Title image Spring 2017

Course Information for Spring 2017

Lecture Times:MWF 8-9am (A), MWF 9-10am (B)
Lab Times:M 1-2:20 (A), T 1:00-2:20 (B), T 2:30-3:50 (C)
Lecture A Location:Davis 117
Lecture B Location:Lovejoy 215
Lab A, C Location:Davis 102
Lab B Location:Davis 122

Instructor Information

Bruce A. Maxwell
Office: Davis 112

Office hours: Knock
M 10pm-late, R 7:30pm-late
Any time my door is open

Stephanie Taylor
Office: Davis 114

M 1-3pm
T 1:30-3:30pm
R 9-11am until Feb 28, 2:30-4:30pm after March 1

Zadia Codabux
Office: Davis 111

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

Data Links