• Gather Real Time Data from the Web

  • Harness the Wisdom of Crowds

  • Learn the Power of Simple Models

  • Capture Social Media Sentiment

  • Collect, Visualize, and Analyze Network Data

At UCLA I teach two undergraduate communications courses, Social Networking (COMM ST 156), and Methodologies in Communication Research (COMM ST 150). You can read my "bruinwalk" teaching reviews here.

Prior to joining UCLA, I taught two courses at the Kellogg School of Management, Social Dynamics and Network Analytics and Business Analytics, both of which are part of the Program on Data Analytics at Kellogg (PDAK).

I developed the elective course Social Dynamics and Network Analytics (syllabus) and the course lies at the center of my own research interests in networks, Big Data, and social dynamics. Social-DNA covers cutting edge research on social media, digital data, and crowdsourcing, and provides tools to practically apply this research. By the end of the course students know how to: measure volume and location of Internet search data to understand and forecast trends; measure volume and sentiment of Twitter conversations; collect network data and create meaningful network visualizations; use the wisdom of crowds, including setting up a prediction market, to create better forecasts; and use Amazon Mechanical Turk for crowdsourcing.

In addition to teaching Social-DNA in both the full-time and part-time MBA programs at Kellogg, I taught an executive version of the course in the Kellogg EMBA program (syllabus) in Evanston and in Miami, and the Kellogg-Recanati International EMBA program in Tel Aviv.

I also taught the Kellogg core course Business Analytics, which provides a foundation for the rest of the courses in the Program on Data Analytics at Kellogg (PDAK).