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 undergraduate- and graduate-level introduction to the inner-workings of methods in bioinformatics and computational biology: analytical techniques, algorithms, and statistical/machine-learning approaches developed to address key questions in biology and medicine. Students will get a variety of opportunities to engage in a number of in-class discussions, discuss-critique-present important papers, work on varied & interesting assignments, and collaborate as learning groups.
By working on a guided semester-long research project, students will also learn how to formulate problems for quantitative inquiry, design computational projects, think critically about data & methods, do reproducible research, and communicate findings.
[Spring 2020]
[Spring 2019]
[Spring 2018]
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.
Bioinformatics Workshops
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).