Education

For those wondering, my traditional education was at UC Berkeley, where I got my bachelors in Economics. During my time there, I assisted several professors with their research and discovered my interest in uncovering patterns from a sea of data, what we today call Data Science, though that term wasn’t coined yet at the time.

Around the time I graduated, MOOCs were just taking off (MOOCs - Massive Open Online Courses). I soon enrolled in Coursera’s quintessential Machine Learning course and was hooked.

Though I’ve only scratched the tip of the iceberg, below are some of the resources I have found useful as either refreshers or for learning the latest technologies, the ones with the asterisks are the ones I would highly recommend.

Completed Online Courses

• Stanford: Statistics in Medicine III *
  with Kristin Sainani
• Udacity: Programming Foundations with Python with Kunal Chawla
• Data School: Machine Learning with Text in Python *
• Udacity: Intro to Inferential Statistics *
• Udacity: Intro to Descriptive Statistics
• Udacity: A/B Testing
• Udacity: How to Use Git and Github
• Stanford Online: SQL * with Jennifer Widom
• Udemy: Javascript - Understanding the Weird Parts [40%]
• Edx: Supply Chain and Logistics Fundamentals [30%]
• Codeacademy: How to use APIs with Python
• Udemy: Ultimate Guide to Funnel Optimization
• Udemy: SQL Database for Beginners
• Coursera: Stanford’s Machine Learning * with Andrew Ng
• Coursera: Johns Hopkins Data Analysis
• Coursera: Johns Hopkins Computing for Data Analysis

Books

• Hands-On Machine Learning with Scikit-Learn and TensorFlow Géron, Aurélien [60%]
• Applied Predictive Modeling by Kuhn, Max, Johnson, Kjell [20%]
• Interactive Data Visualization for the Web: An Introduction for Designing with D3 Murray, Scott [40%]
• Natural Language Processing with Python by Bird, Steven, Klein, Ewan, Loper, Edward, Brand [33%]
• Introduction to Statistical Learning * by James, Witten, Hastie, Tibshirani
• Discovering Statistics * by Field, Miles, Field [60%]

Current Non-Technical Book Favorites

• Dear Data by Giorgia Lupi & Stefanie Posavec
• Tuesdays with Morrie: An Old Man, A Young Man, and Life’s Greatest Lesson by Mitch Albom
• Troublemakers: Silicon Valley’s Coming of Age by Leslie Berlin