1. Activate and consolidate previous knowledge of statistics and programming, applied in a specific way

2. Know the basic principles of Linear Algebra and its application in neuroscientific data processing

3. Apply statistical and computational techniques for the detection, characterization, and removal of noise in neuroscientific data

4. Implement segmentation and normalization methods to identify regions of interest in neuroimaging and correct inter-subject variations

5. Know how to apply advanced statistical methodologies for time series analysis and spatial comparisons in neuroscientific analysis

The teaching methods are designed to promote an integration between theoretical knowledge and practical application. Lectures will provide the conceptual foundation, while hands-on labs and projects will allow students to directly apply knowledge in real-world neuroscientific data analysis situations.