Book Review: R for Business Analytics, A Ohri

      I've added a recently released book to my list of recommendations (at the amazon carousel to the right), as I've reviewed a copy provided to me via Springer Publishers. The book is R for Business Analytics, authored by A Ohri.  Mr. Ohri provides us with a brief background of his own journey as a business analytics consultant, and shares how R helped complement his work with a very low cost (time to learn the software) and very large benefits.  At the outset, he emphasizes that the book is not geared towards statisticians, but more towards practicing business analytics professionals, MBA students, and pragmatically oriented R neophytes and professionals alike. In addition, there is a focus on using GUI oriented tools towards assisting users in quickly getting up to speed and applying business analysis tools (Rattle, for example, is covered as an alternative to Weka, which has been covered here previously).  In addition, he provides numerous interviews with well known company representatives who have either successfully integrated R into their own development flow (including JMP/SAS, Google, and Oracle ), or found that large groups of customers have utilized R to augment their existing suite of tools.  The good news is that many of the large companies do not view R as a threat, but as a beneficial tool to assist their own software capabilities.

     After assisting and helping R users navigate through the dense forest of various GUI interface choices (in order to get R up and running), Mr. Ohri continues to handhold users through step by step approaches (with detailed screen captures) to run R from various simple to more advanced platforms (e.g. CLOUD, EC2) in order to gather, explore, and process data, with detailed illustrations on how to use R's powerful graphing capabilities on the back-end.

     The book has something for both beginning R users (who may be experienced in data science, but want to start learning how to apply R towards their field), and experienced R users alike (many, like myself, may find it useful to have a very broad coverage of the myriad number of packages and applications available, complemented by quickly accessible tutorial based illustrations).  In summary, the book has an extremely broad coverage of R's many packages that can be used towards business data analysis, with a very hands on approach that can help many new users quickly come up to speed and running on utilizing R's powerful capabilities. The only potential down-side is that covering so many topics, comes at a cost of sacrificing some depth and mathematical rigor (leaving the door open for readers to pursue several more specialized R texts).

The Kaggle Bug

If you have any interest in data mining and machine learning, you might have already caught the Kaggle bug.

I myself fairly recently got caught up in following the various contests and forums after reading a copy of "Practical Time Series Forecasting," -- 2nd edition, by
Galit Shmueli. What makes the contests great are that they allow any ambitious and creative data scientist or amateur enthusiast to participate in and learn a wealth of new knowledge and tricks from more experienced professionals in the field.

What should make it even more interesting to readers here is considering that many of the winners that participate in these high purse contests are often from the financial world. Take one of my personally inspirational traders, Jaffray Woodriff, hedge fund manager of well-known machine learning oriented hedge fund, Quantitative Investment Management (better known by its acronym - QIM). I had mentioned recently to a surprised friend, that Mr. Woodriff had also participated in the more well-known Netflix prediction contest (having been a member of the third-place team at one point).

In particular, the most recent contest that has many eager followers watching is the $3,000,000 Heritage Provider --Heritage Health Prize Competition, which is an open contest to predict likelihood of patient hospital admission. What particularly inspired this blog post is a very useful blog from one of the leading contestants, Phil Brierley a.k.a. handle, Sali Mali, who has interestingly joined with the marketmaker team, also affiliated with a prediction related fund. Mr. Brierley has shared tremendously useful insights about practical methods of attacking the problem-- all the way from SQL preprocessing and cleaning to intuitive visualization methodologies. I applaud him for his generous sharing of insights to the rest of the predictive analytics community.  Although he hasn't posted in a while, his journal of thoughts are still highly useful.

Anyone looking for grubstake could certainly use three million to get started=)

Below are the specific links mentioned…

http://anotherdataminingblog.blogspot.com/
http://www.heritagehealthprize.com/c/hhp
http://www.kaggle.com/

...and one newer from stack exchange
 http://blog.stackoverflow.com/2012/08/stack-exchange-machine-learning-contest/?cb=1


 

The Facebook Doomsday Watch

I've been following the myriad circus of Facebook commentators and bystanders pointing to its horrific failed IPO launch and seemingly inevitable crash to zero. While my focus here isn't really so much on fundamentals or basic TA; I do want to comment on some subjective thoughts on the matter as well as illustrate one catchy graphic I put together.

 

Fig 1. FB IPO drawdown (with potential trajectory) vs. EBAY historical IPO opening (a.k.a the U-TURN pattern).

Having lived through and experienced the many ballyhooed IPO juggernauts of the past, I can't help but think back to how overvalued I 'felt' stocks like Ebay and Google felt to me at the outset.  We all know that that we can't directly compare such small samples in any statistical manner with much conviction, but that qualitative sense in me feels that Facebook is one of those Wall Street darlings we rarely encounter and wish we could go back and buy at a discount.  Sure there were the megaflops (Blackstone, The Globe, etc) that never revived quite back, but then again consider the Lynch method of buying (...are the masses using it?), the massive institutional support available, the number of shorts that are sure to pile on, and more importantly, the nagging fact that it is consistently one of the highest viewed websites of all (typically above or next to Google and Baidu -- don't believe me, check Alexa).  Ok, but enough of the soapbox on those biased musings-- one quantitative comparison to consider in the chart above is how Ebay fared at the outset and was also lambasted as a failure throughout Internet chat-rooms and various media pundits.  What I have graphed is a drawdown for both (with adjusted ebay quotes) relative to the 1st day open bid.  The last points on Facebook are potential drawdown (relative to IPO opening price) trajectories  at

28.84	-31.41% (yesterday's close)
26.5	-36.98%
24	-42.93%
23	-45.30%

So, I leave you with that as food for thought.  I don't often discuss my thoughts about semi qualitative opportunities, but then again, we don't get these types of juggernaut long term opportunities all that often.*  As always, please make your own informed decisions, and I'll try to get back on topic...  one of these days.

* Two other counter points (amongst many excluded) that I'm sure some more astute observers will note.
1) Ebay IPO U-Turn occurred during the mega bull run dot com mania.
2) If the Greece (or insert any suitable catalyst here) fiasco escalates into a fat tail flight to safety avalanche (of which I pointed out have been exceedingly abundant of late); then keep in mind Facebook and any other equity leaders should be expected to plunge together; hence, the emphasis on LONG term  portfolio component opportunity.