Syllabus
This syllabus is still under development and is subject to change.
Week  Lecture  Date  Topic  Lab  Discussion  Homework 

1  1  1/22/19 
Course Overview, Motivating/Defining Data Science, Logistics [slides]


2  1/24/19 
Study Design [slides]


2  3  1/29/19 
Data Tables with Pandas [slides]



4  1/31/19 
Data Cleaning [slides]


3  5  2/5/19 
Visualization I [slides]



6  2/7/19 
Visualization II [slides]


4  7  2/12/19 
EDA & Working with Text [slides]



8  2/14/19 
SQL [slides]


5  9  2/19/19 
Dimensionality Reduction [slides]



10  2/21/19 
PCA [slides]


6  11  2/26/19 
Case Study [slides]



12  2/28/19 
Midterm 1


7  13  3/5/19 
Foundations of Statistical Inference (Slides updated 03/12/2019) [slides]


14  3/7/19 
Foundations of Statistical Inference


8  15  3/12/19 
Linear Regression and Feature Engineering [slides]



16  3/14/19 
Gradient Descent for Risk Optimization (Slides updated 03/19/2019) [slides]


9  17  3/19/19 
Risk Optimization and BiasVariance TradeOff [slides]



18  3/21/19 
CrossValidation and Regularized Regression


10  19  3/26/19 
Spring Break


20  3/28/19 
Spring Break


11  21  4/2/19 
Logistic Regression [slides]



22  4/4/19 
Classification [slides]


12  23  4/9/19 
Prediction: Classification and Regression [slides]



24  4/11/19 
Midterm 2


13  25  4/16/19 
Prediction: Classification and Regression [slides]


26  4/18/19 
Inference about Models [slides]


14  27  4/23/19 
Big Data & Spark [slides]



28  4/25/19 
Distributed Computing (Guest) [slides]


15  29  4/30/19 
Ethics of Data Science (Guest)


30  5/2/19 
Review and Conclusion [slides]


16  31  5/7/19 
RRR week


32  5/9/19 
RRR week


17  33  5/14/19 


34  5/16/19 
Final Exam (11:30am2:30pm)
