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).
I do realize I got delayed in putting them out and some of you had followed up with me regarding this. 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.
+++
More reading links (optional) that I could gather, below. Recall the Xerox-evolv caselet we did in class - The one where psychographic Likerts were combined with straightforward machine learning? We then discussed implications, pros and cons etc, and speculated on when such practices may spread worldwide and into India. Well, speaking of India...
Startups, and India Inc use psychometric tests to peek into potential recruits’ minds
This is the session 5 reading from NYT on 'Data janitor' work: For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights
This is the source for the session 5 table on Data munging functions in R from computer world: Great R packages for data import, wrangling & visualization
This is the NYT article on 'the data driven life' about the quantified self movement.
This is the API directory from Programmable web. Recommended that as CBA folks, you register with the site and get periodic updates (if you haven't already). +++
You have 2 more group homeworks coming - one on webscraping using the rvest package and the other on python for scraping google search results. I'm teaching PGP currently, so posting a tad slow, but will put these HWs out there soon.
Any Qs etc, let me know.
Sudhir
No comments:
Post a Comment