Genentech Data Carpentry Workshop

Teaching at a company

So I had a really neat opportunity to teach a Data Carpentry workshop with Tracy Teal at Genentech on September 21-22.

tl;dr? Teaching at a company provides a unique teaching experience. You have an exceptionally motivated audience with specific questions but the level of teaching quality/expertise expected is much higher.

Course description and schedule:


Data Carpentry has provided a high level of training to researchers across the globe in a variety of venues, universities, etc. However, this was the first workshop for a company. Again, the short story is that it was a fantastic experience. Personally, the Software Carpentry and Data Carpentry workshops I’ve had the pleasure of being a part of/teaching have all been in university settings, so this was going to be an adventure. I’m at the point in my degree where I need to start considering what comes next post-Ph.D. and biotech/industry are definitely on my radar. Thus, this was a unique opportunity to teach, interact, and network with researchers at Genentech.

The two people who organized the workshop were Thomas and Melanie. They were excellent at communicating, willingly prepared times for me to meet with folks within the Bioinformatics department so that I could get a feel for what life was like at Genentech, and were generally awesome people. My initial impressions of the culture of Genentech was that of ridiculous happiness. Everyone we interacted with were smiling, enjoyed their job, and had an outstanding respect for one another. The campus was beautiful and enormous with crosswalks shaped like DNA.

The workshop was taught using the genomics Data Carpentry lessons. Day 1 consisted of teaching data organization and cleaning in Excel and OpenRefine (Google Refine) in the morning and the introduction to R in the afternoon. Tracy is an outstanding teacher and communicator making her a tough act to follow to introduce R. I had recently finished reading Vince Buffalo’s book “Bioinformatics Data Skills” where I think he makes an excellent case for how powerful R is for exploratory data analysis (EDA). Thus, we plugged the book and I incorporated some of his thoughts with my own to convince attendees that what we were about to learn was going to be useful, but maybe not immediately obvious until the end of the workshop. However, I quickly learned that R is Genentech’s favorite programming language as one of the creators of the language and several core developers worked or currently work in the bioinformatics department. Thus, many of the attending researchers had already had some exposure to R, some positive and some not. Further, I think this was about the point where I realized that with experience comes the ability to quickly come up with useful metaphors to describe/teach concepts and then read the room as to whether it worked or not. Further, when live coding to a room full of people who have minimal coding experience, take the time to write a comment above the line you are going to execute as it allows the audience a moment to process what is happening and be able to look back during a challenge question and remember what it did. This provides a scaffold to piece together code to solve the challenge questions and frankly this is when I saw a lot of “lightbulb” moments of understanding. When I simply copy and pasted lines of code without the comments, this is when a lot of confusion occurred. Mostly because another drawback during live coding is that your window is large, so only a few commands can fit onto the screen, underscoring the need for commenting lines. Retrospectively this seems obvious, but previous workshops have been in smaller rooms, so the window could be smaller and perhaps allowing more room for commands, clarifying sequential lines/concepts. Ultimately, based on the feedback (minute cards) day 1 was a success.

Day 2 began with data wrangling and plotting in R. Again, it was awesome to see how Tracy teaches. During the plotting section, you could hear people muttering “Whoa, that’s awesome!” and excited gasps, followed by some people attempting to simultaneously load their own data into R to do some basic plotting. Here is where I think teaching at a company is beneficial for both the audience and instructors alike. Every attendee has a specific set of questions and tremendous motivation to learn these skills because they have a data set that immediately requires the concepts/skills taught in a Data Carpentry workshop. As a result, perhaps we should have added a small fee for all the consultation sessions we had. ;) It was a blast and I loved every minute. All of this to say that this probably will not hold true at all companies or even in a university setting, but it was a first for me at a workshop. During the afternoon I had a chance to talk about SQL databases. At this point in the workshop, it was good to finish with this lesson as the syntax is rather straight forward and meshes really well with the dplyr syntax shown in the morning. Finally, at the end of the day, I attempted to provide a capstone example of doing RNAseq differential gene expression in R using commands that they had encountered throughout the workshop. I think for some it was neat to see the utility of R for next generation sequencing (NGS) data analysis and they now had 80% of the coding capacity and know-how to do it themselves.

As a less experienced instructor, working with Tracy allowed me to see some teaching best practices, useful metaphors, and that I need more practice. I keep mentioning these metaphors, but the ability to communicate a concept effectively using a metaphor is a great teaching tool. Thus, outside of simply developing a set of metaphors, it would be useful for instructors to have a list of possible metaphors for key lesson concepts. Finally, pairing a more senior instructor like Tracy, with a more junior instructor like me works well to quickly identify what works for effectively communicating concepts covered in Data Carpentry.

Teaching at a company was a great way to showcase what I know and interact/network with people in an industrial/biotech environment to expand post-graduate opportunities. My concluding thoughts are that it was a spectacular experience and I learned a lot about myself as an instructor. I would jump at the chance to teach at a company again.