PDS II (IDS 541)

Practical Data Science II is a flipped-classroom, exercise and project-focused course. Building on the computational thinking skills developed in Practical Data Science I, this course introduces students to a range of computational inquiry methods, including network analysis, geospatial analysis, advanced plotting, and natural language processing (NLP). Throughout, the focus will be on developing hands-on experience implementing these methods with messy real-world data to ensure students are prepared to deploy these tools to answer the questions they care about. Requirements: Practical Data Science I.

Syllabus

A preliminary syllabus can be found here.

Office Hours

Adriane: Signup with this link

Nick (Zoom or in Gross 231): Thursday 9 am - 10:00 am. Please email me in advance if you plan to attend. I’ll usually be there regardless, but knowing people are coming is useful if conflicts arise.

Diego (Zoom or Gross Hall 2nd Floor, 230K): Monday 12:00 - 1:00 pm

Meron (Zoom or Gross Hall 2nd Floor, 230N): Wednesday 12:00 - 1:00 pm.

Books

• Python for Geographic Data Analysis (PGDA)

Class Schedule (Preliminary)

Date	Topic	Do Before Class	In-Class Exercise
Mon Jan 12	Pandas: Reshaping	Tidy Data Format: the goal of reshaping Reshaping Pandas reshaping (with pics!)	Link
Wed Jan 14	Speed and Performance in Python Pandas: Categoricals	Understanding Performance Solving Performance Problems
Mon Jan 19	MLK DAY NO CLASS
Wed Jan 21	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
Mon Jan 26	SNOW
Wed Jan 28	Parallelism Distributed Computing	conda Environments Parallel Computing Distributed Computing with dask (Note reading includes a 45 minute video to watch)	Dask
Mon Feb 2	SNOW AGAIN
Wed Feb 4		More Dask	Dask ARCOS
Mon Feb 9	-GIS: Vector Data	What is GIS? Installing Geopandas Intro to Geopandas	Census
Wed Feb 11	-GIS: Vector Data	Mapping	Mapping Colormaps
Mon Feb 16	-GIS: Vector Data	Projections Projections and Geopandas GIS Data Formats
Wed Feb 18	-GIS: Vector Data	Spatial Joins Set Operations (Yes, this is just the docs. But I wrote a lot of them so it’s not cheating!)
Mon Feb 23	GIS: Rasters	PGDA Chpt 7 through 7.2 (x-array)
Wed Feb 25	GIS: Rasters	PGDA 7.3 through “Creating a raster mosaic by merging datasets” (stop before Raster to vector conversion) PGDA 7.5 stop before Zonal Statistics with Vector Zones
Mon Mar 2	GIS: Rasters	PGDA 7.4 PGDA 7.3 Resampling Section
Wed Mar 4	GIS: Mixing Vector and Raster	PGDA 7.3 “Raster to vector conversion” and “Vector to raster conversion” PGDA 7.5 Zonal Statistics with Vector Zones
Mon Mar 9	NO CLASS	SPRING BREAK
Wed Mar 11	NO CLASS	SPRING BREAK
Mon Mar 16	Machine Learning	Géron, Chpt 1: The Machine Learning Landscape (stop before “Batch and Online Learning,” then read the “Testing and Validating” section) Géron, Chpt 2: End-to-End Machine Learning Project
Wed Mar 18	Machine Learning	Inference and Prediction Supervised ML Workflow
Mon Mar 23	Machine Learning	Inference and Prediction Supervised ML Workflow
Wed Mar 25	Machine Learning	Supervised ML
Mon Mar 30	Machine Learning	SciKit-Learn	Ex 1
Wed Apr 1	Git and github	Git and Github Create an ssh key and register with ssh-agent (you don’t need to create a passphrase) If you don’t have a github account, make one. Register a passkey or at least two factor authentication. Add your ssh key to your github account Visualize Git Branches	Ex 1
Mon Apr 6	Git and github	Defensive Programming: from first reading through Iceberg Principle Workflow Management Getting Help Code Reviews
Wed Apr 8	Network Data	Intro to graph-tool
Mon Apr 13	Network Data	Community Detection Shortest Path
Wed Apr 15	LAST CLASS