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, but I can tell you why I designed this course, and what I hope you will get out of it.

I’ve been involved in psychology and neuroscience research for over 25 years, and from the beginning I recognized that coding skills were highly prized in every lab I worked in, or knew of. And yet, coding was not typically part of the curriculum in program I was in — at best, it was an elective, but more commonly students learned to code on their own, to varying degrees of success and proficiency. In my own case, I learned to code largely by trying to understand code written by others that did things with data that I wanted to d0 — or similar things.

This is Not CS 101

Programming courses are usually taught through computer science departments, or faculties, and these are most commonly oriented towards computer science students. But the goals of computer science students and programs are quite different from scientists who want to use code (programming) to understand data. As a result, science students sometimes find what they learn in computer science classes hard to relate to their discipline. At the same time, there is a huge difference between “hacked together” code written by self-taught scientists, and clearly written code that follows best practices of style. Good code is efficient and understandable. And a deeper, more systematic understanding of code leads to code that is more likely to be accurate. At the same time, having proficiency with code empowers you to do things with data that you might not otherwise be able to do.

Neuroscience Needs Data Science

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). 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. This course aims to address these needs. The course has also been drastically revised as of 2023 to incorporate more machine learning and AI tools, and to focus less on technical aspects of coding, and more on the actual “data science” aspects of the course.

Data Science Skills Have Value Beyond Neuroscience

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 USA 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% UofT, 2018. Coding, and data science, are valuable and highly employable skills that are much more widely applicable than the specific disciplinary training you get working on a particular research topic.

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 neuroscience or psychology, 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.