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...

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 ...

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...

Penn's computer science program gave Patrick a solid grounding in the algorithmic and mathematical thinking that data science actually demands — probability, linear algebra, and the logic behind model selection. He teaches Python and SQL not as isolated tools but as parts of a larger analytical proc...

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...

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...

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...

I am a recent graduate with a master's in electrical engineering from Case Western Reserve University. I won the Bill and Melinda Gates Millennium Scholarship which covers full tuition up to Ph.D. I was on the Dean's List for three consecutive years. Additionally, I won the OZY Media Genius Award in...

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...

A PhD in statistics and hands-on experience with R programming and SPSS give Bahaeddine a strong grip on the full data science pipeline — from exploratory analysis and probability modeling to regression and visualization. He breaks down concepts like feature selection, hypothesis testing, and predic...

Sitting at the intersection of his mathematics degree and computer science certificate at UT Austin, data science is where Austin's two disciplines converge. He digs into the full pipeline — cleaning data in Python or R, exploratory analysis, statistical modeling, and interpreting results — with par...

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...

A Mathematical Statistics and Probability major at Smith College, Kate brings real fluency with the quantitative backbone of data science — probability distributions, hypothesis testing, and exploratory data analysis. She breaks down concepts like regression modeling and data visualization so studen...

Mehek's computer and information sciences training gives her a programmer's instinct for data science — she teaches students to clean messy datasets, write Python scripts for exploratory analysis, and interpret statistical outputs rather than just running them. Her approach connects the coding side ...

Econometrics is essentially data science applied to economic questions — regression modeling, hypothesis testing, cleaning messy datasets to find real signals. Chica brings that statistical rigor to lessons on R programming, exploratory data analysis, and building reproducible workflows from raw dat...