Daily Schedule
The bootcamp will take place in room 162 on the 1st floor of Olmsted Hall of Biological Sciences. Breakfast and lunch will be provided on site. The schedule for each day will be:
- 8 AM - 9 AM | Breakfast
- 9 AM - 12 PM | Lesson
- 12 - 1 PM | Lunch
- 1 - 2 PM | Discussion
- 2:15 - 5 PM | Activity
Each day of the bootcamp, the learning material will be distributed to each participant in our GitHub organization .
Technical Setup Before the Bootcamp
Sign up for GitHub Sign up for a free GitHub account if you don’t already have one. Make sure to pick a username that you feel comfortable sharing on your CV. You will most likely use your GitHub username for many years in your data science career. Fill out this survey to let us know what your username is. Many of you have already done this. Using these usernames, we will send out an invitation to you become a member of the GitHub organization
bayes-batsplease accept this invitation. If you have not received your invitation please send a gentle reminder to Szofia on Slack.Install R Yes, you do need to download and install R even if you have downloaded before. There is a newer version.
Install RStudio Yes, you do need to download and install RStudio even if you have downloaded before. There is a newer version. Download the free Desktop version.
If you run into any issues about the tech setup please feel free to let us know in advance. You can also feel free to ask questions on the #bootcamp channel on Slack.
Day 0 part 1 - Introduction to the Toolkit
Videos to be watched before the bootcamp. If you run into any issues about this content please let us know in advance. You can feel free to ask questions on the #bootcamp channel on Slack.
| Topic | Materials |
|---|---|
| Overview | |
| hello woRld | |
| Introduction to Quarto | |
| Git/GitHub | |
| ggplot | |
| Data Wrangling | |
| Workflow |
Day 0 part 2 - Probability and Statistics Review
Topic 1: Conditional probability
- Overview: Conditional probability is a concept key to understanding Bayes’ theorem and its application.
- We love the description of the concept at here, which was inspired by this.
The next four topics are based on the open access intro statistics textbook OpenIntro IMS. OpenIntro IMS has an emphasis on simulation-based inference, and hence you will see terms such as bootstrap in many chapters (e.g., Chapters 11 and 12). For the purpose of preparing for Bayes BATS bootcamp, we will focus on Central Limit Theorem-based inference, also called mathematical modeling by OpenInro IMS, as in Chapter 13. As you go over Chapters 14 and 16, you will encounter bootstrap methods and don’t worry if you don’t have the time to fully digest these portions. Focus on the mathematical modeling approaches in Chapters 14 and 16 will suffice.
Topic 2: Chapter 7 Linear regression with a single predictor link
- Overview: This chapter is to help you with Day 4 materials of Bayes BATS bootcamp.
- Exercises: 7, 9, 19, 21, 23, 27 (solutions are included in Appendix A).
- For more details, see here.
- For visual learners, check out this.
- (Check out optional Chapter 8 Linear regression with multiple predictors.)
Topic 3: Chapter 13 Inference with mathematical modeling link
- Overview: This chapter is to help you with Day 5 materials of Bayes BATS bootcamp.
- Exercises: 1, 3, 5 (solutions are included in Appendix A).
- For visual learners, check out this.
Topic 4: Chapter 14 Decision errors link
- Overview: This chapter is to help you with Day 5 materials of Bayes BATS bootcamp.
- Exercises: 1, 3, 5 (solutions are included in Appendix A).
Topic 5: Chapter 16 Inference for a single proportion link
- Overview: This chapter is to help you with Day 5 materials of Bayes BATS bootcamp.
- Exercises: 1, 13, 17, 19, 23, 27 (solutions are included in Appendix A).
Day 1 - Foundation of Bayesian inference
Your GitHub repository: day1-foundations-username
Day 2 - Bayesian computing: Simulating the posterior
Your GitHub repository: day2-simulations-username
| Type | Topic | Materials |
|---|---|---|
| Lecture - part 1 | Conjugate Priors and their Posteriors | |
| Lecture - part 2 | Gibbs Sampler and MCMC | |
| Discussion | Bayes in STEM Fields: Use Cases | |
| Activity | Designing a simulation activity |
Day 3 - Bayesian computing: Posterior analysis
Your GitHub repository: day3-posterior-username
Day 4 - Bayesian modeling: Regression models
Your GitHub repository: day4-regression-username
| Type | Topic | Materials |
|---|---|---|
| Lecture - part 1 | Fitting regression models | |
| Lecture - part 2 | Evaluating regression models | |
| Discussion | Bayes Course vs. Module | |
| Activity | Designing a Regression Model Lab |
Day 5 - Bayesian modeling: Hierarchical models
Your GitHub repository: day5-hierarchical-username