Data skills and data science are moving from something that a small minority of computational scientists got excited about to central tools used by the bulk of neuroscientists. The objectives for this short course are two-fold. First, we will teach basic, useful data skills that should be in the toolkit of virtually all neuroscientists. Second, we will survey the field of more advanced data science methods, to give participants an overview of which techniques to use under which circumstances.
The course is structured around hands-on programming exercises. Lectures will go hand in hand with tutorials. The focus is on participants solving many frequently encountered data analysis problems themselves during the day, aided by lectures of leading experts.
The course will cover a broad range of topics. It will start with basic topics including data cleanup, data visualization, and fundamental statistical ideas. It will progress to everyday problems like fitting functions to tuning curves, adding error bars, and decoding. Some more advanced topics will include generalized linear models, dimensionality reduction, time-series data and networks. The course should produce a solid understanding of the basic techniques used for neural data analysis.
During this course, the tutorials will be presented using the technical computing program, Matlab, due to the instructors’ preferences. However, alternative platforms and programming languages are available and appropriate for neural data analysis. Other options include, but are not limited to: Python (SciPy, iPython, NumPy),
R, Scilab, Free Sage, and Octave.
help on how to format text