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