STKD6810 Neuro-insights: Data Science Approaches in Neuroscience II Emneplan

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 a 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.

All support materials are allowed.

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

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Knowledge

Upon successful completion of this course, the student knows:

• theoretical and practical aspects of data management and data processing for different data types (electrophysiology, imaging, and behavior), so they can independently engage with the state-of-the art literature.
• how statistics, dimensionality reduction, signal processing, calculus, and supervised and unsupervised learning may be applied to data analysis in neuroscience research.
• commonly encountered problems in data science and the most trusted strategies to solve them.
• basic data visualization techniques and how to employ them in exploratory data analysis and scientific communication.

Skills

Upon successful completion of this course, the student can:

• apply data analysis techniques to formulate a hypothesis, collect data, preprocess it, analyze it, and reach conclusions about the data.
• plan, design and implement data analysis pipelines for the most common types of data in neuroscience.
• identify and define a problem and craft a solution using data analytics.
• critically assess the results as well as justify and explain the methodological choice.
• identify new opportunities for organizational change including process improvements, cost reduction, or efficiency improvements.

General competence

Upon 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.

The course on Universal Design of ICT and Accessible Systems will introduce the students to the research discourse and cutting-edge research within universal design, including the research conducted by the members of the Universal Design of Information and Communication Technologies research group.The course will focus on interdisciplinary and multidisciplinary research where knowledge and expertise in universal design of ICT play an important role.

The course will be offered once a year, provided 3 or more students sign up for the course. If less than 3 students sign up for a course, the course will be cancelled for that year.

None.

Students who complete the course are expected to have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge:

On successful completion of the course, the student:

• knows the most influential channels where universal design research of digital systems is communicated.
• knows the historically most influential research works and the most important contemporary works and problems areas within universal design and accessibility, including the local research group's own research discourse.
• understand the role of universal design and accessibility in the bigger picture of digital engineering.
• has in-depth knowledge of important stakeholders nationally and internationally related to universal design and accessibility, including special interest organisations.
• has in-depth knowledge of various national and international legislature, regulations, recommendations and standards related to universal design of accessible computer systems.
• has in-depth knowledge of key research methods used in universal design of accessible computer systems.

Skills:

On successful completion of the course, the student can:

• discuss issues related to universal design, accessibility and vulnerable groups using non-discriminatory and inclusive language.
• plan, lead and manage research projects related to universal design of accessible digital systems.
• take responsibility for the universal design of ICT aspect in interdisciplinary and multidisciplinary research projects.
• ensure that a research project adheres to the highest standards in terms of ethics, protecting vulnerable participants, adequate provisions of consent and adhering to formal requirements for storing information about participants.
• challenge the existing knowledge and practices in the chosen specialisation area of engineering, design or art.

General competence:

On successful completion of the course, the student can:

• conduct ethical and scientific research of high international standard
• communicate and collaborate with experts from other disciplines on larger interdisciplinary and multidisciplinary research projects.
• Recognise and assess a project's potential for innovation and its likely impact on society.
• participate in debates and communicate results through recognised international channels, such as academic conferences.

The course will take the form of a series of seminars where the students actively participate in the Universal Design of ICT research group meetings, in which current and state-of-the-art research is discussed. The students will learn by doing in a real-world research setting with a group of established and respected researchers. The students will present their own papers and listen to those of other students and staff. The students will also actively critique and challenge the other participants. The students are provided with a sound foundation in research skills and naturally integrated into the local research community and its research discourse.

The following required coursework must be approved before the student can take the exam:

• Three individual oral presentations of assigned publications.
• Participate as prepared discussant for three presentations by other group members.
• Independently find and study publications relevant to the research discourse.
• Minimum 80% attendance at research group meetings during the course.

An individually written survey paper on a selected topic related to the course. The paper should be 10 pages in length following the ACM SIG format.

The exam can be appealed.

For students who only want to attend the course, no written coursework is required.

Pass or fail.