Pavel Shuldiner

Mathematics and Statistics Educator

Email: pavel.shuldiner@gmail.com

Currently a sessional lecturer at Western University.

Interests

Mathematics and Statistics Education

Course design, assessment strategy, and evidence-based instructional practices in quantitative disciplines.

Probability and Data Science

Random graphs, network analysis, graph-based frameworks for exploratory data analysis.

Algebraic Combinatorics

Integer partitions, MacMahon operators, generating series approaches to modeling discrete structures.

Graph Theory

Generalized Johnson graphs and their cliques.

Recently Taught Courses

DS 1000

Winter 2026, Fall 2025

Data Science Concepts

This course introduces students to foundational concepts in data science, focusing on the visualization and analysis of both continuous and categorical data. Concepts covered include data visualization, summary statistics, regression, categorical data analysis, probability, central limit theorem, confidence intervals and experimental design.

Emphasis is placed on practical, data-driven examples to develop independent problem-solving skills and connect theoretical concepts to meaningful analysis through Python.

Course meetings: 2:30 PM – 3:30 PM, NSC 150

Open course page

STAT 230

Winter 2025, Fall 2024

Probability

Introductory probability: sample spaces, independence, conditional probability, Bayes' Theorem, and named distributions (Binomial, Poisson, Normal, etc.). Covers random variables, joint/marginal/conditional distributions, means, variances, covariances, and the Central Limit Theorem.

MATH 239

Spring 2025

Introduction to Combinatorics

Introduction to graph theory: colourings, matchings, connectivity, planarity. Introduction to combinatorial analysis: generating series, recurrence relations, binary strings, plane trees.

Teaching Dossier