Course Outline

Week	Date	Topic	Lecture Notebook	Lecture Slides	Lab Notebook	Due Dates
1	4/14	Intro and History of Data Science	DS_Lec01-Intro	DS_Lec01-Intro.slides DS_Lec01-Python-Intro.pdf	DS_Lab01-Python-Numpy
	4/16	Python		DS_Lec02-Python-Libraries.pdf DS_Lec02-Git.pdf	DS_Lab02-Git.pdf DS_Lab02-Python-Pandas
2	4/21	SQL and Relational Theory	DS_Lec03-RDBMS	DS_Lec03-RDBMS.slides	DS_Lab03-RDBMS
	4/23	APIs, JSON, and Mongo	DS_Lec04-API	DS_Lec04-API.slides	DS_Lab04-API	Preliminary Project Proposals
3	4/28	Probability & Statistics	DS_Lec05-Stats	DS_Lec05-Stats.slides	DS_Lab05-Stats	Week 2 Homework
	4/30	Machine Learning, Multiple Linear Regression, ANOVA	DS_Lec06-ML	DS_Lec06-ML.slides	DS_Lab06-ML
4	5/5	Guest Speaker: Paco Nathan		Nine Decades of Machine Learning
	5/7	Choice Modeling & Logistic Regression		Introduction to Choice Modeling	Lab - Choice Modeling	Formal Project Proposals
5	5/12	Bayes, Text Mining, and Cross Validation	DS_Lec09-Bayes	DS_Lec09-Bayes.slides	DS_Lab09-TM
	5/14	Time Series Analysis & Regularization	DS_Lec10-Midterm	DS_Lec10-Midterm.slides	DS_Lab10-Regularization
6	5/19	Principal Components Analysis	DS_Lec11-PCA	DS_Lec11-PCA.slides	DS_Lab11-PCA	DS_HW09-NB-Classifier
	5/21	Project Elevator Pitches				Projects Live on Github
7	5/26	NO CLASS	—	—	—	Midterm Assessment Due
	5/28	Clustering: Hierarchical and K-Means				Peer Feedback Due
8	6/2	Guest Lecture TBD
	6/4	Nonlinear Models & Model Selection				Working Model
9	6/9	IPython.parallel & StarCluster
	6/11	Hadoop
10	6/16	Guest Lectures & Workshop
	6/18	Guest Lectures & Workshop
11	6/23	Final Presentations
	6/25	Final Presentations
12	6/30	Future Directions