As my students know, teaching is one of my passions and I truly enjoy it. I’ve had the opportunity to teach workshops and short courses around the world. The following are Open Educational Resources I have developed for classroom teaching and workshops.

CUNY Graduate School of Public Health and Health Policy

Introductory Biostatistics

  • PUBH614: Quantitative and Qualitative Data Analysis Methods in Public Health Research
    I re-implemented labs using webR and Google Colab, and added Codebooks.
    Homepage: https://cuny-epibios.github.io/PUBH614/

Applied Biostatistics II

  • BIOS 621/821: Applied Biostatistics II
    Lecture and lab materials and recordings.
    Homepage: https://bios2.waldronlab.io

University of Trento (Italy)

Applied Statistics for High-Throughput Biology

I developed this course for my Fulbright scholarship to Italy in 2016, and have updated and offered it nearly annually, at the University of Trento and University of Verona. The course syllabus and lecture materials are available at www.github.com/waldronlab/AppStatBio.

Bioconductor workshops

  1. CNVWorkshop
    Workshop for CNV analysis with Bioconductor.
    Homepage: https://waldronlab.io/CNVWorkshop

  2. PublicDataResources
    Workshop on public data resources and Bioconductor packages for accessing large-scale data (GEO, SRA, GDC, etc.).
    Homepage: https://waldronlab.io/PublicDataResources/

  3. AnVILWorkshop
    AnVIL/Terra workshop for Bioconductor conference.
    Homepage: http://waldronlab.io/AnVILWorkshop

  4. MultiAssayWorkshop
    Multi-omic integration and analysis of cBioPortal and TCGA data with MultiAssayExperiment.
    Homepage: https://waldronlab.io/MultiAssayWorkshop/

  5. curatedTCGAWorkshop
    Workshop material for the BiocNYC R/Bioconductor (archived).

  6. MicrobiomeWorkshop
    A workshop on microbiome analysis using Bioconductor (archived).

  7. MultiAssayExperimentWorkshop
    Workshop on multi-omics data representation and analysis with MultiAssayExperiment (archived).

R and Data Science Resources

Here is a list of recommended textbooks for learning R and getting into Data Science.