Award-Winning Data Science Tutors
serving Staten Island, 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.

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.

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.

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.

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.

As a dedicated tutor with over 2 years of experience, I am passionate about fostering a supportive learning environment where students can thrive in subjects like Algebra, Business Analytics, and Data Science. Currently pursuing my Bachelor's in Informatics at the University of Washington, I incorporate real-world applications into my teaching to engage students and enhance their understanding of complex concepts. My approach emphasizes personalized learning, encouraging students to ask questions and develop critical thinking skills. I find great joy in witnessing my students' growth and success, and I strive to instill a love for learning that extends beyond the classroom.

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 2015 to work on high-temperature superconductors. I currently work as a Technology Analyst at Accenture. I am also seriously considering whether I should go for a Ph.D. or not.

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.

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.

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.

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.

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 their performance. His industry engineering background means every lesson ties back to how data science actually gets done in production environments.

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.