Award-Winning Statistics Graduate Level Tutors
serving Manhattan, NY

Graduate-level statistics throws curveballs that intro courses never prepare you for — survival analysis, mixed-effects models, high-dimensional inference. Nina earned her master's in biostatistics at Columbia and is currently pursuing her doctorate at NYU, so she's actively immersed in the theory and application behind these methods. She also served as a teaching assistant at Columbia, giving her a sharp sense of where grad students typically get stuck.

Graduate-level statistics demands fluency with proofs and derivations that introductory courses barely touch — moment-generating functions, maximum likelihood estimation, and the theory behind hypothesis testing. Victor's master's in Applied Mathematics gave him direct experience with these topics, and he brings that rigor to sessions while keeping notation and logic organized. He holds a 5.0 client rating.

Graduate-level statistics demands comfort with proofs and distributions that undergraduate courses only sketch — maximum likelihood estimation, sufficient statistics, and the theory behind hypothesis testing. Drisana is actively completing her graduate mathematics degree, so she's immersed in the rigorous thinking these courses require and can unpack dense notation into clear reasoning.

Graduate-level statistics can feel like a different language — survival analysis, multivariate regression, Bayesian inference — especially for students outside traditional math backgrounds. Kimberly runs these methods daily in her Columbia MPH program, where biostatistics is central to public health research. She breaks down the logic behind each technique so students can apply it confidently in their own coursework and thesis work.

I am highly praised by my students and supervisors. Even today I still kept the communication with many students.

Graduate-level statistics demands comfort with mathematical proofs and distribution theory that undergraduate courses barely touch — moment-generating functions, maximum likelihood estimation, Bayesian inference. Sabry's Ph.D. training in Chemical and Biomolecular Engineering required rigorous statistical modeling for experimental data, so he approaches these topics as tools with real stakes, not just textbook exercises. He's particularly effective at connecting abstract theory to applied research contexts.

I am currently finishing my thesis. For the past two years I was an adjunct instructor at The City College of New York, teaching statistics and introductory neuroscience, where I learned the importance of communicating complicated concepts clearly at an individualized level. All of my classes performed above average, and I discovered how satisfying it is to help people understand difficult ideas. I've found that by creating a good rapport with my students I am able to more effectively impart difficult concepts to them while causing them less stress. My passion is people, which first led me to study psychology, leading to my work in statistics, and later into teaching.

Graduate-level statistics demands fluency with topics like maximum likelihood estimation, multivariate distributions, and regression diagnostics that go well beyond introductory coursework. Dana holds a degree in statistics and is pursuing PhD-level economics research involving econometrics, so she's actively working with these methods. She unpacks the mathematical theory behind statistical procedures while keeping the applied interpretation clear.

Graduate-level statistics demands comfort with concepts like maximum likelihood estimation, Bayesian inference, and hypothesis testing frameworks that go well beyond intro courses. Abhi's M.S. in Data Science from UIUC and current PhD work at NYU mean he uses these tools daily in research — he teaches the theory alongside the practical intuition for when and why each method applies.

Graduate-level statistics demands comfort with concepts like maximum likelihood estimation, ANOVA designs, and regression diagnostics that go well beyond introductory coursework. Sasha's training in both biology and science education gives her a practical lens for these methods — she connects statistical theory to real research applications, which is especially useful for grad students analyzing their own data.

Graduate-level statistics demands more than plugging numbers into SPSS — it requires understanding why you'd choose a hierarchical regression over a standard multiple regression, or when MANOVA assumptions break down. James applies these methods daily in his Ph.D. program in School Psychology at the University of Arizona, where his own research involves complex statistical designs. Rated 5.0 by students, he walks through everything from structural equation modeling to effect size interpretation.

Graduate-level statistics demands comfort with proofs and derivations that undergraduate courses often skip — moment-generating functions, maximum likelihood estimation, and the theoretical machinery behind hypothesis testing. Aaron brings a PhD mathematician's rigor to these topics, walking through measure-theoretic ideas and convergence arguments with the care they require.

Graduate-level statistics in medical and biomedical research relies heavily on survival analysis, logistic regression, and interpreting multivariate models — all tools Elise used extensively through her M.D. training at Creighton. She breaks down the reasoning behind test selection (why a Cox model instead of a chi-square, for instance) so the methodology clicks rather than just the formulas.

Graduate-level statistics throws curators of data into the deep end — multivariate analysis, mixed-effects models, Bayesian inference — and Michael's biology master's work required him to live in that world daily. He teaches the logic behind each method so students can choose the right test for their research design, not just run code blindly. Rated 5.0 by students.

Graduate-level statistics demands comfort with proofs, distributions, and inference methods that go well beyond intro courses. Dana's Master's in analytics from Georgia Tech and her current PhD research in economics give her deep fluency with topics like maximum likelihood estimation, hypothesis testing frameworks, and regression theory. Rated 4.8 by students, she brings both the mathematical rigor and the applied intuition this level requires.

Graduate-level statistics becomes far more intuitive when the examples come from real research — and Nicholas has applied multivariate analysis, mixed models, and hypothesis testing frameworks extensively throughout his genetics PhD work. He breaks down concepts like maximum likelihood estimation and ANOVA designs by tying them to the kinds of messy, real-world datasets students actually encounter in their own programs. Rated 4.8 by students.

Graduate-level statistics demands comfort with concepts like hypothesis testing, regression modeling, and ANOVA that go well beyond intro courses. Dillon's engineering background — including a master's in welding engineering technology — required heavy applied statistics work, from designing experiments to interpreting multivariate data in real research contexts. He teaches the reasoning behind each method so students can choose and defend the right analytical approach.

I'm a graduate student in Applied Statistics with experience in general math and computer programming to boot. I currently have a PhD in Applied Statistics with experience in data analytics. I've had the opportunity to do some math and statistics work for hospitals and research facilities, and I enjoy getting to share my passion for math with others to assist them in their education.

Holding a Master's in Statistics, Adam digs into the graduate-level material that trips students up most — maximum likelihood estimation, Bayesian inference, multivariate distributions, and the theoretical underpinnings of hypothesis testing. He's taught and tutored across university settings, so he knows how to bridge the gap between abstract proofs and applied problem sets. Rated 4.9 by students.

I am a Molecular Engineering major at the University of Chicago, I am currently taking time off to focus on other aspects of my career but I don't want to stop tutoring outside college campus!. I am a child of immigrants and have spent my life tutoring my siblings and younger students, and I loved working with them! See y'all in class!

Graduate-level statistics demands more than plugging data into software — it requires understanding why a likelihood ratio test applies in one scenario and a Wald test in another. Mayuri's PhD in Physics meant designing experiments and running advanced statistical analyses firsthand, from Bayesian inference to multivariate regression. She breaks down the mathematical derivations behind each method so the theory clicks alongside the application.

Graduate-level statistics demands fluency with techniques like regression modeling, ANOVA, and hypothesis testing frameworks that go well beyond introductory coursework. Scott applies these methods actively in his doctoral research at NYU, where he analyzes complex datasets on human development — so he can walk through both the theory and the practical decision-making behind choosing the right analysis for a given research question.