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Teaching
  1. Introduction to Data Science (2015)
    Some of the course materials
    The class focuses on building skills for collecting, managing, and analyzing large data sets. As part of the course, we will cover two prominent ways of building large original datasets: scraping data from the web, and conducting large n experiments. We will also cover how to manage and analyze data using a popular relational database, SQLite. As part of the discussion around managing and analyzing large datasets, we will also cover cloud based solutions, and basics of mapReduce. In the data analysis segment, we will cover basics of supervised and unsupervised learning, including techniques like cross-validation, and bootstrapping, before learning popular supervised techniques, such as SVM, Ridge Regression, and Elastic Net, and popular unsupervised methods, such as k-means clustering.

  2. R Workshop (2014–2015, x3)
    We cover basics of programming in R: data types and data structures, objects and classes, and writing loops and conditionals. Next, we cover the basic analytical workflow: getting data, including scraping data, cleaning and tidying data, exploring data, including plotting data, and analyzing data.

  3. Python Workshop (2015)
    The workshop introduces data types and data structures, and how to write conditionals and loops. An overview of the analytical workflow in iPython, including how to scrape, plot and analyze data.