HUJI Statistical Inference Summer School 2025
About the course
Welcome to Statistical Inference, an immersive two-week summer school at HUJI to train students in data science and statistical inference. The summer school runs August 4 though August 14, 2025. This webpage has all of the materials for the summer school.
Personnel
- Course instructor: Justin Bois is a teaching professor in the Division of Biology and Biological Engineering at Caltech, where he teaches a variety of courses, including courses on data analysis in the biological sciences.
- Course coordinator: Anat Kahan is a senior lecturer (assistant professor) in the Department of Animal Sciences at the Hebrew University. Her research focuses on neuronal control of ovarian function.
Course structure and schedule
We meet every day 8:15–12:30 in Etrog Class, Building A (Plant Science, floor –1), for a total of ten days, with each day dedicated to a given topic. Each half-day session is further split into an instructional and practical section. In the instructional section, topics are introduced and discussion in a lecture and/or follow-along format. In the practical sections, students apply the concepts in exercises. Certainly not all of the exercises can be completed in class. We therefore also have exercise help sessions each day 13:30–15:30 in the meeting room of the Animal Sciences Building (floor –1). The topics of the sessions are below.
- Mon, August 4: What are we doing? and Review of basics of Python programming
- Tue, August 5: Data frames
- Wed, August 6: Data display
- Thu, August 7: Probability distributions, sampling therefrom, and the plug-in principle
- Sun, August 10: Nonparametric inference with hacker stats
- Mon, August 11: Generative modeling and null hypothesis significance tests
- Tue, August 12: Parameter estimation
- Wed, August 13: Variate-covariate modeling
- Thu, August 14: Model assessment and statistical watchouts
Copyright and License
Copyright 2025, Justin Bois.
With the exception of pasted graphics, where the source is noted, this work is licensed under a Creative Commons Attribution License CC BY-NC-SA 4.0. All code contained herein is licensed under an MIT license.