This is a tentative schedule and will be updated as the course progresses.
- January 10 - Overview and start Python Basics
- January 12 - Finish Python Basics and Collections
- January 17 - Finish Collections and start Control Flow
- January 19 - The rest of Control Flow Lab session on PS 1, Jupyter, Syzygy, etc.
- January 24 - Functions, preparation for PS2 (C-D function, returns to scale, etc), Numpy
- January 26 - PS1 review, Numpy
- January 31 - Net present value stuff (preparation for PS 3), Plotting Intro
- February 2 - Linear Algebra
- February 7 - Randomness
- February 9 - PS2 review, Optimization
- February 14 - PS3 review, Pandas Intro
- February 16 - Pandas Basics, Cleaning Data
- February 21 - Midterm Break
- February 23 - Midterm Break
- February 28 - Reshape, Merge, Groupby
- March 2 - More examples of Merge, Groupby, Timeseries
- March 7 - PS4 review, Timeseries continued, Visualization
- March 9 - Visualization continued, Mapping
- March 14 - PS5 review, Overview of the rest of the lectures; Regression
- March 16 - Regression: LASSO, Ridge, Cross-validation
- March 21 - Regression: Random Forest, Neural Network
- March 23 - PS6 review, Classification
- March 28 - Application: recidivism, Brainstorming session for the final project
- March 30 - PS7 review, introduction of natural language processing and Working with text
- April 4 - Machine learning in economics
- April 6 - Heterogeneous effect
Important: work on the problem sets in the course git repository, not the ones from quantecon.org! Do not do the quantecon.org problem sets that appear in lecture-datascience.notebooks/problem_sets!.
- Due January 21 - Problem Set 1 (uploaded as executed ipynb through Canvas)
- Due January 31 - Problem Set 2
- Due February 9 - Problem Set 3
- Due February 18- Problem Set 4
- Due March 7 - Problem Set 5
- Due March 14 - Problem Set 6
- Due March 23 - Problem Set 7
- Optional Due March 29 Problem Set 8