Data Science: Some Basics

Introduction to Data Science (presentation, tex)
- What can Big Data do for you?
- What is Big Data?
- Implications for Statistics and Computation
- What is Data Science?
- Prerequisites
Get your own (Big) Data (presentation, tex)
- Scrape web pages and pdfs. (Scripts)
- Image to Text (Python Script using Tesseract)
- Image to Text in R using the Abbyy FineReader Cloud OCR
- Image to Text in R using the Captricity API
- Web Scraping/API Applications:
- Get Social Networking Data
- Regular Expressions
- Pre-process text data
- Assignment
Databases and SQL (presentation, tex)
- What are databases?
- Relational Model
- Relational Algebra
- Basic SQL
- Views

4a. Introduction to Introduction to Statistical Learning

4b. 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

Supervised Methods
Unsupervised Methods
- PCA, CA
- k-means (presentation, tex)
Presenting Analyses
- ggplot2 in brief
- Examples of ggplot in action:
  - NYT Civil Rights Coverage (R code, Graph)
  - Military Experience of UK Prime Ministers (R code, Graph)