Intellectual property, Patent,
Quality
procedures, Biotechnology and Regional Economy, Entrepreneurship
- Teacher: Emmanuel Frouté
- Teacher: Agnès Nadjar
- Teacher: Jacques Noël
This course is devoted to the acquisition of programming techniques in python for data preprocessing and analysis. The students will get themselves familiarized with the Jupyter notebook environment to run python code, install libraries and exchange data. They will learn how to use specific libraries for data manipulation (numpy, pandas) as well as data visualization (matplotlib, seaborn). They will also learn fundamentals in programming (functions, objects, loops and conditional statements).
Equipped with this programming knowledge, students will analyze spike trains from single neurons and local field potential (LFP) data in order to reproduce classical representations like raster plots, PSTH, tuning curves, evoked responses for LFP. The main idea is to show the common logic behind all these analyses types of data.
It is organized in online classes and practical classes, with homework on notebooks that are integral parts of the commitment.
Equipped with this programming knowledge, students will analyze spike trains from single neurons and local field potential (LFP) data in order to reproduce classical representations like raster plots, PSTH, tuning curves, evoked responses for LFP. The main idea is to show the common logic behind all these analyses types of data.
It is organized in online classes and practical classes, with homework on notebooks that are integral parts of the commitment.
- Teacher: Nicolas Catz
- Teacher: Matthieu Gilson
- Teacher: Rym BenKhalifa
- Teacher: Mai Elgendy
- Teacher: Pierrick Poisbeau