Course module NWI-NM120 Credits (ECTS) 3 Category MA (Master) Language of instruction English Offered by Radboud University; Faculty of Science; Wiskunde, Natuur- en Sterrenkunde; Lecturer(s) Coordinator prof. dr. F.P. Battaglia Other course modules lecturer Lecturer prof. dr. F.P. Battaglia Other course modules lecturer Contactperson for the course prof. dr. F.P. Battaglia Other course modules lecturer Examiner prof. dr. F.P. Battaglia Other course modules lecturer Academic year 2019 Period KW4   (13/04/2020 to 30/08/2020) Starting block KW4 Course mode full-time Remarks - Registration using OSIRIS Yes Course open to students from other faculties Yes Pre-registration No Waiting list No Placement procedure -

	Aims The students will learn the basics of neural data analysis, and in the process they will acquire a number of programming and statistical techniques applicable to a broad range of problems Content - The python programming ecosystem, version control software - Key data analysis techniques in python (numpy, scipy, pandas, jupyter) - Advanced Data visualization  - Bootstrap and Monte-Carlo statistics - Statistical inference - Analysis of spike data  - Analysis of LFP - Analysis of optical imaging data    Level Presumed foreknowledge Programming in Python at an "advanced beginner" level. If you don't have programming knowledge and you don't have a chance to take one of the many introductory courses at RU, you may check out online courses for example at https://www.datacamp.com/ Contact the instructor for more information Test information 70% workshop assignments, 30% final project Specifics A group of 7 data analysis workshops, on simulated and real neural data, concentrating on system neuroscience approaches and problem (Local field potential, multiple spike trains, analysis of animal behavior). You will learn the most important data analysis methods and programming techniques. Additional comments A group of 7 data analysis workshops, on simulated and real neural data, concentrating on system neuroscience approaches and problem (Local field potential, multiple spike trains, analysis of animal behavior). You will learn the most important data analysis methods and programming techniques. Topics - Use of the python programming ecosystem for data analysis - Main python packages for data analysis (numpy, spicy, pandas) - (Advanced) Data visualization techniques - Analysis of local field potential, spectral methods, wavelets - Anaysis of spike train data - Multi-dimensional Statistical inference applied to neural data - Analysis of animal behavior Test information 70% workshop assignments, 30% final project Prerequisites Programming in Python at an "advanced beginner" level. If you don't have programming knowledge and you don't have a chance to take one of the many introductory courses at RU, you may check out online courses for example at https://www.datacamp.com/ Contact the instructor for more information Contact information Francesco Battaglia (f.battaglia@science.ru.nl)
	Required materials Learning Management System (Brigthspace) Material will be provided on Brightspace Instructional modes Lecture Remark The Lecture will be included in the Computer lab Practical computer training Project Attendance Mandatory Yes Remark Final project (30% of grade) Tests Final Project Test weight 3 Test type Project Opportunities Block KW4, Block KW4 Assignments Test weight 7 Test type Assignment Opportunities Block KW4