I was the founding director and faculty member of the Applied Analytics program at Columbia University. This ground-breaking Master of Science degree trains managers of data-driven organizations.

I have also taught business analytics, business statistics, and data visualization at New York University (10+ years) and at various businesses and organizations.

My instruction emphasizes conceptual understanding, critical thinking, and data interpretation. All analyses and visual designs ride on subjective assumptions, and so the greatest challenge to an instructor is in nurturing the proper intuition that complements the data. I like to use case studies, and I am prone to setting open-ended assignments.

I also teach workshops on business analytics and data visualization for businesses and organizations.

If you would like to organize courses for your organization, click below:

Here are descriptions of my courses:


Applied Analytics Frameworks and Methods // Columbia

A core course in the Master of Science in Applied Analytics. A wide-ranging survey of analytical methods widely used in data-driven businesses and organizations. Some theory is presented but the primary managerial concerns are connecting the business problem to data, selecting the methodology based on real-world concerns, evaluating the quality of analytical models, and disseminating results to non-technical audiences. The course covers statistical methods such as regression as well as machine learning methods such as decision trees.

Data Visualization // Columbia

Part of the Executive Education program on Leading Business Change Through Analytics. What is good data visualization? How can the Trifecta Checkup framework be applied to business datasets? How can managers use precise language to describe what they want from visual design?

More information on Trifecta Checkup here

Numbersense: Statistical Reasoning in Practice // Principal Analytics Prep

Part of the Certified Data Specialist program. This course introduces fundamental statistical concepts using case discussion. Contextual understanding leads to thoughtful assumptions, which result in principled analyses of data.

More information about Principal Analytics Prep here

Careers in Data Science and Business Analytics // NYU

Data Science and Business Analytics are red-hot in the business world -- there are definitely more jobs than qualified people. Is this the right field for you? How do you find a job in this field? Finally, how do you build a lasting career in analytics?

Statistics for Management I // NYU

A practical introduction to statistics as applied to business and management problems. Most students are brushing up their math skills in advance of graduate school and business school while others need a refresher for work. Key topics include summary statistics, statistical graphics, language of probability, confidence intervals, linear and logistic regression, and statistical significance. Ample use of case studies. Emphasis on interpretation and concepts.

The Art of Data Visualization // NYU

The Graph Making Workshop. First of a kind offering. Conducted in the style of a creative writing workshop, this course trains students in the craft of making and re-making charts, and giving and receiving critique on data visualization. Students are encouraged to integrate the statistical, computer-science, and design perspectives.

Also a part of the Certificate in Analytics and Data Visualization.

News, Narratives & Design I // New School

In Spring 2015, I was a guest lecturer in the Journalism + Design program at the New School, focusing on data journalism.

In the picture, a student participated in an in-class exercise illustrating cluster analysis.

Business Analytics and Data Visualization // NYU

Part of the Marketing Analytics track in the M.S. in Integrated Marketing program. Overview of analytics and data visualization in businesses. Introduction to Big Data and predictive analytics. Hands-on training on using SAS Enterprise Guide and JMP data discovery software. In-class presentation of analytics findings. Managing the analytics function.


Certificate in SAS Data Mining for Marketers // NYU

Intensive lab courses introducing SAS programming. Four modules include Introduction to SAS Programming Concepts; Data Integration, Manipulation, and Querying; Basic Statistical Analysis; and Advanced Data Mining Techniques. Hands-on in-class exercises.