This was our second lecture on causality and experimentation, in which we discussed statistical inference and reproducibility for randomized experiments as well as the design and analysis of natural experiments.

This was a joint guest lecture from Andrew Mao and Amit Sharma with an overview of causal inference and randomized experiments.

The third homework assignment, posted on Github, is due on Monday, April 24 by 11:59pm ET.

We spent this lecture discussing network data, including a whirlwhind tour of the history of network theory, representations and characteristics of networks, and algorithms for analyzing network data.

This post covers two lectures on classification, the first a guest lecture from Chris Wiggins.

The second homework assignment, posted on Github, is due on Monday, March 27 by 11:59pm ET.

This was the second lecture on the theory and practice of regression, focused on model complexity and generalization.

This was the first of two lectures on the theory and practice of regression.

We had a guest lecture from Çağatay Demiralp on data visualization.

In this lecture we discussed combining and reshaping data in R as well as counting at scale with MapReduce.

The first homework assignment, posted on Github, is due on Thursday, February 23 by 11:59pm ET.

We had a guest lecture from Sid Sen on computational complexity and algorithm analysis.

Counting is surprisingly useful for understanding and summarizing social data. The key is figuring out what to count and how to count it efficiently.

We used our first lecture to look at case studies in four main areas: exploratory data analysis, classification, regression, and working with network data.

This class will involve a good deal of coding, for which you will need some basic tools. Please make sure to set up the following tools after the first day of class.