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	Working with Text
Tu Jan 28	-GIS: Vector Data	What is GIS? Installing Geopandas Intro to Geopandas
Th Jan 30	-GIS: Vector Data	Mapping
Tu Feb 4	-GIS: Vector Data	Spatial Joins
Th Feb 6	-GIS: Vector Data	Projections Projections and Geopandas GIS Data Formats
Tu Feb 11	GIS: Rasters	Raster Data Intro to Rasterio and X-Array
Th Feb 13	GIS: Rasters	Plotting raster data
Tu Feb 18	GIS: Rasters	Remote Sensing and Satellite Data Band Algebra
Th Feb 20	GIS: Rasters	Reprojection, resampling, and interpolation
Tu Feb 25	GIS: Mixing Vector and Raster	Zonal aggregation (summary statistics) Rasterization/Geohashing
Th Feb 27	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
Tu Mar 4	Machine Learning	Prediction versus Inference Supervised ML Workflow
Th Mar 6	Machine Learning	Supervised ML
Tu Mar 11	Machine Learning	SciKit-Learn
Th Mar 13	Solving Problems with Data	Solving Problems with Data
Tu Mar 18	Solving Problems with Data	Problem Taxonomy
Th Mar 20	NO CLASS
Tu Mar 25	NO CLASS
Th Mar 27	Solving Problems with Data
Tu Apr 1	Network Data	Intro to graph-tool
Th Apr 3	Network Data	Community Detection
Tu Apr 8	Network Data	Shortest Path
	LAST CLASS