Advanced Data Science
Home
Curriculum
Introduction
Organizing and Version Control
RMarkdown and GitHub
Getting Data and APIs
Manipulating Data
Strings and Regular Expressions
Tidy text and sentiment analysis
Topic Models and EDA
Expository Graphs
Dimension Reduction
Unsupervised Analysis
Building R Packages
Modeling
Shiny Part 1
Shiny Part 2
Machine Learning
Data products
Machine Learning (continued)
Blending and deep learning
Grading
Staff
Instructors
Jeff Leek
John Muschelli
Teaching Assistants
Stephen Cristiano
Discussion
Resources
Course resources
Previous versions of the class
2016
2015
2014
Books of interest
Elements of Data Analytic Style
R programming
R for Data Science
Introduction to statistical learning
Elements of statistical learning
Advanced data analysis from an elementary point of view
Blog posts of interest
The four eras of data
Graduate student data analysis inspired by a high-school teacher
What statistics should do about big data: problem forward not solution backward
The key word in “Data Science” is not Data, it is Science
Other resources
Rstudio’s cheatsheets
swirl
DataCamp