data_science's Introduction

Data Science Toolbox

This repo contains snippets of code with a little bit of theory and explanation that can be handy for beginning data scientists. It was created while I was attending Galvanize Data Science immersive program in Seattle. Code is in Python, specifically in ipython notebooks as they are easy to view on GitHub.

Pull requests with updates are welcome!

Theory

Hypothesis testing - t-test, z-test, AB testing. hypothesis_testing.ipynb
Probability and statistics - combinatorics, conditional probability, statistical distributions. probability_and_statistics.ipynb
Gradient descent - gradient_descent.ipynb

Machine Learning

ML algorithms - linear regression, logistic regression, decision trees, random forests, gradient boosting, PCA, SVD, NMF and more. ml_algorithms.ipynb
Recommenders - different graphlab recommenders. recommenders.ipynb

NLP

Doc2Vec - document similarity search using gensim. doc2vec.ipynb
Text summarization - text summarization and keyword extraction using gensim. text_summarization.ipynb
NearPy - locality sensitive hashing (LHS) for approximated nearest neighbor search. nearpy.ipynb

Development

Pipeline - pipeline, feature union, grid search. pipeline.ipynb
Map Reduce - Hadoop, Spark. map_reduce.ipynb
Scraping and MongoDB - requests, BeautifulSoup, pymongo. scraping_mongo.ipynb
AWS Deployment - setting up EC2 instance with PostgreSQL on it, running Flask app. aws.md

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