This repository stores copies of Jupyter notebooks and data that are used for a half-semester course on data anaylsis with Python. These materials are based on the online textbook available at https://wesmckinney.com/book.
For Fall 2023
The following schedule should be used as a guide for keeping in time for turning work in. This is a self-paced class for the most part, however, and the notebooks are meant to be interactive --- you should be filling in blanks in the text and in the code, and answering questions throughout each notebook. There is an associated Google Classroom on which I will provide feedback and grades for the completed notebooks.
- Monday, October 23rd: Class canceled. Get started with 00 - Pre-class.ipynb.
- Wednesday, October 25th: Symposium day. No class. Read Chapters 1-3 in our textbook.
- Friday, October 27th: First day in-class. 01 - Using_Jupyter.ipynb.
- Monday, October 30th: 02 - Getting_Familiar_w_Python.ipynb.
- Wednesday, November 1st: 03 - Getting_Familiar_w_Python_continued.ipynb.
- Friday, November 3rd: 04 - Numpy_Arrays.ipynb.
- Monday, November 6th: 05 - Pandas_DataFrames.ipynb.
- Wednesday, November 8th: 06 - Pandas_DataFrames_pt2.ipynb.
- Friday, November 10th: 07 - Advanced_Cleaning_Techniques.ipynb.
- Monday, November 13th: 08 - Modeling_Techniques.ipynb.
- Wednesday, November 15th: 09 - Modeling_Techniques_pt2.ipynb.
- Friday, November 17th: A Demo of Exploration and Analysis with 11 - Movie_Lens_DA_Notebook.ipynb.
- Monday, November 20th - Friday, November 24th: Break
- Monday, November 27th: Lecture on Developing Research Questions
- Wednesday, November 29th: 10 - Data_Viz_FTW.ipynb
- Friday, Dec 1st - Wednesday, Dec 6th: Work on project.
- Friday, Dec 8th - Wednesday, Dec 13th: Presentations.