Data Science

1. Introduction to Data Science (presentation, tex)
   • What can Big do for you?
   • What is Big Data?
   • Implications for Statistics and Computation
   • What is Data Science?
   • Prerequisites
2. Get your own (Big) Data (presentation, tex)
   • Scrape web pages and pdfs. (Scripts)
   • Image to Text. (Script)
   • Web Scraping/API Applications:
     • Get Data on Journalists
     • Get Weather Data
     • Get Cricket Data
     • Get Congressional Speech Data
   • Regular Expressions
   • Pre-process text data
   • Assignment
3. Databases and SQL (presentation, tex)
   • What are databases?
   • Relational Model
   • Relational Algebra
   • Basic SQL
   • Views
4. Introduction to Statistical Learning (presentation, tex)
   • How to learn from data?
   • Nearest Neighbors
   • When you don't have good neighbors
   • Assessing model fit
   • Clarification about Big Data

Suggested Books

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
By Trevor Hastie, Robert Tibshirani, Jerome Friedman
ISBN: 0387848576

Python Programming: An Introduction to Computer Science
By John Zelle
ISBN: 1590282418

ggplot2: Elegant Graphics for Data Analysis (Use R!)
By Hadley Wickham
ISBN: 0387981403