Award-Winning Data Science Tutors
serving Memphis, TN

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.