Award-Winning Data Science Tutors
serving Buffalo, 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.

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, and applied math translates directly into the kind of analytical thinking that separates someone who understands a model from someone who just calls a library function. Rated 4.9 by students.

Between his economics degree, math minor, and electrical engineering minor at UT Austin, Sameeullah built fluency across the three pillars data science actually rests on: statistical reasoning, programming (Python, SQL, R), and quantitative modeling. He teaches students how econometrics techniques like regression and hypothesis testing translate directly into the data science toolkit — making the leap from academic datasets to real analytical workflows feel natural rather than overwhelming.

A strong math foundation changes everything in data science, where statistical reasoning and algorithmic thinking matter more than just knowing a library's syntax. Joseph pairs his University of Chicago math degree with fluency in Python to teach concepts like data wrangling, exploratory analysis, and basic modeling from the ground up.

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.

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 students understand not just how to run an analysis but why each step matters for drawing valid conclusions. Rated 4.9 by students.

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.

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.

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 he'd architect them professionally — emphasizing clean, reproducible code and understanding why a particular approach fits the problem. Rated 5.0 by students.

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.

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 process, walking students through how to move from a vague question to a structured dataset to a defensible conclusion.

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.

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.