Organize and facilitate Data Science meet-ups with a focus on sharing local research and industry work. We aim to build a strong community promoting any data and coding-related work!

Facilitate workshops and tutorials for mathematics, statistics, and computer science undergraduate and graduate courseGraduate courses: MATH403 - Statistical foundations I MATH404 - Statistical learning MATH454 - Survival analysis MATH533 - Generalized linear models SCC461 - R programmingUndergraduate courses: MATH103 - ProbabilityMATH104 - StatisticsMATH105 - Linear algebraMATH230 - Probability MATH330 - Likelihood theory MATH331 - Bayesian statistics MATH333 - Statistical Models MATH336 - Machine learning MATH338 - Time series

MTH380 - Probability and Statistics I: Descriptive statistics. Probability (Laws of probability. Conditional probability. Discrete probability distributions (binomial, hypergeometric, Poisson). Continuous probability distributions, Normal, t-exponential, x². Applications of discrete and continuous distributions. Sampling distributions (sample mean, sample proportion, difference between two samples, difference between two sample proportions). Sampling distribution concerning mean variance and proportion for one or two populations. Estimation for large and small samples. Hypothesis testing concerning mean, variance and proportion for one or two populations, (large samples and small samples) including paired data testing.MTH410 - Statistics: Description of numerical data. Elements of probability theory. Discrete probability distributions (hypergeometric, binomial, geometric and Poisson distribution). Continuous probability distributions; uniform on an interval, Normal distribution, t-distribution, Exponential distribution, x² distribution. Confidence interval and hypothesis testing concerning mean, variance and proportion for one and two populations. F-distribution. Correlation. Simple linear regression (if time permits).

Implementing and back-testing various models for trading operations optimization with a large focus on trade cost modeling and hidden Markov models for time series classification

Spatio-temporal modeling of Personal Attrition Factors for prediction of deaths in India

STA130 - This course, intended for students considering a program in Statistical Sciences, discusses the crucial role played by statistical reasoning in solving challenging problems from natural science, social science, technology, health care, and public policy, using a combination of logical thinking, mathematics, computer simulation, and oral and written discussion and analysis.STA314 - Statistical methods for supervised and unsupervised learning from data: training error, test error and cross-validation; classification, regression, and logistic regression; principal components analysis; stochastic gradient descent; decision trees and random forests; k-means clustering and nearest neighbour methods. Computational tutorials will support the efficient application of these methods.STA302 - Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters, confidence and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for non-linear regression.

Curriculum Assessment - evaluate effectiveness of the Department of Statistics' curriculum in order to provide meaningful updates

Organize and facilitate monthly social or academic events for students and community members

Published concise reviews of news and events within the finance industry with a key focus on insurance related topics (OPP and CPP updates)

Build financial index for alternative assets and basic portfolio structuring

Standardize models across health, dental, and travel products as well as build statistical models to improving pricing procedures of travel products

Leverage extensive experience and training to methodically teach swimmers of all levels (beginner to competitive)

Provide a safe environment for patrons to enjoy recreational aquatic activities

Monitor quarterly performance of funds in order to optimize asset allocation and automate daily asset reconciliation via VBA within Excel

Leverage extensive experience and training to methodically teach swimmers of all levels (beginner to competitive)

Provide a safe environment for patrons to enjoy recreational aquatic activities

Oversee competitions and training sessions of provincial and national level athletes

Perform funding and accounting valuation of both public and private sector pension plans as well as automation of PSPA calculations via an Excel based calculator