Neuro-insights: Data Science Approaches in Neuroscience Programplan

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.

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.

It is required that you have a Bachelor degree.

A letter of recommendation from your advisor outlining your project and testifying that you are currently doing neuroscientific research at the Master or PhD level.

See course description

The teaching methodology is oriented by Bloom's taxonomy of educational goals, namely, recollection, understanding, application, analysis, evaluation, and creativity. To promote recollection, understanding, and application, the course will consist of seminars taught by the teaching staff of OsloMet and other guests (experts in neuroscience or data science fields), coding workshops and problem solving oriented projects. Students will actively participate by implementing the full data processing pipeline from extracting the raw data to building visualizations. The pipeline and good habits will be consolidated through repetition in different modules, contexts, and data types, which is known to promote generalization of the knowledge. Organized in pairs, the students will constantly have the opportunity to recollect, explain the content to each other, and justify their work, as well as to provide feedback to their partners. With the intent to prepare the students to go beyond the methods taught in the course, once per module, the participants will read relevant papers in neuroscience, and discuss how to implement their analysis.