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[Transcript] Clare Corthell
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 (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!
Data Science is an ideal marriage for my technical capacities, social research inquisitions, and my geekish-freakish love of statistics.
I'm ready to hack on interesting questions with a team. The right team is where I can:
- Invest in industry best practices,
- Gain quality mentorship,
- Work with intractably large data sets, and
- Hack on interesting networking questions.
Are you my team? --> her at clarecorthell.com
- 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.
- Linear Algebra / Levandosky Stanford / Book
- Statistics Stats in a Nutshell / Book
- Problem-Solving Heuristics "How To Solve It" Polya / Book
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Algorithms
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Algorithms Design & Analysis I Stanford / Coursera
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Algorithm Design Kleinberg & Tardos / Book
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Databases
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Introduction to Databases Stanford / Coursera
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Data Mining
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Mining Massive Data Sets Stanford / Book
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Mining The Social Web O'Reilly / Book
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Introduction to Information Retrieval Stanford / Book
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Machine Learning
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Machine Learning / Ng Stanford / Coursera ** en process
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Programming Collective Intelligence O'Reilly / Book ** en process
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Statistics The Elements of Statistical Learning / Book ** en process
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Probabilistic Graphical Models
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Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials] (https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)
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PGMs / Koller Stanford / Coursera ** en process
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Natural Language Processing
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NLP with Python O'Reilly / Book ** en process
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Analysis
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Python for Data Analysis O'Reilly / Book
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Big Data Analysis with Twitter UC Berkeley / Lectures
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Social and Economic Networks: Models and Analysis / Stanford / Coursera
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Information Visualization "Envisioning Information" Tufte / Book
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Python (Learning)
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New To Python: Learn Python the Hard Way, Google's Python Class
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Python (Libraries)
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Basic Packages Python, virtualenv, NumPy, SciPy, matplotlib and IPython
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Bayesian Inference | pymc
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Labeled data structures objects, statistical functions, etc pandas (See: Python for Data Analysis)
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Python wrapper for the Twitter API twython
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Tools for Data Mining & Analysis scikit-learn
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Network Modeling & Viz networkx
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Natural Language Toolkit NLTK
- Pythonic Twitter Analyses (Link Coming)
- Capstone Analysis (Link Coming)
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
- Quora
- 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.