Big Data in Biomedical Sciences

COURSE OBJECTIVE

We are witnessing a rapid expansion of genetic, neuro- and biomedical data and the application of all this data to medical science. Being able to analyse all this data is becoming a new challenge in the field. In this course we will discuss developments in the field of "big data" analysis, discussing several techniques to analyse large datasets, discuss different forms of big data in the field of biomedical and neurosciences, and dicuss general principles of data analysis.

The goal of this course is to get you familiar with some of the core principles of Data analysis techniques. We will go over different data analysis techniques by means of examples of big data in the field of neurosciences.We will familiarise you with the general elements of (Big) Data analysis, get  you up to speed with ongoing revolutions in the field of big neuro, genetic, and biomedical data, and familiarise you with key techniques used in (big) data field. The course contains 3 core elements of Big Data in Biomedical Sciences. These elements are mixed through out the course and include:

• Principles of Big Data analysis
• Big Data types in neuroscience
• Data analysis techniques

Learning objectives of the course include:

• Students will learn how various types of biomedical and neuroscience data are acquired,how they can be analysed using Data Analysis methods and used in fundamental and translational research in disease. A focus will be on neuroscience data, including genetics, transcriptomics, proteomics and connectomics.
• Students will learn 'how-to' knowledge about how to work with raw vs structured data, merging and integration of datasets, statistics for big data, graph analytics, exploratory data analysis and predictive analytics.
• Students will have knowledge of principles on how to share and post data and coding.
• Student will understand the value of accurate and understandable metadata, computer programming and how to share data and scripts with others.
• Students oversee the potential and current challenges of big data applications in genetics, neuroscience, connectomics and transcriptomics.
• Students have hands-on experience with programming algorithms for data analysis or other bio- and neuroinformatics analyses.
• Students will have basic knowledge about strategies for big data data storage and processing
• Students have sufficient insight into bio- and neuroinformatics data workflows, possibilities and limitations to effectively communicate with neuro-informaticians.
• Students can independently collect up-to-date knowledge on the above topics ('metalearning').
• Students will learn how to communicate Data Analytic findings by means of a data science report.

COURSE CONTENT
This elective addresses important concepts in bioinformatics and big data mining, with powerful applications in biomedical sciences. Lectures and practical assignments provide theory and hands-on experience in fast moving fields of personalized medicine, genetics, neuroscience, connectomics and metagenomics.

TEACHING METHODS
Each week the course will offer lectures (8 weeks, 4hs per week) and computer practicals (8 weeks, 4hs per week + 4 hrs self-study).
Expect to spend approximately 100 h on self-study and working on the computer assignments and student data report.

TYPE OF ASSESSMENT
The knowledge in the lectures will be tested by a written exam with open questions held at the end of the course.
Each practical assignment will be evaluated individually by the teachers. The criteria for grading will be made accessible in the form of 'Rubrics' in Canvas.
The final grade will be calculated as 60% (final exam) and 40% (assignments).
To pass the course, both the exam and assignments need to be graded 5.5 or higher.

REMARKS 
This elective is related to the learning track Bioinformatics.It is highly recommended in preparation for the following minors:

• Bioinformatics & Systems Biology, Personalized Medicine, Neurosciences, Research minor: Science in Medicine.