News

September 26, 2014

Introducing two new business analytics courses in Taiwan

This Fall, I'm teaching two new elective courses at NTHU's Institute of Service Science: Business Analytics using Data Mining and Business Analytics using Forecasting. The two new courses join three existing elective courses to form the new concentration in Business Analytics. The most important part is our focus on humane and socially responsible analytics. All our courses are 18-weeks long, with 3-hour weekly sessions. Sufficient time to absorb, experience, and learn!
To learn more, read my blog post.


August 13, 2014

Joining National Tsing Hua University's Institute of Service Science

This Fall, I'm joining Taiwan's National Tsing Hua University as Distinguished Professor at the College of Technology Management's Institute of Service Science (ISS). ISS has just started a PhD program as well as a Masters-level concentration in Business Analytics. Ni hao to all my new colleagues and students!


June 30, 2014

Interview on Statistics Views

"There’s so much we don’t know because we’ve been brainwashed to think that we do know." - I was interviewed for Wiley's Statistics Views. Read the complete interview.


June 28, 2014

"Opening data with Kaggle": talk at #OpenDataHyd

Open Data Camp Logo

I describe collaborations with India start ups to create data mining contests on platforms such as kaggle.com. A great way to open real, interesting, local data. Provides business analytics students with real problems and data as well as access to the domain experts; gives start ups novel creative ideas for using their data, visibility to the world, and relationship with academia. 2014 Open Data Camp is hosted by ISB SRITNE center on June 28, 2014.


June 24, 2014

"Predicting, Explaining and the Business Analytics Toolkit": Keynote at the upcoming 2014 NASSCOM Big Data & Analytics Summit

I'll be presenting a keynote talk at the upcoming NASSCOM Big Data & Analytics Summit on Friday, June 27, 2014. In earlier talks, I have been emphasizing and introducing the advantages of predictive analytics. In this talk, I start from predictive analytics and move on to causal explanation. Synopsis: Big data have brought predictive analytics to the forefront by enabling organizations to generate micro-level predictions. Predictive analytic methods extract correlations and associations from rich datasets for the purpose of generating predictions. Personalized recommendations, offers, treatments, and interventions are examples of predictive analytics used in many data-rich-and-savvy organizations. While predictive analytics offer significant actionable value to companies by answering "who, what, when, where?", they are not capable of providing causal explanations for answering "why?" The good news is that statistical methods exist for causal investigation. The gold standard is randomized experiments, with alternative methods for cases when experiments are impossible. In the realm of Big Data, implementing such methods can offer new macro-level insights that can further strengthen data-driven decision making.


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