# Homework 4

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

Continue reading# Lecture 10: Networks

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

Continue reading# Lecture 9: Classification

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

Continue reading# Homework 3

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

Continue reading# Lecture 8: Regression, Part 2

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

Continue reading# Lecture 7: Regression, Part 1

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

Continue reading# Lecture 6: Reproducibility and replication, Part 2

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

Continue reading# Homework 2

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

Continue reading# Lecture 5: Reproducibility and replication, Part 1

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.

Continue reading# Lecture 4: Data Visualization

We used this lecture to discuss data manipulation and data visualization in R, specifically focusing on `dplyr`

and `ggplot2`

from the `tidyverse`

.

# Lecture 3: Computational complexity

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

Continue reading# Homework 1

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

Continue reading# Lecture 2: Introduction to Counting

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

Continue reading# Lecture 1: Overview

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

Continue reading# Installing tools

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.

Continue reading