I recently met an incredible student from Butte High School, Ryan Tomich, who is interested in making small towns even better through the use of technology. I was fortunate to be a guest on his podcast dedicated to this subject. The questions led me to essentially give a brief summary of my career and also highlighted my recent switch to embracing computational biology in my research and teaching.
Computational Biology? Few of the resources that I describe here specifically address computational biology. My approach has been to first know biology and then learn computer programming while always having the question, “How can I apply this to my research?” in the back of my mind.
False Starts: After multiple attempts to learn a programming language throughout my life, the COVID-19 pandemic lockdown had the upside of letting me (finally) make progress using toward bringing computational biology into my research and teaching activities. So, roughly two years into my coding journey, I am emerging from the world of tutorials to the world of applying my knowledge to real-world projects and scientific problems.
What have the past two years looked like? Hours upon hours of both (on sale) Udemy courses and free tutorials. Learning any new topic can be a roller coaster nicely summarized by the Dunning-Kruger Effect.
Paid Courses: Udemy is my go-to source when I’m learning a new topic. Using the Dunning-Kruger Effect as a framework for thinking about my learning approach, I feel like the Udemy courses layout a roadmap for how to study the topic at hand. This way, I can look through the Udemy curriculum and skip past both the Peak of “Mount Stupid” and the Valley of Despair. That is, by seeing the road ahead, I accept that I am at the low end of the Slope of Enlightenment. Most of the courses I have purchased consist of at least 20 hours of curriculum and, because of this, they contain roughly a 3-credit college course for $12-15 when on sale. My course purchases went from a “vanilla” Python-specific curriculum to branching out into scientific Python, data science, machine learning, application development, computer vision, Git, and data engineering.
What’s next? I consider this journey as a lifelong learning experiment. My decades as a biologist and my newfound passion for computer programming will undoubtedly be a fruitful combination. While my first two years of this combination have gone largely undocumented, I am reminded of the saying, “The difference between science and screwing around is that, in science, you write it down.”
Here’s to becoming more scientific about my personal computational biology learning experiment. -Joel