Forecasting large collections of time series

With the recent launch of Amazon Forecast, I can no longer procrastinate writing about forecasting “at scale”! Quantitative forecasting of time series has been used (and taught) for decades, with applications in many areas of business such as demand forecasting, sales forecasting, and financial forecasting. The types of methods taught in forecasting courses tends to be discipline-specific: Statisticians love ARIMA (auto regressive integrated moving average) models, with multivariate versions such as Vector ARIMA, as well as state space models and non-parametric methods such as STL decompositions. Econometricians and finance academics go one step further into ARIMA variations such as ARFIMA (f=fractional), … Continue reading Forecasting large collections of time series

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!

Designing a Business Analytics program, Part 3: Structure

This post continues two earlier posts (Part 1: Intro and Part 2: Content) on Designing a Business Analytics (BA) program. This part focuses on the structure of a BA program, and especially course structure. In the program that I designed, each of the 16 courses combines on-ground sessions with online components. Importantly, the opening and closing of a course should be on-ground. The hybrid online/on-ground design is intended to accommodate participants who cannot take long periods of time-off to attend campus. Yet, even in a residential program, a hybrid structure can be more effective, if it is properly implemented. The … Continue reading Designing a Business Analytics program, Part 3: Structure

Designing a Business Analytics program, Part 2: Content

This post follows Part 1: Intro of Designing a Business Analytics program. In this post, I focus on the content to be covered in the program, in the form of courses and projects. The following design is based on my research of many programs, on discussions with faculty in various analytics areas, with analysts and managers at different levels, and on feedback from many past MBA students who have taken my analytics courses over the years (data mining, forecasting, visualization, statistics, etc.) and are now managing data at a broad range of companies and organizations. Content Dealing with data, little … Continue reading Designing a Business Analytics program, Part 2: Content

Designing a Business Analytics program, Part 1: Intro

I have been receiving many inquiries about programs in “Business Analytics” (BA), online and offline, in the US and outside the US. The few programs that are already out there (see an earlier post) are relatively new, so it is difficult to assess their success in producing data-savvy analysts. Rather than concentrate on the uncertainty, let me share my view and experience regarding the skill set that such programs should provide. To be practical, I will share the program that I designed for the Indian School of Business one-year certificate program in BA(*), in terms of content and structure. Both … Continue reading Designing a Business Analytics program, Part 1: Intro

What does “business analytics” mean in academia?

But what exactly does this mean? In the recent ISIS conference, I organized and moderated a panel called “Business Analytics and Big Data: How it affects Business School Research and Teaching“. The goal was to tackle the ambiguity in the terms “Business Analytics” and “Big Data” in the context of business school research and teaching. I opened with a few points: Some research b-schools are posting job ads for tenure-track faculty in “Business Analytics” (e.g., University of Maryland; Google “professor business analytics position” for plenty more). What does this mean? what is supposed to be the background of these candidates … Continue reading What does “business analytics” mean in academia?

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 statistics.com … Continue reading Flipping and virtualizing learning

The mad rush: Masters in Analytics programs

The recent trend among mainstream business schools is opening a graduate program or a concentration in Business Analytics (BA). Googling “MS Business Analytics” reveals lots of big players offering such programs. A few examples (among many others) are: Carnegie Mellon’s Heinz College Michigan State’s Broad School of Business NYU Stern University of Connecticut’s School of Business Rutgers Business School Drexel’s Lebow College of Business These programs are intended (aside from making money) to bridge the knowledge gap between the “data or IT team” and the business experts. Graduates should be able to lead analytics teams in companies, identifying opportunities where … Continue reading The mad rush: Masters in Analytics programs

Forecasting + Analytics = ?

Quantitative forecasting is an age-old discipline, highly useful across different functions of an organization: from  forecasting sales and workforce demand to economic forecasting and inventory planning. Business schools have offered courses with titles such as “Time Series Forecasting”, “Forecasting Time Series Data“, “Business Forecasting“,  more specialized courses such as “Demand Planning and Sales Forecasting” or even graduate programs with title “Business and Economic Forecasting“. Simple “Forecasting” is also popular. Such courses are offered at the undergraduate, graduate and even executive education. All these might convey the importance and usefulness of forecasting, but they are far from conveying the coolness of forecasting. … Continue reading Forecasting + Analytics = ?

Explain or predict: simulation

Some time ago, when I presented the “explain or predict” work, my colleague Avi Gal asked where simulation falls. Simulation is a key method in operations research, as well as in statistics. A related question arose in my mind when thinking of Scott Nestler‘s distinction between descriptive/predictive/prescriptive analytics. Scott defines prescriptive analytics as “what should happen in the future? (optimization, simulation)“. So where does simulation fall? Does it fall in a completely different goal category, or can it be part of the explain/predict/describe framework? My opinion is that simulation, like other data analytics techniques, does not define a goal in … Continue reading Explain or predict: simulation