Pursuing his master's in Interdisciplinary Data Science at Duke, Eric lives this subject — from exploratory data analysis and feature engineering to building predictive models and communicating results. His prior role as a data analyst in Puerto Rico means he can connect classroom concepts like regr...

Courage's unusual combination of computer science and environmental science degrees means he's built data pipelines for both software systems and scientific research — two domains where the data looks very different but the analytical thinking overlaps. He teaches students to connect SQL querying, P...

Studying both industrial engineering and statistics gives Juan a natural entry point into data science — he regularly works with regression models, probability distributions, and exploratory data analysis. He unpacks concepts like hypothesis testing, feature selection, and data visualization so stud...

Cleaning messy datasets, choosing the right model, and interpreting results without overfitting — data science lives at the intersection of statistics, programming, and domain knowledge. Anders tackles all three, drawing on his machine learning expertise and daily Python work to teach everything fro...

Cleaning messy datasets is where most data science students lose momentum — missing values, inconsistent formats, and ambiguous features can derail a project before any modeling begins. Bryan brings a computer science engineer's rigor to data wrangling and exploratory analysis, teaching students to ...

Currently pursuing a PhD in Data Science at NYU after completing an M.S. in the field at UIUC, Abhi lives inside the full data science pipeline — cleaning, exploratory analysis, statistical modeling, and machine learning deployment. He teaches students to move from raw data to actionable insight usi...

Studying data science at UW-Madison, Logan lives in the intersection of Python, statistics, and real-world problem-solving every day. He unpacks core concepts like data wrangling with pandas, exploratory visualization, and building predictive models — connecting each tool to the analytical question ...

A software developer with a master's in computer science and an applied math background, Daniel brings both production-level coding skills and statistical grounding to data science concepts like model evaluation, data transformation, and algorithm selection. He teaches Python-based workflows the way...

Firas's postdoctoral research at Princeton sits squarely at the intersection of machine learning and big data — the two pillars of modern data science. He walks students through the full pipeline, from cleaning and exploring datasets with SQL and Python to building predictive models and evaluating t...

Statistical reasoning is the backbone of data science, and Irene's PhD in Mathematics and Computer Science means she can teach the probability, optimization, and quantitative logic underneath the algorithms — not just the syntax for running them. Her deep background in biostatistics, graph theory, a...