Hands-on course on neural data science

Venue: Universitat Politecnica de Barcelona (XIII Summer School UPC-UB edition)
Date: from 1st to 5th of July 2019 (afternoons), 15 hours in total
Registration: https://mesioupcub.masters.upc.edu/en/xiii-summer-school-2019/xiii-summer-school-2019

Organizers:

• Andrea Insabato (Universitat Pompeu Fabra, Barcelona)
• Adrià Tauste Campo (BarcelonaBeta Brain Research Center, Barcelona)
• Matthieu Gilson (Universitat Pompeu Fabra, Barcelona)
• Gorka Zamora-López (Universitat Pompeu Fabra, Barcelona)

Description:

The course focuses on several aspects of data science applied to data recorded from the brain: statistical modeling, graph theory, statistical analysis and machine learning. The course is based on a learn-by-coding philosophy: the students will develop a data-driven project throughout the course to understand and apply some of the taught techniques under the supervision of the teachers. Many methods explained during this course can be applied to different data outside brain sciences, so previous experience in neuroscience is not required.

Course goals:

• Understand statistical modeling for time series, their estimation, evaluation and selection
• Understand graph theoretical measures
• Understand machine learning methods: cross-validation, supervised learning, dimensionality reduction
• Use all explained tools in synergy for the analysis of neural data

• Improve Python programming skills
• Learn to compare results from different types of analyses about the same question
• Present the results of statistical analysis in a clear and appealing format

Prerequisites:

Students are expected to have basic knowledge in applied maths (linear algebra and calculus), as well as basic statistics (BS level) and some programming skills (Python language will be used but the course will also be fruitful for students with good programming skills in other languages). This course can be combined with the introductory course offered in the morning for a more gentle progression from basics to more advanced topics. Students should bring their laptop to each session.

Evaluation:

The students will be evaluated via a hands-on project developed during the course in small groups (up to three participants). They will be asked to summarize and present their results.

Software requirements:

All the software needed for this course is free and open source. We recommend Anaconda distribution that allows to create an environment dedicated to the course with all needed software installed. We will provide an environment configuration file to have everything setup from the beginning. However all libraries used can be installed independently of Anaconda (although this usually requires more work).