My videos for “Business Analytics using Data Mining” now publicly available!

Five years ago, in 2012, I decided to experiment in improving my teaching by creating a flipped classroom (and semi-MOOC) for my course “Business Analytics Using Data Mining” (BADM) at the Indian School of Business. I initially designed the course at University of Maryland’s Smith School of Business in 2005 and taught it until 2010. When I joined ISB in 2011 I started teaching multiple sections of BADM (which was started by Ravi Bapna in 2006), and the course was fast growing in popularity. Repeating the same lectures in multiple course sections made me realize it was time for scale! … Continue reading My videos for “Business Analytics using Data Mining” now publicly available!

Categorical predictors: how many dummies to use in regression vs. k-nearest neighbors

Recently I’ve had discussions with several instructors of data mining courses about a fact that is often left out of many books, but is quite important: different treatment of dummy variables in different data mining methods. From Statistics courses that cover linear or logistic regression teach us to be careful when including a categorical predictor variable in our model. Suppose that we have a categorical variable with m categories (e.g., m countries). First, we must factor it into m binary variables called dummy variables, D1, D2,…, Dm (e.g., D1=1 if Country=Japan and 0 otherwise; D2=1 if Country=USA and 0 otherwise, etc.) … Continue reading Categorical predictors: how many dummies to use in regression vs. k-nearest neighbors

Teaching spaces: “Analytics in a Studio”

My first semester at NTHU has been a great learning experience. I introduced and taught two new courses in our new Business Analytics concentration (data mining and forecasting). Both courses met once a week for a 3-hour session for a full semester (18 weeks). Although I’ve taught these courses in different forms, in different countries, and to different audiences, I had a special discovery this time. I discovered the critical role of the learning space on the quality of teaching and learning. Specifically for a topic that combines technical, creativity and communication skills. “Case study” classroom In my many years of experience … Continue reading Teaching spaces: “Analytics in a Studio”

Running a data mining contest on Kaggle

Following the success last year, I’ve decided once again to introduce a data mining contest in my Business Analytics using Data Mining course at the Indian School of Business. Last year, I used two platforms: CrowdAnalytix and Kaggle. This year I am again using Kaggle. They offer free competition hosting for university instructors, called InClass Kaggle. Setting up a competition on Kaggle is not trivial and I’d like to share some tips that I discovered to help fellow colleagues. Even if you successfully hosted a Kaggle contest a while ago, some things have changed (as I’ve discovered). With some assistance from … Continue reading Running a data mining contest on Kaggle

A Tale of Two (Business Analytics) Courses

I have been teaching two business analytics elective MBA-level courses at ISB. One is called “Business Analytics Using Data Mining” (BADM) and the other, “Forecasting Analytics” (FCAS). Although we share the syllabi for both courses, I often receive the following question, in this variant or the other: What is the difference between the two courses? The short answer is: BADM is focused on analyzing cross-sectional data, while FCAS is focused on time series data. This answer clarifies the issue to data miners and statisticians, but sometimes leaves aspiring data analytics students perplexed. So let me elaborate. What is the difference … Continue reading A Tale of Two (Business Analytics) Courses

Business analytics student projects a valuable ground for industry-academia ties

Since October 2012, I have taught multiple courses on data mining and on forecasting. Teams of students worked on projects spanning various industries, from retail to eCommerce to telecom. Each project presents a business problem or opportunity that is translated into a data mining or forecasting problem. Using real data, the team then executes the analytics solution, evaluates it and presents recommendations. A select set of project reports and presentations is available on my website (search for 2012 Nov and 2012 Dec projects). For projects this year, we used three datasets from regional sources (thanks to our industry partners Hansa … Continue reading Business analytics student projects a valuable ground for industry-academia ties

Flipping and virtualizing learning

Adopting new technology for teaching has been one of my passions, and luckily my students have been understanding even during glitches or choices that turn out to be ineffective (such as the mobile/Internet voting technology that I wrote about last year). My goal has been to use technology to make my courses more interactive: I use clickers for in-class polling (to start discussions and assess understanding, not for grading!); last year, after realizing that my students were constantly on Facebook, I finally opened a Facebook account and ran a closed FB group for out-of-class discussions; In my online courses on … Continue reading Flipping and virtualizing learning

Where computer science and business meet

Data mining is taught very differently at engineering schools and at business schools. At engineering schools, data mining is taught more technically, deciphering how different algorithms work. In business schools the focus is on how to use algorithms in a business context. Business students with a computer science background can now enjoy both worlds: take a data mining course with a business focus, and supplement it with the free course materials from Stanford Engineering school’s Machine Learning course (including videos of lectures and handouts by Prof Andrew Ng). There are a bunch of other courses with free materials as part of the Stanford … Continue reading Where computer science and business meet

SAS On Demand: Enterprise Miner — Update

Following up on my previous posting about using SAS Enterprise Minder via the On Demand platform: From continued communication with experts at SAS, it turns out that with the EM version 5.3, which is the one available through On Demand, there is no way to work (or even access) non-SAS files. Their suggestion solution is to use some other SAS product like SAS BASE, or even SAS JMP (which is available through the On Demand platform) in order to convert your CSV files to SAS data files… From both a pedagogical and practical point of view, I am reluctant to … Continue reading SAS On Demand: Enterprise Miner — Update

SAS On Demand: Enterprise Miner

I am in the process of trying out SAS Enterprise Miner via the (relatively new) SAS On Demand for Academics. In our MBA data mining course at Smith, we introduce SAS EM. In the early days, we’d get individual student licenses and have each student install the software on their computer. However, the software took too much space and it was also very awkward to circulate a packet of CDs between multiple students. We then moved to the Server option, where SAS EM is available on the Smith School portal. Although it solved the individual installation and storage issues, the … Continue reading SAS On Demand: Enterprise Miner