Award-Winning Data Science Tutors
serving Rochester, NY

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 using tools like Python, SQL, and real-world datasets rather than toy examples.

Sitting at the intersection of CS, statistics, and domain knowledge, data science requires someone who can explain both the math behind a model and the code that implements it. Deeptha's bioinformatics training at UAB was essentially applied data science — cleaning biological datasets, running statistical analyses, and building computational pipelines in Python and R. She tackles everything from exploratory data analysis and visualization to the mechanics of regression and classification algorithms.

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 predictive modeling in ways that connect statistical theory to practical coding workflows.

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 students understand not just the code but the statistical reasoning driving every decision.

Running genomics and biophysics analyses during her PhD at the University of Toronto, Bianca lives the full data science workflow — cleaning messy datasets, applying statistical models, building visualizations, and drawing defensible conclusions. She unpacks concepts like probability distributions, regression, hypothesis testing, and machine learning fundamentals with the rigor of someone who depends on getting them right in published research.

Having built her career at the intersection of computer science and biology — including work in bioinformatics — Samantha understands how to clean, analyze, and draw conclusions from messy real-world datasets. She teaches the full data science pipeline, from exploratory analysis and statistical modeling to visualization and communicating findings clearly.

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 (pandas, NumPy) to the reasoning side, so students understand what their models are actually telling them.

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 regression, classification, and data wrangling to the messy datasets students will encounter in real work.

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 particular emphasis on the mathematical reasoning behind techniques like regression and classification.

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 data to insight.

Ecological research is data science in practice: cleaning messy field data, running statistical models, and visualizing population trends over time. Darcey's graduate work at Auburn involves exactly these tasks, and she walks students through core concepts like data wrangling, exploratory analysis, and interpreting model outputs with real datasets as examples.

A PhD in artificial intelligence from Northwestern means Thomas has spent years knee-deep in the statistical modeling, feature engineering, and machine learning pipelines that form the backbone of modern data science. He teaches students to move from raw datasets to actionable insights using Python, SQL, and the kind of mathematical reasoning — probability distributions, linear algebra, optimization — that makes the difference between running a library and actually understanding what it does.

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 it's designed to answer. That combination of daily practice and tutoring experience means he can translate complex workflows into clear, step-by-step explanations.

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 think critically about their data before jumping to regression or classification.