Below is a list of the final projects for the Spring 2019 semester, including a link to the original paper, the students’ final report, and all code and data necessary to reproduce the final report.

In this lecture we discussed causal inference, randomized experiments, and natural experiments.

We spent this lecture discussing representations and characteristics of networks and algorithms for analyzing network data.

The fourth homework assignment, posted on Github, is due on Thursday, April 25 by 11:59pm ET.

We used this lecture to first go through applications of logistic regression and then to discuss the history of network science.

In this lecture we covered classification with linear models, specifically naive Bayes and logistics regression.

The third homework assignment, posted on Github, is due on Thursday, April 11 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.

This was our second lecture on reproducibility and replication in which we discussed false discoveries, effect sizes, and p-hacking / researcher degrees of freedom.

The second homework assignment, posted on Github, is due on Thursday, March 14 by 11:59pm ET.

We discussed the ongoing replication crisis in the sciences, wherein it has proven difficult or impossible for researchers to independently verify results of previously published studies.

We used this lecture to discuss data manipulation and data visualization in R, specifically focusing on dplyr and ggplot2 from the tidyverse.

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

The first homework assignment, posted on Github, is due on Thursday, February 21 by 11:59pm ET.

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.