Class Schedule¶

Office Hours and Links¶

Nick : Monday 12:30 - 1:30pm

Yi : Monday 11 am – 12 am; Tuesday 4 pm – 5 pm

Zoe : Wednesday 11 am – 12 am; Thursday 5 pm – 6 pm

Class Time¶

Class will be Tuesdays and Thursdays from 1:45pm-3pm (EDT). YOU MUST ATTEND THESE SYNCHRONOUS SESSIONS.

Date, Rm	Topic	Do Before Class	In-Class Exercise
Tues, Aug 24	Class Introduction	Setting Up Your Computer for Data Science	What is Data Science?
Thurs, Aug 26	Command Line Jupyter Lab / Notebooks	Read and sign syllabus Register with IPUMS Register for DataCamp Command Line Basics Advanced Command Line	Link 1 Link 2
Fri, Aug 27	SOFTWARE INSTALL DAY	A day of trouble shooting install issues
Tues, Aug 31	VS Code Packages Python v. R / variables as pointers	Why VS Code for Data Science? Setting Up VS Code with Python Augmented Python Console in VS Code Jupyter Tutorial Running R Notebooks Python packages variables v objects Python v. R	Link 1 Link 2
Thurs, Sep 2	Numpy Basics	Settings in VS Code JVP pp 33-77 IF did not complete DataCamp: Do Numpy Section Numbers in Computer OPTIONAL: How numpy works	Link
Tues, Sep 7	Pandas: Series	Pandas 1: Series	Link
Thurs, Sep 9	Pandas: DataFrames	Pandas 2: DataFrames	Link
Tues, Sep 14	Pandas: Indices & Missing	WM pp 136-142 JVP pp 115-139 Views and Copies in Pandas VS Code Livesharing (first half)	Link 1 Link 2
Thurs, Sep 16	Collaborating using Github	Git and Github Homework Guidelines	Link
Tues, Sep 21	Git and Github 2		Link
Thurs, Sep 23	Pandas: Cleaning Tracebacks	What are Tracebacks? WM Chapter 7 Python Strings (string section only!) Identifying Problems Editing Values	Link
Tues, Sep 28	Grammer of Graphics Intro to Plotting with Altair	Principles of Data Visualization Intro to Altair	Link
Thurs, Sep 30	Advanced Plotting	Advanced Altair <plotting_altair_part2.ipynb>`_	Link
Tues, Oct 5	FALL BREAK
Thurs, Oct 7	Pandas: Merging Pandas: Loading and saving data	JVP pp 149 - 157 WM Chapter 6	Link
Tues, Oct 12	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
Thurs, Oct 14	Defensive Programming Workflow Getting Help Online	Defensive Programming Iceberg Principle Workflow Management Getting Help	Discuss mid-semester project in class
Tues, Oct 19	Groupby / Split-Apply-Combine	WM pp Chpt 10, 10.1, 10.2, 10.3	Link
Thurs, Oct 21	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
Tues, Oct 26	Speed and Performance in Python	Understanding Performance Improving Performance	[finish groupby and reshaping exercises]
Thurs, Oct 28	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
Tues, Nov 2	Machine Learning with sckikit-learn	JVP pp 331 - 359	Link
Thurs, Nov 4	Defining Your Own Estimators	Review linear regression as matrix manipulations. Here’s a nice review. Review how to define classes	Link
Tues, Nov 9	Data Science: Backwards Design Regular Expressions	Backwards Design Regular Expressions Tutorial Opioid Project Rough Draft Due
Thurs, Nov 11	Parallelism Distributed Computing	Parallel Computing Distributed Computing with dask	Link 1 Link 2
Tues, Nov 16	Spatial Data (GIS)	What is GIS? Installing Geopandas Vector Data Raster Data GeoPandas User Guide: Data Structures, Reading and Writing Files, and Indexing and Selecting Data	Link 1
Thurs, Nov 18	Spatial Data (GIS)	OPIOID PROJECT DUE (Extensions available upon request to assigned date for PDS final) Projections and Coordinate Reference Systems Managing Projections in Geopandas Mapping with Geopandas Mapping Spatial Data in Geopandas
Tues, Nov 23	LAST CLASS Spatial Data (GIS)	Relating Data Spatially Advanced GIS Uses
Wed, Dec 8	(no class)	Last date things can be submitted

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.