BIOL 5062

Analysis of Biological Data

Lec 01 selected

The course introduces students to techniques available for the analysis of biological data, including regression, general linear models and multivariate methods. Emphasis is on the practical use of these techniques rather than derivations. Students analyze real and realistic data sets, and are assessed on write-ups of these analyses.

Sections

Lecture

From the syllabus

From the Fall 2026 syllabus by Derek Tittensor and Hal Whitehead. Open the PDF

Grading

10%Report of analysis of data set 1aDue Oct 1
10%Report of analysis of data set 1bDue Oct 8
10%Report of analysis of data set 1cDue Oct 15
10%Report of analysis of data set 1dDue Oct 27
10%Report of analysis of data set 1eDue Nov 3
5%Data set 2 description and proposed analysisDue Oct 22
15%Presentation of Type 2 analysis (5)Nov 19, 24, 26; Dec 1, 3
30%Report of analysis of data set 2Due Dec 14

Course rules

Late work
Assignments lose 10% per weekday late without a legitimate explanation.
Submission
Submit assignments through Brightspace by 16:30 on the due date.
Missed work
Students may use the Student Declaration of Absence form up to three times in the course.
Collaboration
Do not work in groups on assignments.
Generative AI
Do not use generative AI to write assignments; AI may assist with coding, but final code must be yours.
Attendance
Students are expected to attend in-person lectures, which will not be recorded for later viewing.

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