Statistics for Data Science

10 weeks 8 - 12 hours per week (Please note that this course has been updated from 12 to 10 weeks as of fall 2026*)

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Online learningLive recorded sessions

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Build on the skills you learned in Foundations of Data Science and get the grounding in basic statistics you need to successfully complete your Data Science certificate.

In this course, you will explore probability and descriptive statistics, cover data analysis from both a classical and contemporary viewpoint and learn to extract insight from datasets. you will get a math notation refresher, learn to form hypotheses, design experiments that collect valid data, test your hypotheses using statistical techniques, and work with causal models. Concepts are solidified through individual and group based practical assignments.

This course is developed in partnership with the School of Continuing Studies at the University of Toronto.

Module 1: Introduction	Working with Data: Learn fundamental techniques for handling and manipulating various types of data. Types of Data: Explore different types of data, including qualitative and quantitative, and their applications. Relationships Between Data: Discover how to identify and analyze relationships between different data sets. Sampling Methods: Gain insights into various sampling techniques for data collection. Understanding Bias: Learn how to recognize and mitigate bias in data analysis.
Module 2: Probability	Introduction to Probability: Gain a foundational understanding of probability concepts and their applications. Definitions of Probability: Explore different definitions of probability, including Frequentist and Bayesian perspectives. Random Variables: Learn about random variables and their significance in probability theory. Conditional Probability: Understand conditional probability and how to calculate the likelihood of events based on given conditions.
Module 3: Distribution of Random Variables	Probability Distributions: Learn about probability distributions for both discrete and continuous variables. Central Limit Theorem: Discover the importance of the Central Limit Theorem in understanding data distributions and statistical inference.
Module 4: Introduction to Statistical Inference	Statistical Underpinnings: Understand the foundational concepts that underpin statistical inference. Variability in Estimates: Learn about variability in estimates and its impact on statistical conclusions. Normality: Explore the concept of normality and its relevance to data analysis. Student’s t-Distribution: Understand the Student’s t-distribution and its applications in small sample statistics.
Module 5: Hypothesis Testing	Inference for Categorical Data: Learn methods for making statistical inferences about categorical data, including hypothesis testing techniques.
Module 6: ANOVA, Goodness of Fit, and Bootstrapping	ANOVA: Understand Analysis of Variance (ANOVA) to compare multiple group means and analyze variability. Goodness of Fit: Learn how to evaluate how well a statistical model fits observed data. Bootstrapping: Explore bootstrapping techniques to assess the reliability and variability of sample estimates.
Module 7: Linear Regression	Linear Regression: Gain insights into linear regression for modeling relationships between variables. Assumptions, Limitations, and Strengths: Understand the key assumptions, limitations, and strengths of linear regression analysis. Multiple Linear Regression: Learn about multiple linear regression to model relationships involving multiple predictors.
Module 8: Logistic Regression	Logistic Regression: Explore logistic regression for modeling binary outcome variables and making predictions.
Module 9: Time Series	Analysis and Forecasting of Time Series: Learn techniques for analyzing and forecasting time series data, including identifying trends and seasonal patterns.
Module 10: Introduction to Causal Inference Part 1	Pearl’s Ladder of Causation: Understand Pearl’s Ladder of Causation to distinguish between correlation and causation. Bayesian Networks: Explore Bayesian networks for modeling probabilistic relationships and causality. Randomized Controlled Trials: Learn the principles and importance of randomized controlled trials in establishing causal relationships.
Module 11: Introduction to Causal Inference Part 2	Pearl’s Ladder of Causation: Further your understanding of Pearl’s Ladder of Causation to analyze complex causal relationships. Bayesian Networks: Deepen your knowledge of Bayesian networks for causal inference. Randomized Controlled Trials: Expand on the use of randomized controlled trials in diverse research settings to draw causal inferences.

Business associates, operations managers, project managers, and intelligence analysts.
Finance, securities, and insurance professionals.
Digital marketing and communication specialists.
Professionals from every level or industry who work with analytics or data.

Need to refresh your skills? Check out Python I Need basic coding experience? Check out Data Analytics for Behavioural Insights

Weekly webinars review key concepts and provide an opportunity for live interaction and Q&A with the instructors. The webinars will be recorded so if you are unable to attend you can watch the webinar recording at a time that is convenient to you.

Academic requirements

Required prerequisites:
- Foundations of Data Science OR
- A passing grade on the prior learning assessment, conducted by the University of Toronto, for equivalent skills
A degree in Engineering, Mathematics, or Computer Science is recommended, but not required. Basic knowledge of programming and programming languages is strongly recommended.

System requirements

Anaconda/Jupyter (software that you are required to install)
Waterloo LEARN

Receive a certificate from the University of Waterloo

Upon successful completion of this program, you will receive a professional education certificate from the University of Waterloo.

Headshot of Larry Simon

Larry Simon, MBA

Founder and Managing Director, Inflection Group

Larry Simon is an entrepreneur, management consultant, and angel investor, specializing in IT strategy and data analytics. He has over 30 years of experience advising startups, global corporations, and government institutions. He is the founder and managing director of Inflection Group. Prior to this he was a partner with Ernst & Young Consulting, their CTO and national director of their strategy and delivery centres.

He has previously served on the faculty of the Rotman School of Management, as the head judge of the Canadian Information Productivity Awards (CIPA), and as a councillor of the Institute of Certified Management Consultants of Ontario. Larry holds an MBA from the University of Toronto and a B.Math (Computer Science) from the University of Waterloo.