Course Schedule

Date	Topics & Slides	Workshops	Assignments
14 & 16 Jan.	What Is Data? & Responsible Data Collection	Jupyter
21 & 23 Jan.	Python & Data Wrangling	Movies
28 & 30 Jan.	Exploratory Data Analysis & Visualization	Movies	Documentation Assignment Due 30 Jan.
4 & 6 Feb.	Hypothesis Testing: Comparison of Means	Sports
11 & 13 Feb.	Hypothesis Testing: Correlation	Sports
18 & 20 Feb.	Linear Regression	Business	Take-Home Test I Due 20 Feb.
25 & 27 Feb.	Logistic Regression	Business
4 & 6 Mar.	K-Nearest Neighbors	Health	Tutorial Assignment Due 6 Mar.
11 & 13 Mar.	SPRING BREAK
18 & 20 Mar.	Naive Bayes Classifier
25 & 27 Mar.	Decision Trees and the Random Forest	Health	Take-Home Test II Due 25 Mar.
1 & 3 Apr.	Clustering and Unsupervised Approaches	Literature	Project Proposal Due 3 Apr.
8 & 10 Apr.	Neural Networks	Literature
15 & 17 Apr.	Ethical Data Science & Project Discussion		Progress Report Due 15 Apr.
22 & 24 Apr.	Sample Final Project & Project Discussion		Video Presentations Due 22 Apr.
1 May 2-5pm	Panel Presentations		Final Project Due 1 May at 2pm

Note on the schedule

Keep in mind that some of this schedule could change throughout the semester. However, if anything changes I’ll update this page, and I’ll be sure to give you plenty of advance notice.

Software

All projects in this course will be scripted and analyzed using Python, an open source programming language and environment. Specifically, we will be using JupyterHub as our programming environment. No previous experience with Python, statistical software packages, or computer programming is required.