yingstat / data-science-seminar

Topics In Data Science and Bioinformatics

Table of Contents

• Single-cell Multimodal Data Journal Club
  • All details on wiki
• Causal Inference: What If? book club
  • Meeting details
  • Current activities
  • About us
  • Getting started
  • Presenting
  • Past textbooks

Causal Inference Book Club Meeting details

• Schedule: https://github.com/waldronlab/data-science-seminar/wiki
• Wednesdays at 5:00pm Central European Time (in March this is noon NYC time)
• Meetings are on Blackboard: https://tinyurl.com/datasciencebb (here is the full link)
• Email list: https://groups.google.com/forum/#!forum/stat_learning
• Calendar ical http

Current activities

During March 18 - May 2020, we will complete reading the new book Causal Inference: What If by Miguel Hernán and James Robins, scheduled to be released by Chapman & Hall/CRC in 2020. See the schedule wiki for the schedule of presentations.

About us

This repository represents 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.

Getting started

1. Leave a comment on the "Welcome" Issue to introduce yourself and to let us know your GitHub username.

2. Join our Google Group (open membership) and sign up to receive emails by visiting https://groups.google.com/forum/#!forum/stat_learning.

3. Optional: Install R and RStudio following these instructions. Here is a short video showing how to use RStudio to contribute to this Github repo.

4. Sign up for a GitHub account, then introduce yourself on the "Welcome" issue of this repository, under Issues. You will then be able to contribute your presentation and/or exercise notes using file upload directly here, or by using git. If you want to use git instead of simple file upload but don't know what that means, follow this tutorial. The process in RStudio is documented here or there is a video here.

Presenting

1. 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.

2. Read the required section of the book, and do the associated exercises that you will present.

3. Edit the presentation file, using the template provided in the folder corresponding to the textbook name. See more information about making Slidy presentations.

4. Commit the presentation to GitHub so that it is available to others. Don't know what that means? The process is documented here or there is a video here.

Past textbooks

Past textbooks have included:

• Modern Statistics for Modern Biology by Susan Holmes and Wolfgang Huber.
• 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.