Gaps, Missteps, and Errors in Statistical Data Analysis
This is a short (1-credit) intermediate-to-advanced course designed to:
- Discuss common misunderstandings & typical errors in the practice of statistical data analysis.
- Provide a mental toolkit for critical thinking and enquiry of analytical methods and results. Classes will involve lectures, discussions, hands-on exercises, and homework about concepts critical to the day-to-day use and consumption of quantitative/computational techniques.
Topics include: Underpowered statistics • Pseudoreplication • P-hacking & multiple hypothesis correction • Difference in significance & significant differences • Base rates & permutation tests • Regression to the mean • Descriptive statistics & spurious correlations • Estimation of error and uncertainty • (Others under consideration; Subject to small changes)
Bioinformatics and Computational Biology
This course is an introduction to contemporary topics in bioinformatics and computational biology, dealing with combining large-scale data and modern analytical techniques to gain biological/biomedical insights. In each topic, centered around a recent paper, we discuss the major biological & biomedical questions, explore the relevant molecular/genomics/biomedical datasets, and understand the underlying statistical, probabilistic, & machine-learning approaches.
Students will learn how to formulate problems for quantitative inquiry, design computational projects, understand and think critically about data & methods, communicate research findings, perform reproducible research, and practice open science. Students will apply all these by carrying out a group project, presenting their project in class, and submit a report at the end of the course.
Modular Courses in Bioinformatics
These courses cover similar material to the August workshops, but use the flipped-classroom philosophy of having students watch video lectures online and come to class to apply the tools to real data. Each module is worth 1 credit.
These bioinformatics workshops provide training in Linux/R/Python programming, Statistical data analysis and visualization, and Analysis of various types of genomic data (e.g. RNA-seq).