Hi Class,
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). Some readings are mandatory and others are totally optional only.
The readings below are NOT optional in the sense that questions based on these readings may feature in your exam.
Readings relating technology to data collection and data use (from the Economist):
1. The first article titled 'Little Brother' (in an obvious play on George Orwell's famous 'big Brother' theme) details the impact of digital on advertising spends of firms worldwide.
2. The second article, 'Getting to know you', is about the various ways in which data is collected about consumers online.
3. The third article in this series, 'The world wild web', extrapolates some of what we are seeing into the future and asks 'Where are we going?'.
Ideally, I'd like you to read and discuss these articles within your groups. Again, remember, questions based on ideas and facts in these articles are fair game in your final exam for DC. Happy reading.
Now, these readings that follow below are optional, more for leisure reading and folks with interest in particular topics/ verticals etc.
a. 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.
b. Recall the habit patterns class we'd covered? Here's an article from HBR blogs on How Customers Get Hooked on Products.
c. 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.
d. 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:
i. 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.
ii. 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.
iii. 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.
h. 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.
h. This will be kinda boring to many perhaps. But here's an Academic journal paper on Behavior prediction using social networks.
If you have come across such material which may be of interest to the class, you may email me or put up links to that material in the comments section below.
For instance, Nikhil Maddirala from the previous batch emailed me with information regarding a useful webscraping tool. Recall the Chrome scraper extension/ plug in tool I showed you in class? Well, it seems there's a way to tweak the tool to scrape multiple pages in one go.
See this link here on Scraping multiple Pages using the Scraper Extension and Refine.
Ciao
Sudhir
