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! I therefore created 30+ videos, covering various supervised methods (k-NN, linear and logistic regression, trees, naive Bayes, etc.) and unsupervised methods (principal components analysis, clustering, association rules), as well as important principles such as performance evaluation, the notion of a holdout set, and more.

I created the videos to support teaching with our textbook “Data Mining for Business Analytics” (the 3rd edition and a SAS JMP edition came out last year; R edition coming out this month!). The videos highlight the key points in different chapters, (hopefully) motivating the watcher to read more in the textbook, which also offers more examples. The videos’ order follows my course teaching, but the topics are mostly independent.

The videos were a big hit in the ISB courses. Since moving to Taiwan, I've created and offered a similar flipped BADM course at National Tsing Hua University, and the videos are also part of the Statistics.com Predictive Analytics series. I’ve since added a few more topics (e.g., neural nets and discriminant analysis).

The audience for the videos (and my courses and textbooks) is non-technical folks who need to understand the logic and uses of data mining, at the managerial level. The videos are therefore about problem solving, and hence the "Business Analytics" in the title. They are different from the many excellent machine learning videos and MOOCs in focus and in technical level -- a basic statistics course that covers linear regression and some business experience should be sufficient for understanding the videos.

For 5 years, and until last week, the videos were only available to past and current students. However, the word spread and many colleagues, instructors, and students have asked me for access. After 5 years, and in celebration of the first R edition of our textbook Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, I decided to make it happen. All 30+ videos are now publicly available on my BADM YouTube playlist.

Currently the videos cater only to those who understand English. I opened the option for community-contributed captions, in the hope that folks will contribute captions in different languages to help make the knowledge propagate further.

This new playlist complements a similar set of videos, on "Business Analytics Using Forecasting" (for time series), that I created at NTHU and made public last year, as part of a MOOC offered on FutureLearn with the next round opening in October.

Finally, I’ll share that I shot these videos while I was living in Bhutan. They are all homemade -- I tried to filter out barking noises and to time the recording when ceremonies were not held close to our home. If you’re interested in how I made the materials and what lessons I learned for flipping my first course, check out my 2012 post.

New short guide: "To Publish or To Self-Publish My Textbook?"

My self-publishing endeavors have led to a growing number of conversations with colleagues, friends, colleagues-of-friends and other permutations who've asked me to share my experiences. Finally, I decided to write down a short guide, which is now available as a Kindle eBook.

To Publish or To Self-Publish My Textbook? Notes from a Published and Self-Published Author gives a glimpse into the expectations, challenges, rewards, and surprises that an author experiences when publishing and/or self-publishing a textbook. This is not a guide on self-publishing, but rather notes about the process of publishing a textbook with a big publisher vs. self-publishing and what to expect.

To celebrate the launch, the eBook is FREE for 72 hours. Post the promotion it will still be cheaper than a cappuccino.

You can read the book (and any other Kindle book) on many devices -- no need for a Kindle device. You can use the Kindle Cloud Reader for online reading, or else download the free Kindle reading app for PC, iPad, Android, etc.

Self-publishing to the rescue

The new Coursera course by Princeton Professor Mung Chiang was so popular that Amazon and the publisher ran out of copies of the textbook before the course even started (see "new website features" announcement; requires login). I experienced a stockout of my own textbook ("Data Mining for Business Intelligence") a couple of years ago, which caused grief and slight panic to both students and instructors.

With stockouts in mind, and recognizing the difficulty of obtaining textbooks outside of North America (unavailable, too expensive, or long/costly shipping), I decided to take things into my own hands and self-publish a "Practical Analytics" series of textbooks. Currently, the series has three books. All are available in soft-cover editions and Kindle editions. I used CreateSpace.com, an Amazon company, for publishing the soft-cover editions. This reduces the stockout problem due to a print-on-demand model. I used Amazon KDP for publishing the Kindle editions, so definitely no stockouts there. Amazon makes the books available on its global websites and so reachable in many places worldwide (the Indian Flipkart also avails the books). Finally, since I got to set the prices, I made sure to keep them affordable (for example, in India the e-books are even cheaper than in the USA).

How has this endeavor fared? Well, more than 1000 copies were sold since March 2011. Several instructors adopted books for their courses. And from reader emails and ratings on Amazon, it looks like I'm on the right track.

To celebrate the power and joy of self-publishing as well as accessible and affordable knowledge, I am running a "free e-book promotion" next week. The following e-books will be available for free:

• Practical Acceptance Sampling will be available for free on Monday, September 24, 2012. 
• Practical Risk Analysis for Project Planning will be available for free on Tuesday, September 25, 2012.

