This repository consists of data science projects and programming code that I worked on at the winter 2016 cohort.
Here's what you can find in this repository:
Every morning we pair up with someone in the class and work on a programming problem together. Here I document each problem, our approach, and the resulting code for each exercise.
Investigations are a chance for students to introduce an interesting application of machine learning and data science.
In my first investigation, I presented some of my work experiences as a data science intern at an insurance company, and projects related to text mining and news analytics. In addition, I introduced Natural Language Processing with Python's NLTK with a live demonstration.
You can find my blog post on this investigation [here]. And more details in the Investigation folder.
Challenges are problem sets on topics that we're learning about.
Challenge 1 is an exploratory data analysis on the MTA's public turnstile data.
Challenge 2 uses Pandas to analyze movie box office data.
Just as it says, these are projects that I've worked on at Metis.
Project 1 uses NYC subway data to help a non-profit find the best subway stations
to place street teams.