Table of Contents
- Wednesdays at 5:00pm Central European Time (noon NYC time in March, 11am April onwards)
- Meetings are on Blackboard: https://tinyurl.com/datasciencebb
- Anonymous dial-in phone access: 571-392-7650 PIN: 275 060 2621
- Schedule: https://github.com/waldronlab/data-science-seminar/wiki
- Email list: https://groups.google.com/forum/#!forum/stat_learning
- Calendar ical http
This repository represent the joint effort of Paris Lodron University of Salzburg and the City University of New York Graduate School of Public Health and Health Policy. During active semesters we hold weekly meetings, where a chapter of a book is presented by a developing instructor with a focus on modern applied statistical methodology and using the R language. Our meetings are open to all (see details below), and materials we produce are licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License. We hope you find these materials useful and will join our sessions.
If you don't already have them, install R and RStudio following these instructions. Here is a short video showing how to use RStudio to contribute to this Github repo.
Sign up for a GitHub account (also free) and clone this repository (open membership) in RStudio. Don't know what that means? Follow this tutorial. The process in RStudio is documented here or there is a video here.
Leave a comment on the "Welcome" issue to let us know your GitHub username.
Join our Google Group (open membership) and sign up to receive emails by visiting https://groups.google.com/forum/#!forum/stat_learning.
Pick the date or topic that best suits you and reserve it on the presentation schedule wiki, adding your GitHub username to the schedule table.
Read the required section of the book, and do the associated exercises that you will present.
Edit the presentation file using RStudio. All presentations should be authored using the
.Rpresformat, more infomation about the format is available here. Additionally, some previous presentation that can be used as examples are available here.
Past textbooks have included:
- Data Analysis for the Life Sciences by Rafael A Irizarry and Michael I Love (Print Version) (HTML Version)
- An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.
- Statistical Rethinking: A Bayesian Course with Examples in R and Stan by Richard McElreath, with supplement by Solomon Kurz and lectures by McElreath.
- The Art of Data Science (https://leanpub.com/artofdatascience or https://bookdown.org/rdpeng/artofdatascience/) by Roger D. Peng and Elizabeth Matsui.