Teaching

 
 

Summary

This portfolio item demonstrates my ability to communicate through some teaching experience. I've been a lecturer for an R workshop series, and a teaching assistant for a statistics course and a Bioinformatics course.

R workshop lecturer

In an R workshop series hosted by Ecoscope, I was honoured to lecture with consultants from UBC's SCARL (now ASDa, Applied Statistics and Data Science Group). Students paid to attend. We hosted workshops on the following content.

Introduction to R

  1. R Studio and R environment

  2. Data types, importing data

  3. Base graphics*

  4. Data subsetting and indexing

  5. Data manipulation and reshaping

  6. Regular expressions and string manipulation

Individual purchase

  1. ggplot2 X 2

  2. Experimental design

Special topics

  1. Statistical Models in R

  2. Objects: Class and Attributes

  3. Writing Functions in R*

  4. Reproducible research with R

  5. Creating presentations and report writing

  6. Collaborative research with Git

*Lectured by me

Workshops emphasised hands-on learning. Students worked with us on their laptops and solved exercises with our help. I was available to help students with exercises in every workshop, including the ones I lectured.

We often answered students' questions about their research after the workshops. My background as a bioinformatician was often useful at this point, because many research problems' data required bioinformatic processing prior to statistical analysis.

Statistics teaching assistant

I was a teaching assistant for a statistics course on methods for high-dimensional biological data. My responsibilities included marking and teaching a tutorial. In the tutorial, I taught students how to computationally apply their statistical methods. We covered the following topics.

  1. Probability

  2. Exploratory data anaylsis

  3. Analysis of variance

  4. Univariate linear regression

  5. Univariate linear regression, large surveys

  6. RNA-Seq (generalized linear models, large surveys)

  7. Basic clustering and classification

  8. Model selection

  9. Bootstrapping

Bioinformatics teaching assistant

I was a teaching assistant for a bioinformatics course. My responsibilities included marking and teaching a tutorial. In the tutorial, I taught students how to computationally apply their bioinformatics methods. In the tutorial, I covered the following topics.

  1. SSH (server access via the terminal)

  2. Basic unix

  3. Genetic sequencing inspection (FastQC)

  4. Short genetic read alignment (BWA & SAMtools)

  5. Integrative Genomics Viewer (IGV)

  6. Phylogentic tree estimation

  7. Single nucleotide polymorphism detection (read pile-ups, and BCFtools)

  8. Short read assembly (Velvet)

  9. Metagenomic analysis (MEGAN)