Skip to content

[Transcript] Clare Corthell

Clare edited this page Oct 18, 2013 · 28 revisions

The Open-Source Masters

I couldn't wait to go back to grad school. Literally. So I designed my own grad school and spent 5 months learning & hacking in great delight!

My Background (linkedin)

I'm a Stanford-educated Engineer, previously a Front-End Developer and UX Designer on early-stage products. I'm always in hot pursuit of deeper insight to social questions!

Goals & Motivations of the Open Source M.S.

Data Science is an ideal marriage for my technical capacities, social research inquisitions, and my geekish-freakish love of statistics.

Next Steps?

I'm now a Data Scientist with an incredible team at Mattermark!


The Data Science Curriculum / April-August 2013

  • Intro to Data Science UW / Coursera
  • Topics: Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.

Math

Computing

Projects

  • Coursework
  • Sentiment analysis, trending topics, and friendship mapping with Twitter API
  • Joins and Matrix Manipulation in MapReduce (AWS EC2)
  • In-database Text analysis (SQL)
  • Sentiment analysis of movie tweets (Python) (Link Coming)

A Note on Tools

This degree is brought to you by: "THE INTERNET".

Information is more democratized^ now than it was at any point in history. Given a little initiative and interest, you can tailor and excel in an education of your own design. The connective web made me what I am today, growing from the child obsessed with Number Munchers to an adult jaw-dropping over DBSCAN.

The most valuable resources I used were:

^ given internet access - an issue near and dear to me.


I "Forked" this into the Open Source Data Science Masters Curriculum.

Follow me on Twitter @clarecorthell

Clone this wiki locally