Teaching

A major part of my professional identity is being an educator. I have a fair bit of experience teaching in a variety of settings (and love to work with students!).

This page is a record of all the classes I have taught, materials I have produced, and how to get in touch if you’d like me to deliver a workshop.

Professional Teaching

I’ve taught several classes at the undergraduate level as both instructor of record and as a teaching assistant. I also spent a year as Lead Instructor of Data Science at London Flatiron School’s now defunct campus. For more details, please see my CV.

Recent classes I have taught or designed include:

School Area Class Role
Humboldt University of Berlin Music Issues with Music and Sciences Lead
Flatiron School Data Science 16 Week Data Science Bootcamp Lead
Louisiana State University Psychology Multivariate Statistics GTA
Intermediate Statistics GTA
Music Aural Skills III, IV Lead
Music Theory III, IV GTA

Workshop Teaching

I have also developed and delivered several workshops. Some were given as my time as Lead Instructor at Flatiron, some are from a weekend coursed I developed for smaller companies like Minerva in London, and some are academic workshops.

I’m also an RStudio Certified tidyverse instructor!

Here are some examples of from my teaching:

Materials Created

Lecturing

The Science of Netflix

R, RStudio, and Tidyverse for Pythonistas

Clustering Algorithms Crash Course

If you’d like to hire me for a workshop or develop specific materials for you on a topic, please send me an email. I have experience delivering work online and in person.

One on One

I also have given one on one instruction to individuals who want to learn more about a topic they’re interested in. Typically this consists of first defining what the individual would like to get out of the lessons, how we’d know if we achieved this, then an action plan of how to work towards that.

For example, goals such as “I’d like to be a good R programmer” or “I want to be better at stats” don’t have any clear way of knowing when that state is achieved. Instead, we might focus on something like “I’d like to feel comfortable to teach ANOVA to undergraduates.” From here, we’d outline some of the action steps we’d take to get to this point…

  1. Get comfortable enough with R to work with common packages used in data manipulation.
  2. Simulate a simple ANOVA to change various parameters to see how the output is affected.
  3. Try to write an ANOVA function from scratch.
  4. Analyze one dataset with both the ANOVA framework and as a general linear model to compare and contrast the output.

Once you feel comfortable with those following clearly defined steps, we can then revisit the initial motivation to see if we have covered the correct material to understand ANOVA.

These usually consist of one hour meetings via Zoom. After the first session, I will give you a list of things to try before meeting again. We will then use our time together to talk about the assignment.