Course Notes - IDSC 4210
1 Interactive Data Visualization for Business Analytics
1.1 About the Course
IDSC 4210 is an elective course for the undergraduate Business Analytics minor at the Carlson School of Management. It focuses on the fundamental and widely used exploratory data analysis technique of interactive visualization that is integral to modern business analytics. The key goal of this course is to prepare students for the rapidly changing digital environment faced by companies as it pertains to data-driven decisions. The students will also have hands-on experience with interactive data visualization using modern, state-of-the-art software on real-world datasets.
Explore a set of data and discover meaningful patterns, insights, and questions.
Demonstrate an effective process to explore data using interactive visualization
Given a business objective, describe how data discovery improves decision making or performance.
Identify data types and apply effective analysis based on those data types
Effectively communicate patterns and business insight via data visualization.
Articulate the story the visualization attempts to convey, and the resulting action-oriented decision it informs.
Apply appropriate visualization models to data
Apply effective object/attribute mappings to visualizations
Apply appropriate design principals to create effective visualizations
Develop effective interaction within individual data visualizations and dashboards
- Apply appropriate design principals to create effective user experience
1.2 Design vs. Programming
This course is about both design/communication and programming. You’ve got to have both aspects to be effective in this area. We will gain experience in the programming packages, but there are lots of resources online to help with those.
The specific software package may vary based on where you work or what type of data you’re using, but learning how to use one package will yield skills that help you next time you need to learn a new package. The design and communication fundamentals translate across software and industry.
1.3 Recommended Books
Storytelling With Data (2015) by Cole Nussbaumer Knaflic, ISBN 978-1119002253 (~$17 used)
The Visual Display of Quantitative Information (2001) by Edward Tufte, ISBN 978-0961392147
R for Data Science (2017) by Hadley Wickham and Garrett Grolemund, ISBN 978-1491910399 (Free digital version online)
How Charts Lie (2020) by Alberto Cairo, ISBN 978-0393358421 ($16 new)
The Wall Street Journal Guide to Information Graphics (2013) by Dona Wong, ISBN 978-0393347289
These books have some good things to offer. There are some contradictions in them, but that’s okay! People have different preferences and there’s no perfect answer to some problems. Tastes also change over time. Overall, they generally agree on the big points.