BIPN 162 (Neural Data Science) is a project-based course in which students will use computational notebooks to perform exploratory data analyses and to test hypotheses in large neuroscience datasets, including the differences between unique neuron types, text mining of the neuroscience literature, and human neuroimaging analyses.
See the Syllabus for Winter 2020.
That’s okay, and expected. This course is designed for new coders. If you already know how to code, that’s also okay. There is endless complexity to these datasets, and lots of code to write.
The prerequisites for this course are MATH 11 (Introductory Probability & Statistics) & BIPN 100 (Human Physiology). We’ll rely on a basic understanding of brain regions and the activity of neurons. We’ll also be applying principles of statistics to the data. If you think you have enough background in these topics, file a waiver.
Like all 4-unit courses at UCSD, this course will meet for 4 hours (T/Th, with discussion sections Friday; Schedule of Classes) and will assign work (including reviewing lecture material) amounting to about 6-8 hours of work outside of class. Each week you will complete a coding problem set that will help you develop the skills you need to work with our datasets. In addition, there will be 2 slightly larger projects to complete towards the end of the course.
Programming and data science skills are increasingly useful in biology and neuroscience in particular. The ability to code will enable you to acquire, analyze, and visualize your data. Plus, coding is quite useful beyond biology – why not learn while looking at brains?
Yes! The code is BGGN 240. I’ll ask that you complete a bonus assignment for graduate credit.
In Winter and Summer 2020, this course is being taught by Dr. Ashley Juavinett. I’m developing the course with Dr. Brad Voytek (Cognitive Science) who will be teaching a COGS version of the course in the future.