Missed the workshop? View the recordings below:
Part I: Introduction to Regression Models
Part II: Multivariate Regression Models in Causal Analysis and Machine Learning
Part III: Introduction to Basic Machine Learning Predictions with Python
Part IV: Finding Relationships via Data and Machine Learning Visualization
Statistical and data visualization methods are quickly becoming integral to evidence-based policy making. Given the recent policy moves of data sharing across provinces, which should make more data available for analysis and help provide a more complete picture of public health in Canada, it’s important to be able to understand how to read the data, visualize the data, and share the data in a meaningful way.
Join us for a one-day workshop where you’ll gain awareness of and receive basic training in statistical and data visualization methods. Participants will need to have an understanding of basic statistical concepts, specifically summary sample statistics such as the mean and standard deviation. Knowledge of single variable linear regression models would be useful but not necessary.
Who is this workshop for?
- Designed for individuals in the role of policy, program, research, and business analysts.
- Great for managers who are interested in learning how to apply statistical models in evidence-based decision making.
What you will learn
- Basic and advanced regression models and how such models are used for causal analysis and machine learning.
- How to share findings from complex statistical models by telling stories with data.
- Methods used by computer scientists in basic machine learning analysis and data visualization.
- Basic coding in Python.
Agenda
| 9:00 - 10:30 a.m. | Welcome and introduction to regression models by Professor Anindya Sen |
| 10:30 - 11:00 a.m. | Break |
| 11:00 a.m. - 12:00 p.m. | Use of multivariate regression models in causal analysis and machine learning |
| 12:00 - 1:30 p.m. | Lunch break |
| 1:30 - 3:00 p.m. | Session 1: Introduction to basic machine learning predictions with Python |
| 3:00 - 3:30 p.m. | Break |
| 3:30 - 4:45 p.m. | Session 2: Finding relationships via data and machine learning visualizations using Python |
Meet your presenters
Anindya Sen, PhD
Anindya Sen is the lead author of the Data Analytics for Behavioural Insights Program. Sen is professor of economics, director of the Master of Public Service (MPS) program, and acting associate dean of co-operative education and planning at the University of Waterloo. He is also the director of Computational Data Analytics for the Social Sciences and Humanities (CDASH). Prior to joining the University of Waterloo, Sen was an economist at the Competition Bureau of Canada.
His research focuses on the economics of public policy, with an emphasis on estimating the statistical effects of government intervention and imperfectly competitive market structures. He has published research on the effects of government policies on COVID-19 cases, the relationship between market concentration and gasoline prices, the impacts of higher cigarette taxes on smoking, the effects of higher minimum wages on employment and poverty, and the consequences of incentive programs on electricity usage. These papers have been published in peer reviewed journals such as the Canadian Journal of Economics, Journal of Law and Economics, Journal of Health Economics, Journal of Regulatory Economics, International Review of Law and Economics, Labour Economics, and Canadian Public Policy. His work has been extensively covered by the Globe and Mail, the Financial Post, CBC, and the Toronto Star.
Elias Puurunen
Elias Puurunen is the president of Northern HCI Solutions. He architects big data solutions for Fortune 500 companies, governments, and startups. His data warehouses enable Canada's top high-performance building researchers to produce new electric grid guidance to reduce energy usage and greenhouse gas emissions while maintaining quality of life.
At the height of the 2020 COVID pandemic, Puurunen’s firm designed a personal protective equipment (PPE) test program for the National Research Council of Canada. This program allowed researchers to perform rapid quality assurance verification on PPE. In 2019, Puurunen developed the Coding for Public Policy course (PS 627) for the University of Waterloo's Master of Public Service program. He is also a Microsoft certified solutions expert in data management and analytics, CompTIA certified technical trainer, and holds his Honours Bachelor of Computer Science from the University of Waterloo.