The Art of Data Science
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 in creating an interactive online reading of Matsui and Peng's The Art of Data Science. In each of our weekly meetings, a chapter of the book is presented by a developing instructor with a focus on using the R language. Our meetings are open to all (see details below) and our materials 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.
Matsui, E. & Peng, R. D. The Art of Data Science. (Leanpub, 2015).
This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.
If you don't already have them, install R and RStudio following these instructions.
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.
Join our Google Group (open membership) and sign up to receive emails by visiting https://groups.google.com/d/forum/artofdatascience.
Join our Google Calendar (open membership) to receive meeting reminders by subscribing to the calendar's email address (email@example.com).
Participate in weekly meetings, the details of which are below.
When: Wednesday's from 11:15-12:15 (NYC) / 17:15-18:15 (Salzburg)
|2017-10-04||Data Analysis as Art||schifferl|
|2017-10-11||Epicycles of Analysis||nicobuettner|
|2017-10-18||Stating and Refining the Question||raph333|
|2017-10-25||Exploratory Data Analysis||philippgrafendorfe|
|2017-11-08||Using Models to Explore Your Data||ITtraveller|
|2017-11-15||Inference: A Primer||judithparkinson|
|2017-12-06||Inference vs. Prediction: Implications for Modeling Strategy||raph333|
|2017-12-13||Interpreting Your Results||philipp|
Pick the date or topic that best suits you.
Edit this file, adding you GitHub username to the schedule table.
Read the chapter in the book.
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.
Present your hard work at the weekly meeting!
Still need help? Email the Google Group (firstname.lastname@example.org).