Course Description: 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, leveraging text mining of the neuroscience literature, and human neuroimaging analyses.
Please see https://github.com/BIPN162/BIPN162_SP24 for the most recent syllabus and course materials.
There is no official textbook for this course. Instead, we’ll be relying on several online resources:
- VanderPlas, Whirlwind Tour of Python
- VanderPlas, Python Data Science Handbook -- this book is available free online or in print.
- Wallisch, Neural Data Science
- Adhikari & DeNero, The Foundations of Data Science
- Software Carpentry, Plotting and Programming in Python
Weeks 1-2: Introduction to data science & scientific computing packages
Weeks 3-4: Exploratory data analysis and statistical foundations
Weeks 5-6: Linear models
Weeks 7-8: Dimensionality reduction, classification, and time series models
Week 10: The future of neural data science & final projects.