Some course related Announcements

Hi all,

A few quick administrative annoucements.

1. External speaker Guest lecture lecture:

We're having Mr Milind Chitgupaker come in as guest lecturer to deliver a talk on analytics in MKTR and in general.

His is a battlefield perspective. He's among the brightest programming minds I know, is the owner of 6 technology patents, is a 15 year veteran with IBM's Global consulting division and currently an entrepreneur in the analytics startup space.

You can find more about him and his firm, here.

Here's he on Youtube on the work his firm did in the last General Elections.

I'm happy and proud to say I have been associated in building this startup up from its beginnings and count Milind among my close friends.

Milind gave the MKTR guest lecture last year as well and it was well received. (Don't take my word for it, feel free to ask alums and confirm).

Date: 20-september. Time 7-9 pm. Venue: AC4 LT.

Seats are limited to venue capacity (70). Pls sign up early, on a first come first serve basis, at this signup sheet here.

If you've missed or plan to skip any class, attending this lecture will insure you one attendence loss. For such folks, CP in the lecture counts towards general CP, besides.

2. Pre-reads for future classes:

For session 5, the pre-read is chapter 1 in the book Modeling Techniques for Predictive Analytics (in R) by Thomas Miller.

You can find the PDF here.

Go through the 12 page chapter carefully, its a fairly easy read. A pre-read quiz *may* land in session 5.

Other pre-reads from the coursepack - 'Perceptual Mapping: A manager's guide' (serial number 5) for Session 6;

'Market Segmentation, Target Market Selection and Positioning' (serial no. 6) for Session 7;

And 'How to design smart buisness experiments' (serial no. 9) for session 8.

The coursepack readings are classic HBS articles. There's a chance I may overrule them and assign new readings instead.

3. R tutorial and help:

Some folks have asked me for a small tutorial on R. To gauge interest, please email the AAs with a blank email entitled "Interested in an R tutorial".

The sampling piece we did on R is a very nice intro to doing R on a small problem for the first time, IMO.

Pls find here an old, detailed blogpost explaining my code etc.

If you're planning a more comprehensive and structured deep-dive into R, let me quote from an older blog post from Analytics Yogi below:

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2. I've received student queries about additional sources of material for study. Well, there are two ways about it. If you are presently working on a problem on R and encounter roadblocks, then the best thing is to simply google your query. Chances are sites like Stackoverflow will have answers for it. It usually works very well for me.

On the other hand, if you are looking for a structured way to start, then there are any number of books you could consider getting and starting. Below I list some which can help the rank beginner get started:

A beginner's guide to R from Computerworld, a video introduction to R here from Google and here is a full fledged book from the Springer publishers' stable on how to get started in R.

Better still is this list of links for books on R: Link for list of books and downloads for R. More advanced users, especially after you are introduced to supervised machine learning as part of the CBA program, may want to consider the following books (some of which are free downloads):

Machine Learning with R, by Brett Lantz. The link takes you to the table of contents which you can browse and also through a sample chapter.

This short document from MIT's open courseware on Machine learning is a useful reporsitary of the very basic datasets, algorithms and packages a beginner can use to get started on the machine learning part of R analytics.

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Will putup a QCs analysis from the past two sessions in the next couple of days.

That's it from me for now. Ciao.

Sudhir