Learning Data Visualization with Tableau¶
Learning Objectives¶
By the end of the course, learners are expected to be able to:
Distinguish between good visualizations vs bad visualizations.
Explain how visualizations are important to the data analysis process.
Conduct full exploratory data analysis including visualizations.
Use appropriate plots and graphs to effectively communicate insights from the data with a specified audience.
Create a compelling narrative for your findings using storytelling.
Connect Tableau to different data sources.
Construct maps, distributions, bar and line plots using Tableau.
Identify the different data fields and data types used by Tableau and when to appropriately apply them to different columns.
Class Instructor¶
Name |
website |
|
|---|---|---|
Hayley Boyce |
hayleyfboyce@gmail.com |
Lesson Meetings¶
Details about class meetings will appear here as they become available.
# |
Date - India (IST) |
Date - Vancouver (PST) |
Topic |
Link |
|---|---|---|---|---|
1 |
Jun/25 9:00am - 11ish |
June 24th 8:30-10:30PM |
Introduction to Tableau and Visualization Importance |
|
2 |
Jul/02 9:00am - 11ish |
Jul/01 8:30-10:30PM |
Visualization Types and How to Create them in Tableau |
|
3 |
Jul/09 9:00am - 11ish |
Jul/08 8:30-10:30PM |
Dashboards and Exploratory Data Visualization |
|
4 |
Jul/16 9:00am - 11ish |
Jul/15 8:30-10:30PM |
Good vs Evil, Tableau Stories and Extras |
Class Outline¶
Lesson 1 - Introduction to Tableau and Visualization Importance¶
Theory¶
Why do we visualize?
tables vs graphs
Improvements and effectiveness
Tableau Applications¶
Introduce Tableau examples
History of Tableau
Other Tools available, pros and cons
Where is visualization situated in the grand scheme of data science? Can visualization be an end goal?
Connecting to csv, excel (differences)
Connecting to SQL Server
Introduction to data types (Ordinal, Nominal, Quantitative & continuous, discrete, binary, ordered, unordered, DateTime, text, etc.)
Tableau buttons (show me menu, filter and marks, sorting)
Dimensions and Measures
Discrete vs continuous
Worksheets vs dashboards vs story
Quick viz examples and effectiveness
Aggregate
Drill down, Hierarchies
Maps
Distributing and saving
Exercise¶
Load in the data
Create separate plots using worksheets
Change datatype
Create a bar chart
Create a map
Asking questions to explore
Lesson 2 - Visualization Types and How to Create them in Tableau¶
Theory¶
Common visualization types (bar, line, points, intervals, geo-spatial, chord, etc.)
When do we use what?
Tableau Applications¶
Pie chart
Stacked bars
Side by side bars
Scatter plot
Line graph
Bins - Histograms
Boxplot
Heatmap
Dashboards
Layouts
Changes to all sheets
Connecting sheets
Filtering
Publishing data sources
Exercise¶
Create a histograph
Create a time series
Heatmap
Add to a dashboard
Add filters
Add tooltips and change the layout
Lesson 3 - Dashboards and Exploratory Data Visualization¶
Theory¶
Developing the 6 types of statistical questions (exploratory, descriptive, inferential {frequentist, Bayesian}, causal, mechanistic, predictive)
Why are EDAs important?
How to approach an EDA?
Conclusion and next steps in an EDA
Tableau Applications¶
Dashboards
EDA
Facetting
Trend lines
Forecasting
Calculations breifly
unions joins, relationships.
Chloropleth map
Exercise¶
Dashboard practice
Facetting
Calculations (briefly)
Combine 2 dataframes
Lesson 4 - Good vs Evil, Tableau Stories and Extras¶
Theory¶
What should you absolutely not do? Data Science Ducks
Common pitfalls (8-10 examples)
A game of Good vs Evil
Best practices
Storytelling
Tableau Applications¶
Chloropleth
Calculation function
Sets
Groups
Tooltip
Formatting Axes, Gridlines
Stories
If time:
Clicks and hovers
Palettes
Shapes
Extensions
Navigation