UT-Austin iSchool Syllabus
I306
Statistics for Informatics
Spring 2026
Description (from the catalog)
Restricted to informatics majors and students pursuing the informatics minor. Examine fundamental principles of probability and statistics. Cultivate an understanding of descriptive and inferential statistics. Conduct and interpret statistical analyses using statistical analysis software, and apply these analyses to common issues in informatics. Three lecture hours a week for one semester. Offered on the letter-grade basis only.
Quantitative Reasoning Flag
This course carries the Quantitative Reasoning flag. Quantitative Reasoning courses are designed to equip you with skills that are necessary for understanding the types of quantitative arguments you will regularly encounter in your adult and professional life. You should therefore expect a substantial portion of your grade to come from your use of quantitative skills to analyze real-world problems.
More information is available at quantitative reasoning flag.
Course Number
28130
Prerequisites
None, but you should have a strong command of College-level algebra. Past programming experiences will help.
Time
Tuesdays and Thursdays 3:30PM- 5:00PM
Place
UTC 1.118
Dates
January 13–April 23, 2026
Instructor
Nathan TeBlunthuis
nathante@utexas.edu
Office
1616 Guadalupe St, Room 5.434
Office Hours
Mondays 12:00pm-1:00pm and Fridays 1:00pm to 2:00pm or by appointment. I hold office hours in my UTA office or Zoom. Make reservations for my office hours by editing this Wiki page. We’ll meet in my jit.si office.
Course Website
The course website is https://pages.ischool.utexas.edu/i306StatisticsForInformatics/.
Schedule Overview
| Week | Dates | Weekly Topic | Tuesday | Thursday |
|---|---|---|---|---|
| 1 | Jan 13-15 | Introduction | Course intro & fundamentals | R setup & data basics |
| 2 | Jan 20-22 | Data & Sampling | Sampling & experiments | Numerical data & visualization |
| 3 | Jan 27-29 | Categorical Data | Categorical data & independence | Quiz 1; Data viz in R |
| 4 | Feb 3-5 | Probability | Probability fundamentals | Conditional probability & Bayes |
| 5 | Feb 10-12 | Distributions | Discrete distributions | Normal distribution |
| 6 | Feb 17-19 | Central Limit Theorem | Law of Large Numbers & CLT | Quiz 2; Normal distribution lab |
| 7 | Feb 24-26 | Inference Foundations | Confidence intervals | Hypothesis testing (proportions) |
| 8 | Mar 5, 10 | Chi-Square & t-Tests | Chi-square tests | Quiz 3; t-distribution & t-tests |
| 9 | Mar 12 | Power & Corrections | Statistical power & multiple comparisons | — |
| Mar 14-22 | Spring Break | |||
| 10 | Mar 24-26 | ANOVA & Correlation | ANOVA | Quiz 4; Intro to regression |
| 11 | Mar 31 - Apr 2 | Simple Linear Regression | Least squares regression | Regression inference |
| 12 | Apr 7-9 | Multiple Regression | Quiz 5; Intro to multiple regression | Confounders & collinearity |
| 13 | Apr 14-16 | Logistic Regression | Intro to logistic regression | Logistic regression interpretation |
| 14 | Apr 21-23 | Course Conclusion | Quiz 6; Project presentations | Project presentations |
Detailed Schedule
The course schedule contains topics, readings, video lectures, activities for each class session, and assignments.
Setup
See the setup instructions for installing R, RStudio, and required packages. Attempt the setup instructions before our first class.
Materials
Our primary textbook is the freely available Diez, Çetinkaya-Rundel, and Barr (2019).
This course also uses a number of other resources in whole and part including video lectures from OpenIntro, Nick Huntington-Klein, Khan Academy, StatQuest, 3Blue1Brown, and JBStatistics, in-class activities from Gelman and Glickman (2000), Morrell and Auer (2007), and Burcu Eke Rubini, and tutorials from OpenIntro and AppliedStatsInteractive.
