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 between cross-sectional data and time series data?
Think photography. Cross-sectional data are like a snapshot in time. We might have a large dataset on a large set of customers, with their demographic information and their transactional information summarized in some form (e.g., number of visits thus far). Another example is a transactional dataset, with information on each transaction, perhaps including a flag of whether it was fraudulent. A third is movie ratings on an online movie rental website. You have probably encountered multiple examples of such datasets in the Statistics course. BADM introduces methods that use cross-sectional data for predicting the outcomes for new records. In contrast, time series data are like a video, where you collect data over time. Our focus will be on approaches and methods for forecasting a series into the future. Data examples include daily traffic, weekly demand, monthly disease outbreaks, and so forth.How are the courses similar?
The two courses are similar in terms of flavor and focus: they both introduce the notion of business analytics, where you identify business opportunities and challenges that can be potentially be tackled with data mining or statistical tools. They are both technical courses, not in the mathematical sense, but rather that we do hands-on work (and a team project) with real data, learning and applying different techniques, and experiencing the entire process from business problem definition to deployment back into the business environment.How do the courses differ in terms of delivery?
In both courses, a team project is pivotal. Teams use real data to tackle a potentially real business problem/opportunity. You can browse presentations and reports from previous years to get an idea. We also use the same software packages in both courses, called XLMiner and TIBCO Spotfire. For those on the Hyderabad campus, BADM and FCAS students will see the same instructor in both courses this year (yes, that's me).
Since last year, I have "flipped" BADM and turned it into a MOOC-style course. This means that students are expected to do some work online before each class, so that in class we can focus on hands-on data mining, higher level discussions, and more. The online component will also be open to the larger community, where students can interact with alumni and others interested in analytics. FCAS is still offered in the more traditional lecture-style mode.Is there overlap between the courses?
While the two courses share the data mining flavor and the general business analytics approaches, they have very little overlap in terms of methods, and even then, the implementations are different. For example, while we use linear regression in both cases, it is used in different ways when predicting with cross-sectional data vs. forecasting with time series.So which course should I take? Should I take both?
Being completely biased, it's difficult for me to tell you not to take any one of these courses. However, I will say that these courses require a large time and effort investment. If you are taking other heavy courses this term, you might want to stick with only one of BADM or FCAS. Taking the two courses will give you a stronger and broader skill set in data analytics, so for those interested in working in the business analytics field, I'd suggest taking both. Finally, if you register for FCAS only, you'll still be able to join the online component for BADM without registering. Although it's not as extensive as taking the course, you'll be able to get a glimpse of data mining with cross-sectional data.Finally, a historical note: when I taught a similar course at the University of Maryland (in 2004-2010), it was a 14-week semester-long course. In that course, which was mostly focused on cross-sectional methods, I included a chunk on forecasting, so it was a mix. However, the separation into two dedicated courses is more coherent, gives more depth, does more justice to these extremely useful methods and approaches, and allows gaining first-hand experience in the uses of these different types of data structures that are commonly encountered in any organization.
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