# Lecture 12: Causality & Experiments, Part 2

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.

Continue reading# Lecture 11: Causality & Experiments, Part 1

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

Continue reading# Homework 3

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

Continue reading# Lecture 10: Networks

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.

Continue reading# Lectures 8 & 9: Classification

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

Continue reading# Homework 2

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

Continue reading# Lecture 7: Regression, Part 2

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

Continue reading# Lecture 6: Regression, Part 1

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

Continue reading# Lecture 5: Data Visualization

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

Continue reading# Lecture 4: Counting at Scale

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

Continue reading# Homework 1

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

Continue reading# Lecture 3: Computational complexity

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

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