Syllabus

This syllabus is still under development and is subject to change.

Week Lecture Date Topic Lab Discussion Homework
1 1 6/24/2019

Introduction to Data Science, Logistics, Study Design

HW1 (due 06/25 @ 11:59PM)

2 06/25/2019

Data Tables with pandas

3 06/26/2019

Data Tables with pandas

4 06/27/2019

Data Cleaning

2 5 07/01/2019

Visualization

6 07/02/2019

Visualization

7 07/03/2019

EDA & Working with Text

8 07/04/2019

Holiday (no class)

3 9 07/08/2019

SQL

10 07/09/2019

Dimensionality Reduction

11 07/10/2019

PCA

12 07/11/2019

Case Study

4 13 07/15/2019

Midterm Review

14 07/16/2019

Midterm

15 07/17/2019

Foundations of Statistical Inference

16 07/18/2019

Foundations of Statistical Inference

5 17 07/22/2019

Linear Regression and Feature Engineering

18 07/23/2019

Gradient Descent

19 07/24/2019

Risk Optimization and Bias-Variance

20 07/25/2019

Cross-Validation and Regularization

6 21 07/29/2019

Logistic Regression

22 07/30/2019

Classification

23 07/31/2019

Prediction: Classification and Regression

24 08/01/2019

Prediction: Classification and Regression

7 25 08/05/2019

Inference about Models

26 08/06/2019

Big Data and Spark

27 08/07/2019

Case Studies

28 08/08/2019

Conclusion

8 29 08/12/2019

Final Review

30 08/13/2019

Final Review

31 08/14/2019

Final Review

32 08/15/2019

Final (9:30am - 12:30pm)