Charting human progress with data science.
The objective of this session is to talk about the challenges that come up, when one tries to mix data science with social science. We’re going to look at how advances in mathematical and statistical computing have actually made this possible, and we’re going to touch upon the key problem areas one might face, when trying to use data science to answer a question of social significance.
We’re going to first talk about how various fields have come up - cliodynamics, econometrics, the like - fields that use intensive mathematical and statistical constructs, to analyze social and economic structures. We’re also going to discuss how recent advances in computing, have made - and are making - these fields blow up in terms of what they can now help us analyze and answer.
Next, we’re going to talk about key problems that a data scientist and a social scientist face, when they put their skills together to attack and analyze a particular problem statement. Broadly, these 4 areas are:
A lot of the points we cover here, are from personal experience, as well as takeaways culled from research being undertaken across the world, in this regard.
Finally, we’re going to try out a sample problem - predicting and analyzing racial segregation using math - to get some sort of exposure to the assumptions one makes, and the consequences of said assumptions, while trying to fit human actions into a mathematical model.
I’m Rudraksh, with a lot of experience in using computational mathematics for a lot of different real-world problems. I’ve worked with IBM and the Hindustan Times, been a part of various startups, one of which was all about computational science, and I currently work with HasGeek, where I help build discussion spaces for geeks, through technology and high-powered events and conferences.