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.
The content in this GitHub repo is from Winter and Summer 2020 offerings with Dr. Juavinett.
Please note that this course is now taught by Dr. Mikio Aoi and requires some Python knowledge (BILD 62, COGS 18, CSE 8A) as a prerequisite! Contact Dr. Aoi with questions.
- What do the different cell types and circuits in the brain do?
- How do we link genes, circuits, and behavior?
- And can we use open source data to answer these questions?
- Write and edit Python code, particularly in Jupyter Notebooks
- Develop hypotheses specific to big data environments in neuroscience
- Design a big data experiment and excavate data from open sources
- Integrate data from multiple datasets to answer a biological question
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.