Understanding how people think, behave, and make decisions in everyday life is critical for innovating or improving programs and services across any organization. This eight-week course teaches you the multivariate regression models, forecasting, and data analysis tools needed for effective, well-informed decision-making. Establish market-ready skills in modelling and predictive analytics and answer important questions to lead innovation, decision-making, and policy development within your organization.
This course is offered individually, or as part of the three-course Data Analytics for Behavioural Insights certificate program. You will receive a certificate for each individual course you successfully complete, and then a certificate upon successful completion of the entire program. If you have successfully completed the first two courses in the Data Analytics for Behavioural Insights certificate program, you will have background knowledge that will be helpful for this course. Prior knowledge of basic statistics and coding using R is required.
- Policymakers: Do you develop policies for any public service sector (e.g., health care or municipalities)? Understand how modern data analytics techniques can better inform your decisions.
- Public servants, non-profit and community leaders: Are you working in public service or leading a business improvement area, non-profit, or public office? Gain behavioural insights that can be used to inform your work, improve your community, and increase your chances of success when competing for funding.
- Public service organizations: Do you primarily serve the public sector or the general public? Elevate your understanding about your customers and partners and learn to make more informed decisions that better serve public interest.
- Explore the most relevant statistical modelling techniques used in today’s market.
- Construct scripts in R to execute intermediate statistical programming and commands.
- Draw behavioural insights from data analytics to drive decision-making.
- Use intermediate statistical methods to draw conclusions and inform public policy development and decision-making.
- Employ multivariate regression models to extract behavioural insights and forecasting from public data.
- Interpret and communicate findings from complex statistical outputs to colleagues and management.
NOTE: Some prior knowledge of R and statistics is recommended before taking this course. Don't have background in R or statistics? Check out our "Basics of Modern Statistics and Analysis" course.
- Instruction from professional data experts in a course designed by University of Waterloo faculty.
- Weekly live Q&A sessions via Zoom on Fridays from 10 - 11 a.m. ET (also available as recordings).
- Opportunities to discuss your learning with others in the course.
- Curated case studies and content based on Canadian public data.
- Five to seven hours of online learning and independent work each week (including live sessions, graded assignments, and activities).
- Regular quizzes to test your knowledge throughout the course.
- Capstone project that integrates your learning.
- Attendance requirement: It is highly recommended that participants attend live sessions, but it is not required. Live sessions will be recorded and for all participants to view.
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.
Andrés Arcila-Vasquez, PhD
Senior Research Data Scientist, AB InBev
Lecturer, Economics
Andrés Arcila-Vasquez is senior research data scientist at AB InBev. His work specializes in economic experimentation and causal inference, applying advanced statistical and econometrics techniques like Randomized Control Trials, Matching Estimators, Difference-in-Difference, and Instrumental Variables to estimate average treatment effects of policy and business interventions.
Prior to joining AB InBev, Arcila-Vasquez worked at TD Bank as a quantitative associate in the Risk Management department developing, validating, and managing statistical models to estimate probability of defaults. He holds a Ph.D. in Economics from the University of Waterloo, specializing in policy evaluation and applied economics. He is also a sessional lecturer with Waterloo’s Faculty of Arts, where he has taught undergraduate and graduate economics and econometrics courses.