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 Cequity and TheBargain.in). One is a huge dataset from an Indian retail chain of hyper markets. Another is data on electronic gadgets on online shopping sites in India. A third is a large survey on mobile usage conducted in India. These datasets were also used in several data mining contests that we set up during the course through CrowdANALYTIX.com and through Kaggle.com. The contests were open to the public and indeed submissions were given from around the world.

Business analytics courses are an excellent ground for industry-academia partnerships. Unlike one-way interactions such as guest lectures from industry or internships or site visits of students, a business analytics project that is conducted by student teams (with faculty guidance) creates value for both the industry partner who shares the data as well as the students. Students who have gained the basic understanding of data analytics can be creative about new uses that companies have not considered (this can be achieved through "ideation contests"). Companies can also use this ground for piloting or testing out the use or their data for addressing goals of interest with little investment. Students get first-hand experience with regional data and problems, and can showcase their project as they interview for positions that require such expertise.

So what is the catch? Building a strong relationship requires good, open-minded industry partners and a faculty member who can lead such efforts. It is a new role for most faculty teaching traditional statistics or data mining courses. Managing data confidentiality, creating data mining contests, initiating and maintaining open communication channels with all stakeholders is nontrivial. But well worth the effort.

Got Data?!

The American Statistical Association's store used to sell cool T-shirts with the old-time beggar-statistician question "Got Data?" Today it is much easier to find data, thanks to the Internet. Dozens of student teams taking my data mining course have been able to find data from various sources on the Internet for their team projects. Yet, I often receive queries from colleagues in search of data for their students' projects. This is especially true for short courses, where students don't have sufficient time to search and gather data (which is highly educational in itself!).

One solution that I often offer is data from data mining competitions. KDD Cup is a classic, but there are lots of other data mining competitions that make huge amounts of real or realistic data available: past INFORMS Data Mining Contests (2008, 2009, 2010), ENBIS Challenges, and more. Here's one new competition to add to the list:

The European Network for Business and Industrial Statistics (ENBIS) announced the 2011 Challenge (in collaboration with SAS JMP). The title is "Maximising Click Through Rates on Banner Adverts: Predictive Modeling in the On Line World". It's a bit complicated to find the full problem description and data on the ENBIS website (you'll find yourself clicking-through endless "more" buttons - hopefully these are not data collected for the challenge!), so I linked them up.

It's time for T-shirts saying "Got Data! Want Knowledge?"

Eight great projects

As the Fall semester just came to a close, another cohort of 36 MBAs completed the data mining course at the Smith School of Business. Students worked throughout the semester on real business problems with real data. From data collection, through exploration, and modeling.

Projects ranged from more socially aware (Profiling the medically- insured vs. uninsured in the USA; Reverse-engineering student loan deference algorithms) to more $ aware (Customer retention at an online fitness company; Drivers of dividend decreases) . A few projects on real-estate and travel (Determinants of flight delays from Washington to Honolulu; Factors leading to a quick-sale of condos in Arlington) and one on healthcare (Predicting delays in the operating room), which one the vote of the class for "best project".

To see short presentations and reports on these projects, please see the course webpage.

Finally, I am happy to announce that the course's new official name is "Data Mining for Business Intelligence". On some university webpages "Intelligence" was dropped. But I won't crack any jokes!