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.

Class Schedule (Preliminary)

Date	Topic	Do Before Class	In-Class Exercise
Th Jan 9	Pandas: Reshaping Pandas: Categoricals	Wes McKinney’s Python for Data Analysis, Third Edition, Chapter 8, Section 3. Available online through duke library free Tidy Data Format: the goal of reshaping Pandas reshaping (with pics!)	Link
Tu Jan 14	Speed and Performance in Python	Understanding Performance Solving Performance Problems
Th Jan 16	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
Tu Jan 21	Parallelism Distributed Computing	conda Environments Parallel Computing Distributed Computing with dask (Note reading includes a 45 minute video to watch)	Dask More dask (optional)
Th Jan 23	GIS	What is GIS? Installing Geopandas Intro to Geopandas
Tu Jan 28	GIS	Mapping
Th Jan 30	GIS	Spatial Joins
Tu Feb 4	GIS	Projections Projections and Geopandas GIS Data Formats
Th Feb 6	GIS	Raster Data Intro to Rasterio
Tu Feb 11	GIS	Remote Sensing and Satellite Data
Th Feb 13	Machine Learning	Unsupervised versus Supervised Machine Learning Clustering
Tu Feb 18	Machine Learning	Prediction versus Inference Supervised ML Workflow
Th Feb 20	Machine Learning	Supervised ML
Tu Feb 25	Network Data	Intro to graph-tool
Th Feb 27	Network Data	Community Detection
Tu Mar 4	Network Data	Shortest Path
Th Mar 6	Advanced Modelling	SciKit-Learn
Tu Mar 11	Advanced Modelling	pyGam
Th Mar 13	Larger Programs	Top Down Design
Tu Mar 18	NO CLASS
Th Mar 20	NO CLASS
Tu Mar 25	Larger Programs	Monte Carlo and Poker Project
Th Mar 27	Larger Programs	Poker Test Cases
Tu Apr 1	NLP	Overview of LLMs
Th Apr 3	NLP	LLM fine tuning with Hugging Face
Tu Apr 8	NLP	LLM fine tuning with Hugging Face
	LAST CLASS