Welcome to INFO 3312/5312

Lecture 01

Dr. Benjamin Soltoff

Cornell University
INFO 3312/5312 - Fall 2026

August 25, 2026

Announcements

Learning objectives

Introduce the course and its structure
Identify the course learning objectives and how we will learn to visualize data
Review course policies
Discuss hopes and dreams for the course
Introduce the grammar of graphics

Students on the waitlist

INFO 3312/5312 enrollment is restricted to IS/ISST majors and IS MPS students
If you are not an IS/ISST major (or are still in the process of affiliating), students are admitted through the waitlist in Student Center
PINs distributed on a rolling basis
As of August 25 (11:50am):
- INFO 3312: -5 seats available and 3 on the waitlist
- INFO 5312: 9 seats available and 3 on the waitlist

Introductions

Meet the instructor

Dr. Benjamin Soltoff

Associate Teaching Professor in Information Science

Director, Online Master’s Program in Artificial Intelligence

CIS Building 284

Headshot of Dr. Benjamin Soltoff

Meet the course team

	Name	Role(s)
	Daye Kang	PhD TA
	Ke Li	PhD TA

Meet each other!

Activity

Form a small group (3-4 individuals) with people sitting around you
First, introduce yourselves to each other:
- Your name - Prof/Dr. Soltoff
- Your major - Political science
The last movie you saw - The Sheep Detectives
What you hope to get out of this class - a job?
Start with bad graphs – Share your examples of “bad” graphs and why you think they’re bad.
Then, share good graphs – Same deal, share your examples of “good” graphs and why you think they’re good.
Finally, share your graphs on Canvas as comments on this discussion post.

05:00

Themes: what, why, and how

What: the communication (e.g. plot, table, report)
- Specific types of visualizations for a particular purpose (e.g., maps for spatial data, Sankey diagrams for proportions, etc.)
- Tooling to produce them (e.g., specific R packages)

How: the process
- Start with a design (sketch + pseudo code)
- Pre-process data (e.g., wrangle, reshape, join, etc.)
- Map data to aesthetics
- Make visual encoding decisions (e.g., address accessibility concerns)
- Post-process for visual appeal and annotation

Why: the theory
- Tie together “how” and “what” through the grammar of graphics
- Extend to underlying theory of cognition and information processing

Course components

Homepage

https://info3312.infosci.cornell.edu/

All course materials
Links to Canvas, GitHub, Posit Workbench, etc.
Let’s take a tour!

Course toolkit

All linked from the course website:

GitHub organization: github.coecis.cornell.edu/info3312-fa26
Positron
- Use the Workbench: posit-workbench.infosci.cornell.edu
- Or install it locally
🤖 CayugaViz
Communication: GitHub Discussions
Assignment submission and feedback: Gradescope

Important

Make sure you can access Positron before Friday.

Activities: Prepare, Participate, Practice, Perform

Prepare: Introduce new content and prepare for class by completing the readings/activities
Participate: Attend and actively participate in class and office hours
Practice: Practice applying visualization techniques with application exercises during class, graded for completion
Perform: Put together what you’ve learned to analyze real-world data
- Homework assignments x 6-ish
- Projects (3) – written and oral components
- Quizzes (2)

Grading

Category	Percentage
Project 1	15%
Project 2	20%
Project 3	20%
Quizzes	20%
Homework	15%
Application exercises	10%

See course syllabus for how the final letter grade will be determined.

15 minute rule

Support

Attend office hours
Ask and answer questions on the discussion forum
Reserve email for questions on personal matters and/or grades
Read the course support page

Diversity + inclusion

I want you to feel like you belong in this class and are respected
We are committed to full inclusion in education for all persons
If you feel that we have failed these goals, please either let us know or report it, and we will address the issue

Accessibility

I want this course to be accessible to students with all abilities. Please feel free to let me know if there are circumstances affecting your ability to participate in class.

Course policies

As long as you meet
the prereqs

Prerequisites

INFO 2950/2951 or INFO 5001
Prior experience with R and Git is required

Ideally you took INFO 2950/2951 or 5001 with me.

If not, you need a firm understanding of R (including {tidyverse}) and Git workflows.

Late work, waivers, regrades policy

We have policies!
Read about them on the course syllabus and refer back to them when you need it

Tech-lite classroom

Unless explicitly required for an in-class activity, laptops and other devices should be put away during class. This is to ensure that you are fully engaged in the course material and to minimize distractions for yourself and your classmates.

If you have a specific need to use a device during class, please let me know.

Can I use Claude/ChatGPT/etc?

Need to master the underlying fundamentals and methods of data communication
AI as a coding assistant is fine as long as you are in control
Cannot just rely on AI to do all the work for you
- At best, you have not met the learning objectives
- At worst, you end up with AI slop
AI continues to get more expensive
Bottom line – employers are hiring you, not Claude

So can I use Claude/ChatGPT/etc?

Animated GIF of a character from 'Schitt's Creek' saying 'A simple yes or no is fine'

Generative AI usage policy

Use generative AI to facilitate, rather than hinder, learning
✅ GAI tools for reference purposes

How do I make a scatterplot using ggplot2 in R?
✅ GAI tools for writing my code
- You may use GAI tools to assist in writing code in this class
- You may not make use of the technology as a substitute for critical thinking
- Oral assessments will include being able to explain your code and the reasoning behind it
❌ GAI tools for design and narratives
You are ultimately responsible for the work you turn in; it should reflect your understanding of the course content