Award-Winning Statistics Graduate Level Tutors
serving Fresno, CA

Graduate-level statistics demands comfort with proofs and derivations that most intro courses skip — maximum likelihood estimation, Bayesian inference, and the mathematical foundations behind common tests. Brian's Caltech background in economics and computer science gave him deep exposure to these methods in both theoretical and applied contexts, and he breaks down dense notation into intuitive steps.

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.

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 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 moves quickly from probability theory into regression modeling, hypothesis testing frameworks, and ANOVA designs that require both mathematical rigor and software fluency. Juan is completing a statistics degree at the University of Florida alongside his engineering program, so he's immersed in these methods right now — from Bayesian inference to experimental design. His 4.9 rating speaks to how clearly he communicates dense material.

Hi! I'm Alexandre, I am a Machine Learning Engineer, so I write code to make AI do all sorts of stuff everyday. I have degrees in Applied Mathematics and Computer Science so if curious about how an area of math is useful in the real world I'd be happy to give a list of examples!

Graduate-level statistics in the health sciences — biostatistics, survival analysis, logistic regression — requires more than formula memorization; it demands understanding which test fits which study design and why. Julia's Doctor of Science in Pharmacy means she's applied these methods firsthand in clinical research contexts, interpreting p-values and confidence intervals with real patient data on the line. She teaches students to think like researchers, connecting statistical output back to the questions driving the analysis.

Graduate-level statistics throws students into multivariate analysis, hierarchical modeling, and software-driven data work that textbooks alone rarely make clear. Tashina uses MATLAB and Python in her own doctoral research in Psychological and Brain Sciences, so she can walk through both the mathematical theory and the practical implementation side by side. Rated 4.7 by students.

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 often demands fluency with multivariate analysis, mixed models, and experimental design — topics Dan tackled extensively during his Master's in Plant Biology and Conservation, where statistical modeling was central to his research. He breaks down the logic behind tests like ANOVA, regression diagnostics, and maximum likelihood estimation so the methodology clicks, not just the software output. Rated 5.0 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 throws students into the deep end — maximum likelihood estimation, mixed-effects models, Bayesian inference — and expects fluency, not just familiarity. Elliot's PhD in Neuroscience required designing and analyzing complex experimental datasets, so he teaches these methods as tools for answering real research questions. Rated 5.0 by students.

Graduate-level statistics demands comfort with proofs and distributions that introductory courses barely mention — maximum likelihood estimation, Bayesian inference, multivariate hypothesis testing. Channing digs into the mathematical machinery behind these methods, connecting measure-theoretic ideas to practical applications so the theory actually sticks. His broad math background, spanning calculus through biostatistics, keeps the explanations rigorous without losing sight of real-world relevance.