This course teaches how to critically evaluate quantitative data. Designed for bioscience graduate students, it covers statistical principles and basic programming skills to accelerate and empower data analysis. The course introduces common statistical practices and concepts in the life sciences, such as summary statistics, probability and distributions, hypothesis testing, and confidence intervals. In parallel, students learn basic Python programming skills for loading, processing, plotting, and analyzing datasets. The course is appropriate for students with no prior statistics or programming experience, up to those with intermediate skills in either or both. By combining hands-on computation with statistical concepts, the course aims to develop students' intuition for core concepts in statistical hypothesis testing, provide students a flexible set of tools for analyzing their own data, and sharpen their abilities to critically evaluate different statistical approaches.
Section 01Fundamentals of Biostatistics for Graduate Students
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