Both promotions will commence a little after midnight, Pacific Standard Time, and will last for 24 hours. To download each of the e-books, just go to the Amazon website during the promotion period and search for the title. You will then be able to download the book for free.

Enjoy, and feel free to share!

Launched new book website for Practical Forecasting book

Last week I launched a new website for my textbook Practical Time Series Forecasting. The website offers resources such as the datasets used in the book, a block with news that pushes posts to the book Facebook page, information about the book and author, for instructors an online form for requesting an evaluation copy and another for requesting access to solutions, etc.

I am already anticipating my colleagues' question "what platform did you use?". Well, I did not hire a web designer, nor did I spend three months putting the website together using HTML. Instead, I used Google Sites. This is a great solution for those who like to manage their book website on their own (whether you're self-publishing or not). Very readable, clean design, integration with other Google Apps components (such as forms), and as hack-proof as it gets. Not to mention easy to update and maintain, and free hosting.

Thanks to the tools and platforms offered by Google and Amazon, self-publishing is not only a good realistic option for authors. It also allows a much closer connection between the author and the book users -- instructors, students and "independent" readers.

Start the Revolution

Variability is a key concept in statistics. The Greek letter Sigma has such importance, that it is probably associated more closely with statistics than with Greek. Yet, if you have a chance to examine the bookshelf of introductory statistics textbooks in a bookstore or the library you will notice that the variability between the zillions of textbooks, whether in engineering, business, or the social sciences, is nearly zero. And I am not only referring to price. I can close my eyes and place a bet on the topics that will show up in the table of contents of any textbook (summaries and graphs; basic probability; random variables; expected value and variance; conditional probability; the central limit theorem and sampling distributions; confidence intervals for the mean, proportion, two-groups, etc; hypothesis tests for one mean, comparing groups, etc.; linear regression) . I can also predict the order of those topics quite accurately, although there might be a tiny bit of diversity in terms of introducing regression up front and then returning to it at the end.

You may say: if it works, then why break it? Well, my answer is: no, it doesn't work. What is the goal of an introductory statistics course taken by non-statistics majors? Is it to familiarize them with buzzwords in statistics? If so, then maybe this textbook approach works. But in my eyes the goal is very different: give them a taste of how statistics can really be useful! Teach 2-3 major concepts that will stick in their minds; give them a coherent picture of when the statistics toolkit (or "technology", as David Hand calls it) can be useful.

I was recently asked by a company to develop for their managers a module on modeling input-output relationships. I chose to focus on using linear/logistic regression, with an emphasis on how it can be used for predicting new records or for explaining input-output relationships (in a different way, of course); on defining the analysis goal clearly; on the use of quantitative and qualitative inputs and output; on how to use standard errors to quantify sampling variability in the coefficients; on how to interpret the coefficients and relate them to the problem (for explanatory purposes); on how to trouble-shoot; on how to report results effectively. The reaction was "oh, we don't need all that, just teach them R-squares and p-values".

We've created monsters: the one-time students of statistics courses remember just buzzwords such as R-square and p-values, yet they have no real clue what those are and how limited they are in almost any sense.

I keep checking on the latest in statistics intro textbooks and see exercpts from the publishers. New books have this bell or that whistle (some new software, others nicer examples), but they almost always revolve around the same mishmash of topics with no clear big story to remember.

A few textbook have tried going the case-study avenue. One nice example is A Casebook for a First Course in Statistics and Data Analysis (by Chatterjee, Handcock, and Simonoff). It presents multiple "stories" with data, and how statistical methods are used to derive some insight. However, the authors suggest to use this book as an addendum to the ordinary teaching method: "The most effective way to use these cases is to study them concurrently with the statistical methodology being learned".

I've taught a "core" statistics course to audiences of engineers of different sorts and to MBAs. I had to work very hard to make the sequence of seemingly unrelated topics appear coherent, which in retrospect I do not think is possible in a single statistics course. Yes, you can show how cool and useful the concepts of expected value and variance are in the context of risk and portfolio management, or how the distribution of the mean is used effectively in control charts for monitoring industrial proceses, but then you must move on to the next chapter (usually sampling variance and the normal distribution), thereby erasing the point by piling on it totally different information. A first taste of statistics should be more pointed, more coherent, and more useful. Forget the details, focus on the big picture.

Bring on the revolution!