning Tableau. Feel free to use the comments section to post other resources and things you found helpful in your learning. Be sure to follow me if you are interested in building your data skills and learning Tableau.


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