Neuro-insights: Data Science Approaches in Neuroscience

About

Because most young researchers in life and health sciences do not have a solid quantitative background, they face difficulties when analyzing data independently. This difficulty represents a major drawback in research. Students waste time learning analytical methods by themselves that could be more quickly learned with proper instruction and support. Additionally, the lack of convention or standards in some fields is a source of confusion that slows the learning process. As consequence, the quality of insights and research productivity suffer. This course provides a comprehensive introduction to data science and big data applied to neuroscience research.

Its content is designed to train the participants in state-of-the-art techniques in data analysis and machine learning. This will enable the students to interact independently with the data and draw insights from them. The modules are organized so the participants have the opportunity to learn how to handle the most common data types (e.g., EEG, calcium imaging). Special attention is given to field-tested data management protocols, as they are critical for a fast transition from data acquisition to knowledge generation.

This is a hands-on course where the students will learn from implementing the analysis themselves with close supervision. The course will focus on case studies using data from real experiments; advanced students may choose to use their own data. The students will develop understanding through constant presentation of their work and dialectical reflection over their choices, results, and interpretations.

Target group

Recommended preliminary courses
This course is recommended for PhD and Master Students that are involved in neuroscience research projects. It is an advantage if the student has knowledge in algebra, linear algebra, statistics, calculus, neurophysiology, and any programming language, especially Python.

Required preliminary courses
It is required that the student has a bachelor degree.

A letter of recommendation from the advisor outlining the student's project and testifying that the student is currently doing neuroscientific research at the Master’s or PhD’s level (template will be provided).

Course aim

General competence

On successful completion of this course the student can apply:

- Data analysis principles to neuroscientific data in the context of its own research.
- Methods and tools for data analysis and visualization.
- Heuristics and strategies commonly used in the research field to solve data analysis problems.

Fee info