Teaching

Research Design and Applications for Data Analysis (RDADA)

Masters course, University of California, Berkeley, School of Information, 2020

Introduces the data sciences landscape, with a particular focus on learning data science techniques to uncover and answer the questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply disciplined, creative methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis throughout is on making practical contributions to real decisions that organizations will and should make.

IST 687 Introduction to Data Science

Masters course, Syracuse University, School of Information Studies, 2020

The course provides students a hands-on introduction to data science, with applied examples of data collection, processing, transformation, management and analysis. Students will explore key concepts related to data science, including applied statistics, information visualization, text mining and machine learning. R, the open source statistical analysis and visualization system, will be used throughout the course. R is reckoned by many to be the most popular choice among data analysts worldwide; having knowledge and skill with using it is considered a valuable and marketable job skill for most data scientists. Students will also learn how to use supervised and unsupervised machine learning techniques. They will focus on structured data, using R (e.g., support vector machines, association rules mining) in conjunction with learning the full life cycle of data science.