compastica/gads7

GA Data Science NY Section 7

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.

####Final Project Details

Key Dates

• Feb 6: Project Presentations
• Feb 20: Data Review and Processing Presentations
• Last Day of Class: Final Presentations

###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

####Slides

• Lesson 5 Slides

####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 ####Slides

• Lesson 6 Slides

####Handouts

• Linear Regression

####Links

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

Lesson 7: Logistic Regression and Regularization

####Slides

• Lesson 7 Slides

####Links

• Logistic Regression Walkthrough
• Logistic Regression w/ Statsmodel - Well Switching in Bangledesh
• Odds Ratio Explanation
• Fast Logistic Regression: Mahout
• Fast Logistic Regression: Vowpal Wabbit
• Fast Logistic Regression: LIBLINEAR

Lesson 8: Naive Bayes and Bayesian Estimators

####Slides

• Lesson 8 Slides

####Links

• Insult Detection Kaggle Submission
• Holy Trinity of Bayesian Estimation
• History of Bayes
• Mathematical Exploration of Bayes Theorem
• Naive Bayes v. Logistic Regression

Lesson 9: Decision Trees and Random Forests

####Slides

• Lesson 9 Slides

####Handouts

• Random Forest on Text Data

Links

• Random Forests in Python
• Random Forests for Kaggle
• Random Forests and Performance Metrics

Lesson 10: Classification Review

####Slides

• Lesson 10 - Review Slides

###Handouts

• In Class Review

####Links

• Choosing a ML Classifier
• Machine Learning Cheat Sheet for Sklearn

Lesson 11: Ensemble Learning

####Slides

• Lesson 11 - Ensemble Learning
• Lesson 11 - K Means

####Handouts

• Random Forest on Text Data

###Links

• [KMeans IPython Notebook] (http://nbviewer.ipython.org/urls/raw.github.com/temporaer/tutorial_ml_gkbionics/master/2%2520-%2520KMeans.ipynb)
• Text Clustering in Sklearn
• Cloudera ML KMeans

Lesson 12: K-Means Clustering

Lesson 13: PCA and Unsupervised Learning

Lesson 14: Recommendation Systems