Mathematics and Statistics Education
Course design, assessment strategy, and evidence-based instructional practices in quantitative disciplines.
Email: pavel.shuldiner@gmail.com
Currently a sessional lecturer at Western University.
Course design, assessment strategy, and evidence-based instructional practices in quantitative disciplines.
Random graphs, network analysis, graph-based frameworks for exploratory data analysis.
Integer partitions, MacMahon operators, generating series approaches to modeling discrete structures.
Generalized Johnson graphs and their cliques.
Courses
DS 1000
Winter 2026, Fall 2025
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
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 graph theory: colourings, matchings, connectivity, planarity. Introduction to combinatorial analysis: generating series, recurrence relations, binary strings, plane trees.
Academic Dossier
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Session active
Full academic CV with teaching history and selected professional activities.
Checking file...Comprehensive teaching portfolio including instructional materials and reflections.
Checking file...Selected student feedback from recent offerings of probability and combinatorics courses.
Checking file...Short classroom demonstration on interpreting and communicating linear-model results.
Checking file...Mini-lesson illustrating leverage and influence in regression diagnostics.
Checking file...Applied data-science example connecting exploratory analysis to model-based decisions.
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