Title image Spring 2018

Course Information for Spring 2018

Lecture Times:(A) MWF 9am, (B) MWF 10am
Lab Times:(A) T 1-2:20, (B) T 2:30-3:50,
(C) W 1:00-2:20, (D) W 2:30-3:50
Lecture A Location:Lovejoy 215
Lecture B Location:Lovejoy 213
Lab A, C, D Location:Davis 122
Lab B Location:Davis 102

Instructor Information

Stephanie Taylor
Office: Davis 114
Office Hours: M 1-2:30pm, T 3-4pm,
W 11am-noon, R 1:30-2:30pm, F 1-2pm

Caitrin Eaton
Office: Davis 116
Office Hours: M 1-2pm, R 10-11am,
F 1-4pm (Friday in Davis 122)

Bruce A. Maxwell
Office: Davis 112
Office hours: Knock
M 10pm-late, R 7:30pm-late
Any time my door is open, good times will be before 11am M-F

Zadia Codabux
Office: Davis 111
Office Hours: MW 1-2pm, T 9am-noon, or by appointment

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