Rob Molinari teaches STAT 4000 while walking students through a projected example at the front of the classroom.
“There’s a low bar at the start, and then it slowly grows to bring everyone along for the more challenging concepts that are toward the end of the course,” he said.
Beyond technical skills, Molinari emphasizes a deeper lesson — learning to reason under uncertainty.
“Data are random. There’s a lot of uncertainty in there, and conclusions you draw from them are uncertain,” he said. “One of the major things we can learn in college is dealing with uncertainty.”
He draws parallels between statistical reasoning and everyday decision making.
“In the long run, you will end up where you’re supposed to be,” he said. “But in the short run, you’re going to have a lot of ups and downs.”
Students say the course delivers both conceptual clarity and practical impact.
Chris Hinkson, now a first-year master’s student in computer science and software engineering, took STAT 4000 during his junior year as an undergraduate. He said the course connected statistical foundations to real machine learning applications.
“STAT 4000 taught me to approach problems in data science and machine learning not just from a computer science perspective but also through the statistical principles and reasoning behind them,” Hinkson said.
Destanie Pitt, a senior mathematics major, said the course aligned directly with her interests.
“I really enjoyed probability and statistics,” she explained. “As a math major interested in work with data, I knew it would be really essential for my career.”
She added the randomly assigned final project strengthened her analytical and teamwork skills. She later applied those skills in the Auburn Business Analytics Case Competition, where she and her partner won using techniques learned in STAT 4000.
Carlos Berrios, who completed his bachelor’s degree in computer science in 2024 and expects to finish his master’s degree in 2026, said the course strengthened his statistical foundation.
“Actually learning and seeing the inner workings of neural networks, rather than just accepting them as magic, was valuable to me,” Berrios said.
For Molinari, helping students understand what lies beneath modern AI tools is central to the course’s purpose.
“It’s really a way to understand how AI works,” he said. “It’s important to understand how to use AI to process information.”
Whether students continue into advanced coursework in statistics and machine learning or apply data skills in other disciplines, Molinari said the goal is consistent: to build informed, critical users of data.
“I think it’s a great way of reasoning with information,” he said. “It’s a good way to teach people how to think.”