Class Schedule

Date, Rm	Topic	Do Before Class	In-Class Exercise
Tues, Aug 27, Rm 270	Intro	N/A
Thurs, Aug 29, Rm CC	Command Line Basics	Command Line Basics Read and sign syllabus Register with IPUMS Register for DataCamp	Link
Tues, Sep 03, Rm 270	Advanced Command Line Jupyter Lab / Notebooks	Advanced Command Line Setup Python Jupyter Tutorial	Link Link 2
Thurs, Sep 05, Rm 270	Ipython Packages Python v. R / variables as pointers	Follow this link , then click “Ipython - Beyond plain python” and read that notebook. Python packages variables v objects	Link
Tues, Sep 10, Rm 330	Numpy Basics	JVP pp 33-77 IF did not complete DataCamp: Do Numpy Section Numbers in Computer	Link
Thurs, Sep 12, Rm 270	Pandas: Series	Pandas 1: Series	Link
Tues, Sep 17, Rm CC	Pandas: DataFrames	Pandas 2: DataFrames	Link
Thurs, Sep 19, Rm CC	Intro to Plotting with PlotNine	Plotting in Python, Part 1	Link
Tues, Sep 24, Rm 330	Advanced Plotting	Plotting in Python, Part 2	Link
Thurs, Sep 26, Rm 270	Pandas: Indices & Missing	WM pp 136-142 JVP pp 115-139 Views and Copies in Pandas	Link 1 Link 2
Tues, Oct 01, Rm 270	Pandas: Loading and saving data Pandas: Cleaning	WM Chapter 6 WM Chapter 7 Python Strings	Link
Thurs, Oct 03, Rm 330	Pandas: Merging	JVP pp 149 - 157	Link
Tues, Oct 08, Rm	FALL BREAK
Thurs, Oct 10, Rm 330	Collaborating using Github	Git and Github	Link Link
Tues, Oct 15, Rm 270	Git and Github 2
Thurs, Oct 17, Rm 330	Big Data: What is it, how do I work with it?	What is Big Data? Strategies for Big Data Download the dataset linked at the top of the linked exercise before class.	Link
Tues, Oct 22, Rm 330	Defensive Programming Workflow Getting Help Online	Defensive Programming Workflow Management Getting Help	[Discuss mid-semester project in class]
Thurs, Oct 24, Rm 330	Groupby / Split-Apply-Combine	WM pp Chpt 10, 10.1, 10.2, 10.3	Link
Tues, Oct 29, Rm 270	Pandas: Reshaping Pandas: Categoricals	WM 8.3 Pandas reshaping (with pics!) What is goal of reshaping? Categoricals: WM 12.1	Link
Thurs, Oct 31, Rm 330	Speed and Performance in Python	Understanding Performance Improving Performance	[finish groupby and reshaping exercises]
Tues, Nov 05, Rm 270	Statistics with statsmodels	WM Chapter 13, 13.1, 13.2, 13.3 Skim “Linear Models” Skim “Discrete Dep Var Models” Patsy: Differences from R	Link
Thurs, Nov 07, Rm 330	Statsmodels Day 2
Tues, Nov 12, Rm 330	Machine Learning with sckikit-learn	JVP pp 331 - 359	Link
Thurs, Nov 14, Rm 330	Parallelism Defining Your Own Estimators	Parallel Computing Review linear regression as matrix manipulations. Here’s a nice review. Review how to define classes	Link
Tues, Nov 19, Rm 270	Data Science: Backwards Design Regular Expressions	Regular Expressions Tutorial
Thurs, Nov 21, Rm 330	Distributed Computing, Part 1	Parallel ML with Dask What is Dask? OPTIONAL: Full Dask Tutorial (3+ Hours)
Tues, Nov 26, Rm 270	Distributed Computing, Part 2
Thurs, Nov 28, Rm	THANKSGIVING BREAK
	Regular Expressions

Reminders:

• JVP: Python Data Science Handbook: Essential Tools for Working with Data by Jake VanderPlas.

• WM: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Second Edition by Wes McKinney.