Course Description

Course introduces undergraduate and graduate students to biomedical datasets, focusing on practical data parsing, visualization, and statistical analysis. Emphasis on developing coding skills in R and using omics, drug, and clinical data for analysis.

Additional Requirements for Graduate Students:
Graduate students are expected to present projects based on thesis-relevant datasets.

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Athena Title

Biomedical Data Literacy

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Prerequisite

[(STAT 2000 or STAT 2000E) and (BIOL 1107 or BIOL 1107E or BIOL 2107H)] or permission of department

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Semester Course Offered

Offered spring

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Grading System

A - F (Traditional)

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Student Learning Outcomes

• Students will increase awareness of biomedical datasets and associated software tools.
• Students will develop skills in data parsing, visualization, and statistical analysis.
• Students will apply statistical and coding skills to real-world biomedical data.
• Students will understand and apply standard operating procedures within biomedical data research.
• Students will understand categories of AI methods and capabilities as research tools.

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Topical Outline

• Introduction to Biomedical Datasets
• Basic Skills in Omics, drug and clinical data analysis
• Data Parsing, coding and visualization in R
• Statistical methods applied to biomedical data
• Emerging technologies in biomedical data science

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