Wide range of topics we'd seen in the DC course. Some of you asked for more sources and reading material. Pls find the same below (in no particular order) and totally optional only:
1. Recall the google glass example somebody had raised in class? Well, here's a Gigaom article on the Future of the wearables market.
2. Recall the first example in the network analytics class on world international call patterns? Well, here's the associated Atlantic article on a World mapped by phone calls. It nicely illustrates how much visualization of networks can tell us.
3. More from the Atlantic on how its now technologically feasible to arrive at one's Identity. Big Data Can Guess Who You Are Based on Your Zip Code
4. Recall the habit patterns class we'd covered? Here's an article from HBR blogs on How Customers Get Hooked on Products.
5. There's an undercurrent somewhere in the program that spells the words "data science". This link here offers a rounded perspective on what precisely is data science. This follow-on link here describes 8 concrete steps you must take to become a data scientist. Yes, R features there. Apt read for all CBA students, IMO.
For sessions 1-3 which focussed more on constructs, designing questionnaires around constructs etc., here below is some interesting material which you may consider browsing at leisure. They're basically to help understanding for those folks who may have felt the coverage in class was not detailed enough on certain topics.
This is a Wikipedia link to Quantitative psychology as a subject area. It provides a nice, concise and precise introduction to the area in general and has a good number of downstream links that you can pick up on as and when necessary.
This is the Wiki entry to Scaling techniques in general in the social sciences. As you can see the comparative versus noncomparative dichotomy comes in early on here. More links to detaiuled topics are also available.
This is the wiki entry to psychometrics as a discipline. I thought it a tad too inclined towards educational testing but still, worth a read perhaps, for those interested.
Recall that in one of our sessions (2 or 3?), there was much debate about k-means and other clustering (or, in Marketing speak 'Segmentation') algorithms? There was as well as an element of affinity analysis there.
A conceptual introduction to these terms can be found online as well - for instance, here for market segmentation, for cluster analysis and for affinity analysis in retail analytics.
And of course, there's always google available to produce reports and summaries at varying levels of detail on any subject under the sun.
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These links below are more technical in nature. And are even more optional reading than the ones above. I'd suggest revisiting the below links after a couple of more terms are done in the program.
6. This will be kinda boring to many perhaps. But here's an Academic journal paper on Behavior prediction using social networks
7. And here is an excellent set of slides for computing basic metrics in network data from r-bloggers.com. BTW, you should consider subscribing to their newsletter, if you are into R.
8. More R here. An excellent intro to general R and then some network basics along with code and examples workshop style.
That's it for now. Will update as more comes in. Your Homeworks will be up next. And also, data+R code to replicate classwork examples.
Ciao.
Sudhir
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