Mark Corey's repositories
churn_risk_classification
creating a classifier from a sample ride-share service data set
content
Official content for Harvard CS109
courses
Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1
Data-Analysis-and-Machine-Learning-Projects
Repository of teaching materials, code, and data for my data analysis and machine learning projects.
data-science-primer
A set of self paced resources for anyone looking to get into data science. The materials assume an absolute beginner and are intended to prepare students for the Galvanize Data Science interview process: http://www.galvanize.com/courses/data-science/
nba_player_clustering
Using unsupervised clustering to determine optimal team composition and relative player valuation
pydata-book
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Various-EDA-projects
-NYPD Collision Data EDA
dataweek-workshop
Machine learning workshop using Python, pandas, and scikit-learn. The first half of the day covered supervised classification using Logistic Regression and how to use cross validation to evaluate your models . The second half of the day covered unsupervised clustering with Kmeans as well as an overview of the data science process.
javascript
GitBook teaching programming basics with Javascript
LearnDataScience
Open Content for self-directed learning in data science
pipelines_and_featureunions
An in depth tutorial on sklearn's Pipeline and FeatureUnion classes.
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Python-WebImageScraper
A Python powered app that scrapes images from requested URLs and dumps them in scraper's directory.
SQL-Tutorial
A Gentle Introduction to SQL Using SQLite
statlearning-notebooks
Python notebooks for exercises covered in Stanford statlearning class (where exercises were in R).
ThinkStats2
Text and supporting code for Think Stats, 2nd Edition