Class Schedule

Office Hours and Links

Nick : Monday 12:45 pm - 1:45 pm

Raza : Tuesday 12:00 pm - 1:00 pm; Thursday 4:00 - 5:00 pm.

Marlyne : Monday 10:00 am - 11:00 am; Wednesday 10:00 - 11:00 am.

Class Time

Class will be Tuesdays and Thursdays from 1:45pm-3pm (EDT).

Date, Rm	Topic	Do Before Class	In-Class Exercise
Tues, Aug 30	Class Introduction Welcome to VS Code	SETUP 1: Installing Python and miniconda SETUP 2: Installing and Configuring VS Code
Thurs, Sep 1	Command Line Basics	SETUP 3: Setup Augmented Command Line Command Line Basics Read and sign syllabus Register with IPUMS Register for DataCamp	Ex
Fri, Sep 2	SOFTWARE INSTALL DAY	A day of trouble shooting install issues
Tues, Sep 6	Advanced Command Line Jupyter Packages	Advanced Command Line SETUP 4: Jupyter in VS Code Python packages OPTIONAL SETUP: R Notebooks	Ex 1 Ex 2
Thurs, Sep 8	Python Debugger R / Python Differences Packages	What are Tracebacks? Debugging Tools Python Debugger in VS Code variables v objects Python v. R	Ex 1 Ex 2
Tues, Sep 13	Numpy Basics	Why numpy? Numpy Vectors Math with Vectors Type Promotion Subsetting Vectors Modifying Subsets of Vectors Numbers in Computer Wanna leave feedback?	Ex 1
Thurs, Sep 15	Numpy Arrays	Views and Copies Matrices Reshaping Matrices Images as Matrices Subsetting Matrices ND-Arrays Broadcasting in numpy Wanna leave some feedback?	Link
Tues, Sep 20	Pandas: Series	Why Pandas? Pandas Series Manipulating Pandas Series Vectorizing	Link
Thurs, Sep 22	Pandas: DataFrames	Pandas 2: DataFrames Managing Settings in VS Code Writing Good Notebooks	Link
Tues, Sep 27	Pandas: Indices & Missing	WM pp 136-142 (Indices, Section 5.2 up to MultiIndexes) JVP pp 119-127 (“Handling Missing Data” in Chpt 3) Views and Copies in Numpy Views and Copies in Pandas	Link 1 Link 2
Thurs, Sep 29	Collaborating using Github	Git and Github Visualize Git Branches	Link
Tues, Oct 4	Git and Github 2	Oopsie Resource: Oh 💩, Git! The secret of git	Link
Thurs, Oct 6	Pandas: Cleaning Tracebacks	Identifying Problems Editing Values Globally Editing Specific Entries Cleaning Data Types Missing Data Python Strings (string section only!)	Link
Tues, Oct 11 FALL BREAK
Thurs, Oct 13
Tues, Oct 18	Grammer of Graphics Intro to Plotting with Altair	Principles of Data Visualization Intro to Altair Altair in Context Plotting with Pandas	Link
Thurs, Oct 20	Advanced Plotting	Advanced Altair	Link Link
Tues, Oct 25	Pandas: Merging Pandas: Loading and saving data	JVP pp 149 - 157 WM Chapter 6	Link
Thurs, Oct 27	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. Parquet Format	Link
Tues, Nov 1	Defensive Programming Workflow Backwards Design Getting Help Online	Workflow Management Backwards Design Getting Help	Discuss mid-semester project in class
Thurs, Nov 3	Defensive Programming Groupby / Split-Apply-Combine	Defensive Programming Iceberg Principle WM pp Chpt 10, 10.1, 10.2, 10.3	Link
Tues, Nov 8	Pandas: Reshaping Pandas: Categoricals	WM 8.3 Pandas reshaping (with pics!) What is goal of reshaping? Categoricals: WM 12.1 Project Strategy Plan Due	Link
Thurs, Nov 10	Speed and Performance in Python	Understanding Performance Improving Performance Code Reviews	[finish groupby and reshaping exercises]
Tues, Nov 15	Statistics with statsmodels	WM, 3rd Edition, Chapter 12, 12.1, 12.2, 12.3 (Patsy and statsmodels, not scikit-learn) Skim “Linear Models” Skim “Discrete Dep Var Models” Regression Diagnostics Regression Plots Patsy: Differences from R	Link
Thurs, Nov 17	Machine Learning with sckikit-learn	JVP Chapter 5 up to “Hyperparameters and Model Validation” Section (pp 331 - 359)	Link
Tues, Nov 22	No Class
Thurs, Nov 24	THANKSGIVING
Tues, Nov 29	Defining Your Own Estimators Regex	Review linear regression as matrix manipulations. Here’s a nice review. Review how to define classes Broadcasting in numpy Opioid Project Rough Draft Due	Link
Thurs, Dec 1	Parallelism Distributed Computing	Parallel Computing Distributed Computing with dask (Note reading includes a 45 minute video to watch)	Link 1 Link 2
Fri, Dec 9th		Final Project Report and Presentation Due

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 Jupyter, Third Edition by Wes McKinney.