CS 251: Data Analysis and Visualization

Course Description

This course covers the analysis and visualization of scientific data. Topics will include data management, basic statistical analysis, data mining techniques, and the fundamental concepts of machine learning. Students will also learn how to visualize data using 2-D and 3-D graphics, focusing on techniques that highlight patterns and relationships. Course projects will use data from active research projects at Colby.

Course Goals

1. Students understand and can write programs to store and manipulate data and measurements.
2. Students understand and can implement the fundamental concepts of interactive visualization of data.
3. Students understand and can implement common data transformations and statistical analysis.
4. Students understand and can make appropriate use of current machine learning techniques for prediction and knowledge discovery.
5. Students present methods, algorithms, results, and designs in an organized and competently written manner.
6. Students gain experience working with real data from disciplines outside computer science.

Useful Links

• Maxwell's 2017 Lecture Notes
• Maxwell's 2017 Daily Outlines
• Taylors's 2017 Lecture Notes
• Writing in CS 251
• Python website
• Safari Online College Resources
• Weka machine learning software

Data Links

• UCI Machine Learning Repository
• US Government Open Data
• Bureau of Labor Statistics
• Center for Medicare & Medicaid Services Data Navigator
• US Census Data
• NOAA Climate Data Online
• NOAA General Data Access
• Biogeoinformatics of Hexacorals
• NIH Data Sharing Repositories
• Health Services Research Information Central
• KD Nuggests Data Sets for Data Mining and Data Science
• Google Public Data
• European Union Open Data Portal
• UK Data Portal
• Google Books Ngram Viewer
• 20 Big Data Repositories
• Publicly Available Big Data Sets