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At the end of this course, participants will be able to:
Describe the complexity of data;
Describe numerous examples of big data applications;
Describe current methods in the analysis of big data and understand their differences;
Recognize the main challenges and potential limitations of big data research
Apply different machine learning methods (including basic natural language processing) Understand the potential and limitations of big data for causal inference.
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Please note: This is a Summerschool course.
Period (from-till): This course is taught several times per year, during fulltime weekdays according to the following Schedule (Face-2-Face in Period A, Online in Period B):
| Education form |
Startdate |
Enddate |
Registration period |
| Face-2-Face |
15-8-2022 |
19-8-2022 |
BMS_P4_A |
*Online courses are only available for Epidemiology students following a full online programme.
Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl
Registration:
Register via Home | Utrecht Summer School
Course description:
The digital universe is expanding continuously. This huge amount of information often referred to as big data have a huge potential to answer questions that could not be answered before. For example, in biomedical sciences, researchers increasingly make use of these Big Data, by pooling real-time data from multiple sources including electronic healthcare records in order to e.g. detect diseases at an early stage.
The summer school on big data will provide you with a sound introduction to this exciting new field in health research. Spanning topics such as:
- Introduction to machine learning (ML)
- Automated ML
- Causal inference using big data and ML
- Natural language processing
- Data linkage
This will be embedded in medical research through numerous real-life examples and case-studies.
Literature/study material used:
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Mandatory for students in own Master’s programme:
MIght be for a specialization programme of Epidemiology & Epidemiology Postgraduate
Optional for students in other GSLS Master’s programme:
Yes
Prerequisite knowledge:
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