This course is part of OsloMet International Summer School. 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.
This course is recommended for PhD and Master Students that are involved in neuroscience research projects.
It is an advantage if you have knowledge in algebra, linear algebra, statistics, calculus, neurophysiology, and any programming language, especially Python.
It is required that the you have a bachelor degree.
A letter of recommendation from the advisor outlining the your project testifying that the you are currently doing neuroscientific research at the Master’s or PhD’s level is required (template will be provided).
How to apply
You select courses when you apply to summer school.
The application deadline is 1 February.
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.
The modules in the course are organized so you will 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 you 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.
You will develop understanding through constant presentation of your work and dialectical reflection over your choices, results, and interpretations.
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.
You 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, you and an other student will constantly have the opportunity to recollect, explain the content to each other, and justify your work, as well as to provide feedback to your partners.
With the intent to prepare you to go beyond the methods taught in the course, once per module, you can read relevant papers in neuroscience, and discuss how to implement your analysis.
You need to complete four online quizzes. One at the beginning of the course, then three quizzes during the course. Each one of them following each of the three weeks of classes.
Exam and Assessment
An individual oral presentation, which counts for 100% of the final mark
The oral presentation cannot be appealed.
Permitted Exam Materials and Equipment
Only personal notes written in non-digital media will be allowed during the oral exam.
The final assessment will be graded on a grading scale from A to E (A is the highest grade and E the lowest) and F for fail.
Two examiners will be used, one of which can be external. External examiner is used regularly.
Questions about This Course?
You can contact us by e-mail if you have questions about this course.