Course Descriptions

Prerequisites

General Biology 01:119:115/116/117 and Basic Stats for Research (01:960:401)

Course Syllabus

Fall 2025 Syllabus  

Course Description and Learning Goals

This course is an introduction to the key statistical methods used in drug discovery and development. Students will explore essential test statistics, including t-tests, ANOVA, chi-square tests, and regression analysis, to assess the efficacy of compounds and candidate drug treatments in each phase of the drug discovery process. Example topics will include hypothesis testing, p-values, confidence intervals, and statistical significance in clinical trials. Through data gleaned from published studies and simulated data, students will gain practical skills in evaluating statistical results to support evidence-based decision-making in drug research. By the end of the course, students will be prepared to evaluate experimental data in the pharmaceutical and biomedical industries.

Course Learning Goals

By fully participating in this course, students will be able to:

1. Demonstrate fundamental laboratory techniques relevant to computational analysis of data generated from the different stages of drug discovery.
2. Apply scientific principles to generate experimental data.
3. Analyze experimental data to draw meaningful conclusions; specifically, conclusions from results for statistical hypothesis testing.
4. Communicate scientific findings clearly through written lab reports.
5. Collaborate with peers to troubleshoot challenges and improve experimental outcomes.

Course Satisfies the Following Genetics Dept. Learning Goals:

By fully participating in this course, students will be able to:

1. Apply key genetic concepts to experimental design and data analysis.
2. Integrate knowledge from coursework and research to interpret experimental results.
3. Critically analyze genetic data and relate findings to published research.
4. Communicate scientific results effectively through written lab reports and presentations.

Exams, Assignments, and Grading Policies:

Your success in this computational lab course and your final grade depend on the following assessment components. Each set of components is packaged into a weekly module.

• Assignments (40%)
  • Weekly assignments consist of multiple-choice and open-ended questions. These assignments typically involve computational data analysis drawn from real and simulated experiments. The data are determined based on the stage of drug discovery and the experiment within the stage.
  • The two lowest assignment grades are dropped.
• Quizzes (25%)
  • Semi-weekly quizzes are based on material from the previous two weekly modules.
  • Formats are multiple-choice.
  • The two lowest quiz grades are dropped.
• Final Exam (15%)
  • The final is cumulative, covering all lab/module topics.
  • The format is multiple-choice.
• Discussion Forums (15%)
  • Students will read a news or research article and write a answer post to the professor’s question.
  • A response to at least two other students’ posts is required.
  • The format is text.
• Participation (5%)
  • Semi-weekly participation questions provide feedback on course materials.
  • The format is text.

Course Materials and Technology Requirements:

There is no required text. In each lab you will see Information from websites or slide presentations. 

There are no specific technology requirements other than the ability to access the course Canvas site. When needed, in each module, you will see links to online calculators.

Course Closed?  

There are no SPNS available for the course. Please continue to monitor the web-reg for openings. 

Faculty

Dr. Derek Gordon 
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

** All information is subject to change at the discretion of the course coordinator.