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Interactive Data Visualization with Tableau

Interactive Data Visualization with Tableau

This course will teach you how to estimate descriptive quantities and sampling variances from complex surveys, and also how to fit linear and logistic regression models to complex sample survey data.


In this course you will learn about the interactive exploration of data, and how it is achieved using state-of-the-art data visualization software. You will learn to explore a range of different data types and structures, and about various interactive techniques for manipulating and examining data to produce effective visualizations. The learning process is hands-on as students are guided through an analysis of quantitative business data to discern meaningful patterns, trends, relationships, and exceptions that reveal business performance, potential problems and opportunities.

  • Introductory, Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

Students who complete this course will be able to:

  • Apply principles of perception to data visualization
  • Use software tools to interactively visualize relationships among variables
  • Analyze distributions of data visually
  • Use a range of displays to explore data
  • Use parallel coordinate plots, scatterplots, and trellising to analyze multivariate data
  • Visualize hierarchical data with treemap

Who Should Take This Course

Statistical analysts and data miners who need to explore and graph multivariate data, either to form impressions of the data or as a preliminary step to performing statistical tests or building models.

Our Instructors

Ms. Madhuri Maddipatla

Ms. Madhuri Maddipatla

Madhuri Maddipatla is an analytics specialist and problem solver with 10+ years of experience in analytics consulting across multiple domains, including Retail, Consumer Packaged Goods, Healthcare, Finance, Manufacturing, and E-commerce.  Currently a Specialist with McKinsey and Company, she has been an instructor and mentor in the data analytics, data visualization and business consulting space for 6+ years now. She completed her M.S. in Data Science and Business Analytics at the University of North Carolina at Charlotte and worked on several analytics efforts with the industry and in the academic setup. She won several online crowd sourcing analytics contests and is a passionate problem solver and data science mentor.

Course Syllabus

Week 1

  • Information visualization characterization and history
  • Elements of visual perception
  • Software introduction and data preparation (merging data, getting started, export)

Week 2

  • Interaction techniques
  • Distribution analysis
  • Hands-on visual exploration of business data

Week 3

  • Time Series
  • Multivariate views (scatterplots, parallel coordinate plots, trellising)
  • Treemaps for hierarchical data

Week 4

  • Specialized visualizations
  • Video demonstrations of novel techniques
  • From visualization to visual analytics

Class Dates


07/07/2023 to 08/04/2023
11/10/2023 to 12/08/2023


03/08/2024 to 04/05/2024
07/12/2024 to 08/09/2024
11/08/2024 to 12/06/2024


03/07/2025 to 04/05/2025
07/04/2025 to 08/01/2025
11/07/2025 to 12/05/2025


Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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Interactive Data Visualization with Tableau

Additional Information


Homework in this course consists of guided exercises using state of the art software.

In addition to assigned readings, this course also has an end of course data modeling project, and example software files.

Course Text

A recommended text for this course is Now You See It: Simple Visualization Techniques for Quantitative Analysis by Stephen Few.  Note: This text is not available in digital format. For those residing outside the US and not able to purchase this text, you may use The Truthful Art by Albert Cairo instead.


The use of Tableau software is illustrated and access to this program will be provided in the first lesson. Prior experience with Tableau is not expected or required.

Some students also use Spotfire, but it is not available as part of the course.  Want to use R?  Please see   our course: Visualization in R with ggplot2.

Options for Credit and Recognition

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the upper-division baccalaureate degree, 2 semester hours in computer science, computer science systems, or information technology. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

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Register For This Course

Interactive Data Visualization with Tableau