Mathematics and Statistics Education
Course design, assessment strategy, and evidence-based instructional practices in quantitative disciplines.
Pavel Shuldiner
Mathematics and Statistics Educator
Email: [email protected]
Currently a sessional lecturer at Western University.
Research
Interests
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.
Courses
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.
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.
Academic Dossier
Teaching Dossier
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