Konstantin Kashin

Institute for Quantitative Social Science, Harvard University

Konstantin Kashin is a Fellow at the Institute for Quantitative Social Science at Harvard University and will be joining Facebook's Core Data Science group in September 2015. Konstantin develops new statistical methods for diverse applications in the social sciences, with a focus on causal inference, text as data, and Bayesian forecasting. He holds a PhD in Political Science and an AM in Statistics from Harvard University.

Posts Tagged “data visualization”

  • Data Visualization & Databits

    I am a big fan of interactive data visualization for data exploration and presenting research findings, and thus I wanted to share a new site [Sergiy Nesterko](http://nesterko.com/) recently launched called [Databits](http://databits.io/). The site features interactive visualizations built using a variety of tools (primarily D3.js, although there are a few recently examples in Processing.js), and the goal is to get a community of people interested in data visualization to showcase their work (including open-source code), interact with one another, and hopefully hone their skills in the process! It's very much in its initial stages, but please join and contribute if you have visualizations you want to share.

    I've uploaded a few of my visualizations [here](http://databits.io/kkashin) and hope to upload more in the near future. One of the nice features of Databits is the ease with which you can embed a visualization in another website:



    This visualization is part of the broader [HarvardX Insights](http://harvardx.harvard.edu/harvardx-insights) project I contributed to.
  • Network Visualization with D3.js

    Here is a visualization I constructed using D3.js based on a visualization for Harvard's Stat 221 class of a network of individuals for whom HIV status is known (original visualization [here](http://theory.info/harvardstat221#?v=network-of-individuals-at-risk-of-hiv)). I wanted the visualization to maximally exploit the information available in the data, such as for example whether friendships are mostly seroconcordant (with individuals of the same HIV status) or serodiscordant. I also wanted to see if most friendships were of the same gender or not. Hence, I adapted the [hive plot template](http://bost.ocks.org/mike/hive/) for this network data. Here is a static picture of the resultant network: