Course Details
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Brief Description
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| Module Description | Module Code | NFQ Level | ECTS Credits | Start Date | Fees |
|---|---|---|---|---|---|
|
Data Analytics for Cancer Real World Data Research |
BM6063 | 9 | 9 | TBC |
€1,250.00 |
This micro-credential represents a single module within a larger further award (eg. Certificate, Diploma, Masters). By taking this micro-credential you may be eligible to apply for a credit exemption should you progress to study for a further award.
The purpose of this module is to provide practical experience and upskill data scientists and health informatics professionals who manage, curate or analyse cancer electronic health data and/or are engaged in cancer real world evidence research. The module will provide students with an understanding of the research challenges posed by traditional siloed healthcare data, and how standardisation of data can facilitate sharing and federated data analysis. This module will provide practical experience in transforming health data to widely used international data standards to facilitate of large-scale, federated data analytics research. It will prepare participants for real-world data research. By working with real-world cancer data and developing software and data science skills, participants will be able to contribute to the growing efforts in federated data sharing and secondary analysis of health data.
Learning Outcomes
On successful completion of this module, students will be able to:
- Recommend standards for interoperability and secondary use of data in the cancer healthcare record
- Explain why a common data model systems facilitates observational studies
- Critically evaluate data quality of observational health data when framing a research question
- Create and validate software to standardise raw health data to a common data model with standardised terminologies and ontologies
- Evaluate software to query, map fields, construct concept sets, and standards for genetic and genomic data
- Defend that software adheres to open-source software community best practice, and facilitates reproducible secondary data research.
- Effectively and respectively participate in multidisciplinary research teams that process healthcare data for secondary use
- Display commitment to ethical and rigorous standards in secondary use of healthcare data.
- Design and produce an software that harmonises health data to a common data model and enables secondary data analysis
Assessment
There is no final exam for this module. You will be assessed through continuous skill-based assignments, provided by your lecturer and tutor.
Student Weekly Time Commitment
15 hours
Applicants must have a minimum Level 8 honours degree, at minimum second class honours (NFQ or other internationally recognised equivalent), in a relevant engineering, computing, mathematics, science or technology discipline. Proficient in R or Python essential. Experience in working with healthcare or cancer data.
Entry requirements are established to ensure the learner can engage with the course material and assessments, at a level suitable to their needs, and the academic requirements of the module. By applying to this micro-credential, you are confirming that you have reviewed and understand any such requirements, and that you meet the eligibility criteria for admission.
Successful completion of this module does not automatically qualify you for entry into a further award. All programme applicants must meet the entry requirements listed if applying for a further award.
€1,250.