[Transcript] Clare Corthell

The Open-Source Masters

I didn't want to wait to go back to grad school - so I designed my own. From April to August, I learned and hacked in great delight.

My Background / linkedin

I'm a Stanford-educated Engineer (and Linguist), previously a Front-End Developer and UX Designer on early-stage products. I'm always hacking 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 ready to hack on interesting questions with a team. The right team is where I can:

• Invest in industry best practices,
• Find quality mentorship,
• Work with intractably large data sets, and
• Hack on interesting networking questions.

Are you my team? --> her at clarecorthell.com

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

• Linear Algebra / Levandosky Stanford / Book
• Statistics / Stats in a Nutshell / Book
• Problem-Solving Heuristics "How To Solve It" Polya / Book

Computing

• Algorithms

• Algorithms Design & Analysis I / Stanford / Coursera

• Algorithm Design Kleinberg & Tardos / Book

• Databases

• Introduction to Databases / Stanford / Coursera

• Data Mining

• Mining Massive Data Sets / Stanford / Book

• Mining The Social Web O'Reilly / Book

• Introduction to Information Retrieval Stanford / Book

• Machine Learning

• Machine Learning / Ng Stanford / Coursera

• Programming Collective Intelligence O'Reilly / Book

• Statistics The Elements of Statistical Learning #current

• Probabilistic Graphical Models

• Stanford / Coursera

• Selected Topics: Bayesian Network Fundamentals, Markov Networks, Inference, MAP Estimation, Decision Making, Structured Learning

• Probabilistic Programming and Bayesian Methods for Hackers / Github / Tutorials

• NLP

• Natural Language for ML / link

• NLP for Python O'Reilly / Book

• NLTK

• Analysis

• Big Data Analysis with Twitter UC Berkeley / Lectures

• Social and Economic Networks: Models and Analysis / Stanford / Coursera

• Python (Language & Learning)

• Python for Data Analysis O'Reilly / Book

• New To Python: Learn Python the Hard Way, Google's Python Class

• Python (Libraries)

• Basic Packages Python, virtualenv, NumPy, SciPy, matplotlib and IPython

• Bayesian Inference | pymc

• Labeled data structures objects, statistical functions, etc pandas (See: Python for Data Analysis)

• Python wrapper for the Twitter API [twython]](https://github.com/ryanmcgrath/twython)

• Tools for Data Mining & Analysis scikit-learn

• Network Modeling & Viz networkx

Projects

• Pythonic Twitter Analyses (Link Coming)
• Capstone Analysis (Link Coming)

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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:

• Coursera
• Khan Academy
• Wolfram Alpha
• Wikipedia
• Kindle .mobis (carrying textbooks is so 90s.)
• PopSci Read: The Signal and The Noise Nate Silver
• Friends & Family (Impossible without their support! Special Thanks to N.S.)

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

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

Follow me on Twitter @clarecorthell