Postgraduate Course: Fundamentals of Data Management (INFR11176)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||Better and more effective approaches to managing digital research data are becoming increasingly important in computational science and beyond. The scientific data sets that underpin research papers can now occupy many gigabytes of storage, and are increasingly complex and challenging to work with. This course introduces students to the ideas, methods and techniques of modern, digital data management.
The course will cover:
- Why managing research data better matters, and why it's hard
- Data management planning: a required part of twenty first century research
- Data formats: structuring data and keeping them useful
- Relational and NoSQL databases
- Metadata: describing data and keeping them useful
- Publication and citation of research data
- Persistence, preservation and provenance of research data
- Licensing, copyright and access rights: some things researchers need to know
- Important distributed data processing tools and techniques, such as: Spark and MapReduce
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2019/20, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||100% Written Exam
||Provided via practical class sessions and after the exam.
||Hours & Minutes
|Main Exam Diet S1 (December)||Fundamentals of Data Management||2:00|
On completion of this course, the student will be able to:
- Identify common data formats and understand applicable usage of these.
- Demonstrate both an understanding of data storage techniques and an ability to apply these techniques
- Explain and appreciate the importance of long-term data management
- Analyse important distributed data processing models
- Synthesise these concepts to address data management problems
|Made available via Learn|
|Graduate Attributes and Skills
||- Solution Exploration, Evaluation and Prioritisation.
- Critical thinking
- Communication of complex ideas in accessible language
- Working in an interdisciplinary field
- Programming and Scripting
||There are limited spaces on this course. Students not on the MSc in High Performance Computing or MSc High Performance Computing with Data Science or a programme of study in the School of Informatics should contact the course secretary to confirm availability and confirm that they have the required prerequisites before being enrolled on the course.
The course is available to PhD students for class-only study. PhD students requiring a form of assessment (e.g. SUPA/School of Physics and Astronomy CDT students) must contact the course secretary to confirm method of enrolment.
|Additional Class Delivery Information
||2 lectures per week, 1x practical class per week (Weeks 1-10).
|Keywords||FDM,EPCC,Data Science,Data Management,Data Processing,Databases,NoSQL,HPCwDS,MapReduce,Spark,Data
|Course organiser||Dr Adam Carter
Tel: (0131 6)50 6009
|Course secretary||Mr Ben Morse
Tel: (0131 6)51 3398