Current Academic Coursework at OHSU and PSU
I teach in the summer hybrid BMI569/669 Data Analytics course at OHSU (Hybrid course co-taught with Kaiser Permanente Data & Information Management Enhancement (DIME) group).
I am also a co-instructor for BMI551/651 Bioinformatics and Computational Biology II: Statistical Methods, providing drop-in sessions for R programming and general tutoring.
We are teaching an introductory bootcamp in python called NEU640 Python Bootcamp for Neuroscientists.. This is a week-long course meant to introduce neuroscience students to the basics of python with data types familiar to them, including imaging and electrophysiology data. Taught with Stephen David, Brad Buran, Daniela Sadieri, Lucille Moore, Charlie Heller, Zack Schwartz. Winter Quarter, 2018.
I am course co-director for HSMP/PHE410 Introduction to Health Informatics. at Portland State University. Spring Quarter, 2018.
I have a free online introductory course at DataCamp called the RBootcamp. In this course, we cover the basics of visualization, data munging, and basic statistics using the tidyverse in R. It is freely available to everyone, but registration is required.
I am currently developing a course called Statistical Analysis of Networks in R for DataCamp. More info as it develops.
I have developed a number of self-directed training materials for learning R and specific packages that I’ve found useful. Most of these are available from my github page and may be used freely by students.
- R-Bootcamp (tidyverse) - The new version of RBootcamp, that covers the
tidyversefor manipulating data in R. You will need to register on DataCamp. This interactive course was written with both Jessica Minnier and Chester Ismay.
- R-Bootcamp (Base R) - an MOOC (Massively Open Online Course) for teaching the basics of data manipulation in R (Coursesites registration required). Note that I do not support this course anymore at this point.
Many of these tutorials have been given through the student club at OHSU that I mentor, called BioData Club. They are all freely available to be reused by other instructors.
- The Magic of Markdown - An introduction to the many uses of Markdown, including RMarkdown for reproducible scripting.
- GitHub Pages - A pain free introduction to getting a personal website started using GitHub pages. With Robin Champieux and Eric Leung.
As a developer and programmer of R, I enjoy teaching others about the basics of data wrangling and analysis. Here are a few of the tutorials I’ve put together.
- A gRadual intRoduction to the tidyverse. A workshop that Chester Ismay and I gave for Cascadia-R teaching the basics of the
- Introduction to the Tidyverse - An introduction to basic data wrangling using the
tidyversein R. With Eric Leung.
- ggplot2 Tutorial - A gentle introduction to
ggplot2and the grammar of graphics.
- ggvis Tutorial - A gentle introduction to
ggvis, which adds extra interactivity to
shinyplots using the magic of vega.
- Shiny Tutorial - A do it yourself tutorial to try out
dplyrfor interactive graphics.
- igraph Tutorial - Another tutorial, this time on network analysis using the
- Exacloud Tutorial - A DIY tutorial to running jobs on Exacloud, OHSU’s cluster computing environment. With Ryan Swan.
Big Data to Knowledge
As an instructor under OHSU’s Big Data To Knowledge (BD2K) Training Grant, I have also developed the following short workshops to encourage students to explore data and learn the basics of data-wrangling.
- Cardiovascular Risk Workshop - A workshop given for Portland State University students exploring the difficulties of predicting cardiovascular risk using
shinyfor exploratory data analysis and
caretfor machine learning. Part 1 and Part 2.
- Exploratory Data Analysis Using Shiny - A Tutorial and slides about doing exploratory data analysis with a Shiny Dashboard
- Machine Learning Using RMarkdown - A tutorial about Machine Learning and Reproducible Scripting and Workflows.
Other Teaching Interests
- BioData Club - A student and postdoc driven discussion group at OHSU that focuses on practical skills (documentation, software engineering and visualization) necessary for success in data science and bioinformatics.
- I participate in the PDX R User Group and I was one of the organizers of the first Cascadia R conference
- I have also started to contribute lesson material to Software Carpentry.