ISOM 675 · Data Visualization

Course information, schedule, and materials for ISOM 675 Data Visualization.

ISOM 675 · Data Visualization

Course

ISOM 675, Fall 2026

Instructor

Prasanna Parasurama

TA

Kevin Sheng

Class meetings

MoWe 11:30 AM–12:45 PM · GBS 201

Course home

This site + Canvas

Syllabus

View syllabus →

This course will introduce students to the techniques and tools used to create effective visualizations that clearly and efficiently communicate relationships within data. Students will learn how to perform exploratory analysis through visualization, how to create professional looking visualizations for use in business reports and presentations, and how to design interactive visualizations and dashboards that include time series, rankings, proportions, deviations, frequencies and distributions, correlations, categorical comparisons, and geospatial plots. Students will analyze real data sets and utilize Python, R, Tableau, and other tools to design and prototype their visualizations. This course is for MSBA students only.

Schedule

This is a living document for all course activities, and will be updated and modified as we go along.

Week	Date	Topic	Due
Week 1	Wed, Aug 26	00 · Class Intro
Week 2	Mon, Aug 31	01 · Fundamentals of Data Visualization	Few, Show Me the Numbers, Chapter 5 (Canvas) Healy, Chapter 1: “Look at Data”
	Wed, Sep 2	01 · Fundamentals of Data Visualization	Few, Show Me the Numbers, Chapter 6 (Canvas)
Week 3	Mon, Sep 7	Labor Day — no class
	Wed, Sep 9	01 · Fundamentals of Data Visualization
Week 4	Mon, Sep 14	01 · Fundamentals of Data Visualization	Few, Show Me the Numbers, Chapter 2 (Canvas)
	Wed, Sep 16	02 · Python/Pandas/Altair Primer	Introduction to Altair Data Types, Graphical Marks, and Visual Encoding Channels
Week 5	Mon, Sep 21	03 · Tableau Primer Module 1 Quiz (in class)
	Wed, Sep 23	04 · Exploratory Visual Analysis
Week 6	Mon, Sep 28	04 · Exploratory Visual Analysis
	Wed, Sep 30	04 · Exploratory Visual Analysis
Week 7	Mon, Oct 5	04 · Exploratory Visual Analysis
	Wed, Oct 7	05 · Visualization for Model Evaluation	Python project due
Week 8	Mon, Oct 12	Fall break — no class
	Wed, Oct 14	06 · Visualization for Communication
Week 9	Mon, Oct 19	06 · Visualization for Communication
	Wed, Oct 21	06 · Visualization for Communication
Week 10	Mon, Oct 26	06 · Visualization for Communication
	Wed, Oct 28	06 · Visualization for Communication
Week 11	Mon, Nov 2	Midterm exam
	Wed, Nov 4	06 · Visualization for Communication
Week 12	Mon, Nov 9	06 · Visualization for Communication
	Wed, Nov 11	06 · Visualization for Communication
Week 13	Mon, Nov 16	07 · Interaction and Dashboard Design
	Wed, Nov 18	07 · Interaction and Dashboard Design
Week 14	Mon, Nov 23	07 · Interaction and Dashboard Design
	Wed, Nov 25	Thanksgiving break — no class
Week 15	Mon, Nov 30	07 · Interaction and Dashboard Design
	Wed, Dec 2	07 · Interaction and Dashboard Design
Week 16	Mon, Dec 7	Tableau project presentations
	Wed, Dec 9	Review
Final	Fri, Dec 11	Final exam · 11:30 AM–2:30 PM

Course materials

Module 00 · Class Intro

Lecture slides 1

• Class Intro Introductions, course goals, tools, grading, assessment expectations, and AI-use policy.

Module 01 · Fundamentals of Data Visualization

Focus: definitions, viewer tasks, visual encoding, perception, Gestalt grouping, and misleading design.

Lecture slides 3

• Lecture 01: What Is Data Visualization? Definitions, purposes, and viewer tasks.
• Lecture 02: Perception and Cognition Visual channels, attention, grouping, and memory.
• Lecture 03: Bad Practices Misleading scales, dimensions, and decoration.

In-class activity 2

• Graphical Perception Lab Estimate values and compare the accuracy of different encodings.
• Fix the Chart Diagnose and redesign misleading charts. Answer key slides.

Required readings 4

• Stephen Few, Show Me the Numbers, Chapter 5 (Canvas).
• Stephen Few, Show Me the Numbers, Chapter 6 (Canvas).
• Kieran Healy, Data Visualization: A Practical Introduction, Chapter 1: “Look at Data.”
• Stephen Few, Show Me the Numbers, Chapter 2 (Canvas).

Quiz 1

• Module 01 quiz on perception, encoding, grouping, and misleading design. Complete it in Canvas.

Module 02 · Python/Pandas/Altair Primer

Tutorials 3

• Introduction to Altair
• Data Types, Graphical Marks, and Visual Encoding Channels
• Pandas/Altair Primer (Jupyter notebook)

Dataset for the third tutorial: Netflix titles workbook (.xlsx). Save it as datasets/netflix_titles.xlsx relative to the notebook.

Python environment setup and notebook instructions (Canvas).

Module 03 · Tableau Primer

Module 04 · Exploratory Visual Analysis

Module 05 · Visualization for Model Evaluation

Module 06 · Visualization for Communication

Module 07 · Interaction and Dashboard Design