Course Information for Spring 2018

Instructor Information
Stephanie Taylor
Caitrin Eaton
Bruce A. Maxwell
Zadia Codabux 
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 2D and 3D graphics, focusing on techniques that highlight patterns and relationships. Course projects will use data from active research projects at Colby.
Course Goals
 Students understand and can write programs to store and manipulate data and measurements.
 Students understand and can implement the fundamental concepts of interactive visualization of data.
 Students understand and can implement common data transformations and statistical analysis.
 Students understand and can make appropriate use of current machine learning techniques for prediction and knowledge discovery.
 Students present methods, algorithms, results, and designs in an organized and competently written manner.
 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