Bayes BATS Bootcamp Schedule

Daily Schedule

The bootcamp will take place in room 3011 on the 3rd floor of Donald Bren Hall. 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.

  • 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.

  • Install and setup git

  • Test your installations

  • 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. Federica will also be holding office hours and you will receive the appropriate zoom links via email from her.

Day 0 - 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 also 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 1 - Foundation of Bayesian inference

Your GitHub repository: day1-foundations-username

Type Topic Materials
Opening Welcome to Bayes BATS!
Lecture - part 1 Introduction to Bayesian thinking
Lecture - part 2 Discrete Response Data
Discussion Challenges of teaching Bayes
Activity Designing a Bayes Theorem activity

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

Type Topic Materials
Lecture - part 1 Posterior Inference and MCMC Algorithms
Lecture - part 2 Posterior Analysis with MCMC
Lecture - part 2 Posterior Analysis with MCMC and rstanarm version
Discussion Effective Assessment for Bayes Learning
Activity Designing a posterior analysis activity

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

Type Topic Materials
Lecture Hierarchical Models
Lecture Statistical Inference: frequentist vs. Bayesian
Discussion Software choices for Bayes learners
Activity Tier 2 preparation
Closing Before We Say Goodbye

Tier 2

About ten instructors who attend the bootcamp will be selected to develop course materials in the Fall semester.

You can find more information following this link.