Start with why#

Why are you here, reading this? What do you hope to get out of a course in “neural data science”? These are questions for you to answer for yourself, and obviously I can’t hear your answer. But, I can tell you why I designed this course, and what I hope you will get out of it.

This course was borne from a long-standing recognition that, although researchers that I knew in various academic departments related to psychology and neuroscience valued and desired students with coding and data science skills in their labs, the undergraduate curricula didn’t include course offerings in this area. Programming courses are usually taught through computer science departments, or faculties, and these are most commonly oriented towards computer science students. The reasons that science students need to learn code are much more specific than what a computer science major needs to learn. As a result, science students sometimes find what they learn in computer science classes hard to relate to their discipline.

Put more succinctly, I realized there was a need for neuroscience and psychology students to learn how to use a programming language to work with data (and a 2021 paper in Nature Neuroscience agrees with me). And more fundamentally, I recognized that there was a need for students in these fields to develop greater “fluency” in working with data. In the same way that we develop fluency in language, we can develop a fluency in working with data to organize, summarize, and visualize it — and ultimately, derive meaning from it. This sentiment is captured in this great 3 min video by McGill Neuroscience grad student Emily Irvine as well. This course aims to address these needs.

Another factor that drove the development of this course was my recognition that the majority of people who pursue undergraduate coursework in neuroscience and psychology, don’t end up working as scientists in those fields — even the ones who get PhDs! Indeed, in the US as many PhDs are working in industry as in academia (Science, 2019), and that’s across all age groups. Estimates of the odds of currently-graduating science PhDs getting a job in academia range from 20-50% [citation needed].

I have experience as a scientist collaborating with companies on research and development, and I teach design thinking, innovation, and entrepreneurship through the SURGE program. These experiences have shown me that data science and critical thinking skills, combined with a background in psychology or neuroscience, are highly valued. I’ve also talked to many recent graduates who found that their lack of coding skills held them back from the most interesting (and lucrative) job opportunities. Almost universally, these opportunities are in the knowledge economy — be that startups, big tech companies, healthcare, government, or other sectors. These fields all rely on people’s abilities to work with data, interpret it, and use it to make decisions. Training in data science will thus both prepare you to work more effectively in psychology and neuroscience, but also provide you with fundamental, cross-cutting skills that you will likely find useful whatever direction your future takes you.