The freely downloadable Wickham, Çetinkaya-Rundel, and Grolemund (2023) is recommended for learning R.
A more advanced textbook on statistics, machine learning, and R is the freely available Kuhn and Silge (2022), the full text of which is available at tmwr.
You may also find the study guide from past editions of this course useful.
Learning Outcomes
- Learn to describe data using statistics and contingency tables to summarize
- Learn to use probability distributions
- Learn to visualize data
- Learn to develop confidence intervals
- Learn to conduct hypothesis tests
- Learn to conduct single and multiple regression and logistic regression
- Learn to write reproducible reports
Class Format
The class will primarily be a “flipped” classroom.
You will prepare for class by reading from the textbook, watching lecture videos published by OpenIntro, and attempting the exercises from the textbook section. Thinking through the exercises is at least as important as reading the text to our mathematical and technical learning goals. Doing textbook exercises is the best way to prepare for the quizzes.
Each even-numbered exercise in the textbook is similar to an odd-numbered exercise, and Appendix A in the back of the textbook has solutions to the odd-numbered exercises.
I will not grade these exercises, and I don’t expect you to always be able to solve them on your own before class. We will have time in class to answer your questions and work through textbook exercises.
Assignments
Credit for this class comes from quizzes, homework and in-class participation, and a semester project.
Attendance
Participation in in-class activities and assignments is graded and constitute 40% of your grade. I expect you to attend each class and participate in activities. If you do not attend class it will be difficult for most of you to learn the material needed to succeed on quizzes and the semester project.
Grading
I plan to grade assignments within two weeks of their due date except where circumstances interfere. The grading scale used along with the grade components follow.
| Letter Grade | Percentage Range | Points |
|---|---|---|
| A | 94% and above | 940+ |
| A- | 90% – 93.9% | 900 – 939 |
| B+ | 87% – 89.9% | 870 – 899 |
| B | 83% – 86.9% | 830 – 869 |
| B- | 80% – 82.9% | 800 – 829 |
| C+ | 77% – 79.9% | 770 – 799 |
| C | 73% – 76.9% | 730 – 769 |
| C- | 70% – 72.9% | 700 – 729 |
| D | 60% – 69.9% | 600 – 699 |
| F | Below 60% | Below 600 |
Note that project work requires the submission of a .qmd file and a .html file. Omission of either will result in no credit for the work.
There are a total of 1010 available points (including 10 extra credit) in the class.
Quizzes (400 points, 40%)
Six in-class quizzes throughout the semester. The quizzes are cumulative, covering all material up to that point. However, they tilt quite heavily (quiz 6 less so) toward new material. You may use a 3.5” × 5” index card of handwritten notes.
| Quiz | Topic | Points |
|---|---|---|
| Quiz 1 | Data Types & Visualization | 63 |
| Quiz 2 | Probability, Distributions, CLT | 63 |
| Quiz 3 | CI, Hypothesis Testing, Chi-Square | 63 |
| Quiz 4 | ANOVA | 63 |
| Quiz 5 | Simple Linear Regression | 63 |
| Quiz 6 | Multiple & Logistic Regression | 85 |
Homework & Participation (400 points, 40%)
In almost all class sessions, we will have in-class activities, such as demonstrations, interactive tutorials, programming exercises, and worksheets. Participation points are earned by attending class engaging with activities, and completing tutorials, exercises, and worksheets.
Semester Project (200 points, 20%)
We will have semester-long project where you choose a dataset and apply the statistical methods learned in class. Milestone assignments scaffold the project. You will use Quarto to create reproducible reports.
| Assignment | Points |
|---|---|
| Milestone 1: Dataset Selection & EDA | 40 |
| Milestone 2: Data Visualization | 40 |
| Milestone 3: Statistical Analysis | 40 |
| Final Report | 50 |
| In-class Presentation | 30 |
University Policies and Resources for Students Canvas Page
Review this Canvas page. It is a part of all UT syllabi (including this one) and contains University policies and resources that you can refer to as you engage with and navigate your courses and the university.