
Data collection and management 




1/17 
Introduction 




1/22 
Data collection and scraping 




1/24 
Jupyer notebook lab 




1/29 
Relational data 




1/31 
Visualization and data exploration 




2/5 
Vectors, matrices, and linear algebra 




2/7 
Graph and network processing 




2/12 
Free text and natural language processing 




2/14 
Free text, continued 





Statistical modeling and machine learning 




2/19 
Introduction to machine learing 




2/21 
Linear regression continued 




2/26 
Linear classification 




2/28 
Nonlinear modeling, crossvalidation 




3/5 
Nonlinear modeling (cont), evaluating ML models 




3/7 
Class postponed 




3/12 
Spring break 




3/14 
Spring break 




3/19 
Basics of probability 




3/21 
Maxiumum likelihood estimation, naive Bayes 




3/26 
Hypothesis testing and experimental design 





Advanced modeling techniques 




3/28 
Clustering and dimensionality reduction 




4/2 
Recommender systems 




4/4 
Anomaly detection 




4/9 
Decision trees, interpretable models 




4/11 
Deep learning 





Additional topics 




4/16 
Big data and MapReduce methods 




4/18 
Probabilistic modeling 




4/23 
Debugging data science 




4/25 
A data science walkthrough 




4/30 
Data science walkthrough (pt 2) 




5/2 
The future of data science and Q&A 



