PDS II (IDS 591)

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.

Class Schedule (Preliminary)

Date	Topic	Do Before Class	In-Class Exercise
Th Jan 9	Speed and Performance in Python	Understanding Performance Solving Performance Problems
Tu Jan 14	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
Th Jan 16	Parallelism Distributed Computing	conda Environments Parallel Computing Distributed Computing with dask (Note reading includes a 45 minute video to watch)	Dask More dask (optional)
Tu Jan 21	Defining Your Own Estimators	Writing to Stakeholders Review linear regression as matrix manipulations. Here’s a nice review. Review how to define classes Broadcasting Gotchyas	Link
Th Jan 23	GIS: Intro GIS: Geopandas and Vector Data	What is GIS? Installing Geopandas Intro to Geopandas
Tu Jan 28	GIS: Projections	Projections Projections and Geopandas GIS Data Formats
Th Jan 30	GIS: Vector Spatial Joins and Maps	Spatial Joins Mapping
Tu Feb 4	GIS: Raster Data	Raster Data Intro to Rasterio
Th Feb 6	GIS: Satellite Data	Remote Sensing and Satellite Data
Tu Feb 11	GIS: Travel Distances	Distance APIs
Th Feb 13	Network Data	What is Network Data?
Tu Feb 18	Network Data	Intro to graph-tool
Th Feb 20	Network Data	Community Detection
Tu Feb 25	Network Data	Shortest Path
Th Feb 27	Advanced Modelling	Matching
Tu Mar 4	Advanced Modelling	pyGAM
Th Mar 6	NLP	Stemming, stop words, etc.
Tu Mar 11	NO CLASS
Th Mar 13	NO CLASS
Tu Mar 18	NLP	Topic models, bag of words
Th Mar 20	NLP	Embeddings
Tu Mar 25
Th Mar 27	NLP	LLMs
Tu Apr 1	Project
Th Apr 3	Project
Tu Apr 8	LAST CLASS	ROUGH DRAFT OF OPIOID PROJECT DUE, extendable to Morning of Mon Nov 26 but feedback may be later
Th Apr 9		Final Project Report Due
Tu Apr 15
Wed, April 23
Wed, Apr 30