Center for Informed Democracy & Social - cybersecurity (IDeaS)

This course builds on the principles and methods of statistical reasoning developed in 36-200 (or its equivalents). The course covers simple and multiple regression, basic analysis of variance methods, logistic regression, and introduction to data mining including classification and clustering. Students will also learn the principles of overfitting, training vs testing, ensemble methods, variable selection, and bootstrapping. Course objectives include applying the basic principles and methods that underlie statistical practice and empirical research to real data sets and interdisciplinary problems. Learning the Data Analysis Pipeline is strongly emphasized through structured coding and data analysis projects. In addition to three lectures a week, students attend a computer lab once a week for hands-on practice of the material covered in lecture. There is no programming language pre-requisite. Students will learn the basics of R Markdown and related analytics tools. Not open to students who have received credit for: 36-208/70-208. Students who have completed or are enrolled in 36-401 prior to completing 36-202, are not able to take/receive credit for 36-202.

PREREQUISITES

36200 or 36201 or 36207 or 36220 or 70207 or 36247