Data Visualization & Intro to Tableau, August 24, 2017
Software: If you haven’t already, download the free version of Tableau and install it on your computer.
Data sets: We will be using the Superstore dataset that comes with the free version of Tableau. If you have difficulty locating it, download this one: Sample – Superstore.
Unit 1: History & Intro to Data Visualization
We will dive into the history of data visualization and the basics of matching your dataset to an effective visualization.
Exercise 1: Starting With Paper
If you were designing a data dashboard for your life, what would it look like? We go into how starting with simple paper sketches can help you plan out an effective visualization.
Unit 2: Mapping
We work through the process of loading data into Tableau and review the mapping features of the software.
- How do you connect data to a Tableau worksheet?
- Loading the Superstore dataset. Why use this dataset?
- How does Tableau do automatic joins between different datasets so that you can combine them into one workbook?
- How can you split one data element (Customer Name) into two elements (Firstname, Lastname)?
Exercise 2: Profit By City
We will create a map showing profit and loss by US City for our superstore, reviewing how to place dots, and get dots to respond to measures like sales volume and profitability, as well as review the difference between dimensions and measures.
- Measures & Dimensions — what are measures, and what are dimensions?
- How do you apply measures and dimensions to a particular visualization?
- How can I use the Show Me feature to match visualizations to my dataset?
- How do I know that the geocoding for my dataset is correct when I do a map? How can I make it more accurate?
- How do I make the dots on my map respond to things like sales volume, or profitability? How do I make my map more readable by choosing between labels and tooltips?
Unit 3: Comparing Different Dimensions To Reach a Conclusion
Exercise 3: Charts & Graphs Basics
We will use measures & dimensions to create a basic chart with sparklines showing quarterly profit by product category for our superstore.
- How do I change from year to quarter or month in my visualization?
- How can I use sparklines?
- How do I change colors?
- Can I use the same measure or dimension to affect different aspects of the same visualization (for example, as both lines and colors?)
Exercise 4: Scatterplot — Profit & Sales By City
We will create a scatterplot using the sales and profit data and slicing it by our superstore’s location.
- How do I apply specific measures and dimensions to a scatterplot?
- How can I change axes to see if a scatterplot is more effective that way?
- How can I use color and size to show volume and profitability for different cities?
- How can I sort or exclude specific datapoints?
- Is there a visual indicator for profitability in my scatterplot?
Tea Break & Further Introductions
Unit 4: Cooking the Books: Deceptive and Misleading Charts & Graphs
We will learn the three basic ways that the creators of charts and graphs try to manipulate us into coming to conclusions that might not be right. We’ll also learn how to not create bad or misleading charts by accident.
- What are the three basic ways that misleading or deceptive charts and graphs are created?
- What are humans good and bad at seeing, and how does that affect how likely a chart is to be misread?
Exercise 5: Data Two Ways
We will create two charts — one showing sales per region over a span of years, and one showing sales and profitability.
- How can I use a heatmap-style grid to focus the user’s eye on highs and lows?
- Why is it important to show certain types of data together?
Exercise 6: Tying It All Together Into A Dashboard
We will take our charts and tie them together into a dashboard.
- How do I create a dashboard?
- What’s available to me in Tableau Public, and what might I have to upgrade to access?
Exercise 1: Mapping
Exercise 2: Scatterplot
Exercise 3: Cooking the Books
- Download the dataset: milk-tea-coffee.csv
- Use Quartz Chartbuilder to visualize the data.