The Vancouver Summer Program with UBC Science provides students with the opportunity to study at a top international research university and experience local Vancouver culture.
What you might expect/course format
Courses with UBC Science include in-class and lab portions taught by UBC faculty members, graduate students, and guest lecturers. Courses also include short field trips and other engaging learning opportunities. Students can expect team-based learning activities and assignments.
July 2026 Course Packages
Foundations of Statistical Reasoning, Exploratory Analysis and Reproducible Research
In this course, students will develop their statistical literacy and reasoning, while also building up practical skills for exploratory data analysis and reproducible research. They will learn to frame research questions, design studies, and collect representative data. Students will gain a fundamental understanding of study design, data exploration, data visualization, and introductory R programming. They will also learn how to organize their analysis projects using reproducible tools such as R JupyterLab for automated analysis reporting. The course learning objectives are: i) build data literacy and statistical reasoning; ii) improve students’ ability to formulate data-driven problems; iii) design studies; iv) perform exploratory data analysis in R; v) produce appropriate visualizations and interpret trends and patterns; vi) write reproducible analysis workflows and clear exploratory reports.
Statistical Inference, Modeling and Foundations of Machine Learning
In this course, students will transition from exploratory data analysis to formal statistical modeling and inference. They will learn how to evaluate the strength of statistical evidence, conduct hypothesis testing, and fit and interpret statistical models. Students will also learn about foundational machine learning concepts, including supervised vs. unsupervised learning, bias-variance trade-off, predictive performance assessment, and high dimensional methodologies. The main learning objectives are: i) conduct statistical inference using t-tests and ANOVA; ii) fit and interpret linear models; iii) evaluate model assumptions and diagnostics; iv) understand and apply basic and high dimensional machine learning methods; v) assess model performance and communicate results clearly.
Prerequisite: Previous experience with coding in R is useful, but not a requirement. Course 1 will start with R basics for any beginners.
Additional information: Please bring your laptop to class, as most in-class work will involve using in Jupyterlab.
For more information
For VSP Statistics and Data Science-specific questions, email Kaique Barros at vsp@stat.ubc.ca.
Program overview video describing the Vancouver Summer Program in Statistics and Data Science at the University of British Columbia, including coursework, projects, and student experience.
Short promotional video introducing the Vancouver Summer Program in Statistics and Data Science at the University of British Columbia, highlighting hands-on learning, team projects, and student experience.
Student testimonials
– VSP STAT Student
– VSP STAT Student
– VSP STAT Student
– VSP STAT Student
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