Welcome to the General Assembly Data Science Handout page. Here I'll be assembling handouts, walkthrough and links for everyone to have some references to follow-up after class.

###General Resources ####Books

• Python for Data Analysis
• Elements of Statistical Learning (great reference for the theory behind a lot of the techniques)
• Machine Learning For Hackers (R code examples and walkthroughs)

###Lesson 1: Introduction to Data Science & Basic Data Manipulation

####Slides

• Lesson 1 Slides

####Handouts

• Unix Basics: Intro to the Command Line

####Links

• How To Start Thinking Like a Data Scientist
• Data Science Workflow
• Video Tutorials for Command Line Basics
• Command Line Data Manipulation
• Git Tutorial from Atlassian
• Git Tutorial from CodeSchool

###Lesson 2: Data Storage and Extraction

####Slides

• Lesson 2 Slides

####Handouts

• MySQL Tutorial
• Introduction to Python
• Python Exercises

####Links

• Comparison of NoSQL Databases
• Interactive Python Tutorial
• Introduction to Python
• Python Data Structures

###Lesson 3: Python and Data Manipulation

####Handouts

• Python Exercises

####Links

• Introduction to Pandas

###Lesson 4: Data Visualization ####Assignment 1: Due Jan 9

####Handouts

• Pandas and Data Viz Notebook

####Slides

• Lesson 4 Slides

####Links

• Linear Algebra in 4 Pages
• Narrative Visualization: Telling Stories With Data
• Bokeh
• Vincent
• Python ggplot
• Matplotlib

Lesson 5: Introduction to Machine Learning

####Handouts -Sklearn and KNearest Neighbors

####Links

• Scikits-learn User Guide
• A Few Useful Things to Know About Machine Learning

Lesson 6: Linear Regression

####Assignment 1: Due Jan 24

####Handouts -Linear Regression

####Links

• A Few Useful Things to Know About Machine Learning
• Statsmodels Documentation
• Python 538 Model

Lesson 7: Logistic Regression and Regularization

Lesson 8: Naive Bayes and Bayesian Estimators