Welcome to the first module of IA 342! This week sets the foundation for the entire semester. Before we dive into the concepts, please ensure you have read the Fall 2026 Syllabus.
See the syllabus for grading, attendance, communication, and AI policies.
How does IA 342 connect with the rest of your courses? Our technical curriculum is not a strict sequence where one is a prerequisite for the next. Instead, they are three complementary pillars of the modern analytical workflow:
Collect → Store → Query → Analyze
Explore → Visualize → Interact → Communicate
Interact → Interpret → Augment with AI
With the rapid advancement of Artificial Intelligence, you might wonder:
If AI can analyze data for us, why do humans still need visualization?
The Answer: Because humans still make decisions. AI can summarize text or run regressions, but when it comes to high-stakes intelligence, human analysts and policymakers bear the responsibility of action. Visualization is the bridge between complex data and human cognition.
In this course, we treat visualization as a deliberate analytical pipeline:
Consider Anscombe’s Quartet, a classic concept in data visualization. Imagine four datasets that have nearly identical simple descriptive statistics (same mean, same variance, same correlation).
If you just run a statistical summary or ask an AI to give you the “average” of the data, they look exactly the same:
| Dataset | Mean of X | Mean of Y | Variance of X | Variance of Y | Correlation |
|---|---|---|---|---|---|
| I, II, III, IV | 9.0 | 7.5 | 11.0 | 4.12 | 0.816 |
But the moment we apply Visual Encoding and plot them on a chart, the human eye instantly recognizes patterns that the statistics hid:
Visualization reveals the truth. Statistics alone would never catch the curve, the outlier, or the vertical cluster. This is why the “Visual Encoding” step in our pipeline is not optional — it is the step that enables human cognition to do what AI summaries cannot.
To see the ultimate example of data storytelling, watch Hans Rosling's legendary BBC presentation. He turns 200 years of global health data into a live animated visualization that makes the story impossible to miss.
Our semester is designed to build your skills progressively through industry-standard platforms.
A milestone project applying spatial visualization techniques.
End-to-end visual analytics and dashboard project.
In Phase 1, we focus on geographic context. Esri’s ArcGIS is the industry standard for spatial analysis, but it consists of multiple environments. Here is how they differ:
Note: Week 1 does not require you to install ArcGIS Pro. We will operate entirely in the web browser.
The Week 1 assignment is a simple access check, but it opens the door to powerful spatial analysis. Later in the ArcGIS block, you will use Business Analyst to create: