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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Fundamentals of Data Management (INFR11176)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryBetter 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.
Course description 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)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2020/21, Available to all students (SV1) Quota:  80
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 10, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Assessment (Further Info) Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam 75%
Coursework (Class test) 25%

Written examination comprised of 3 compulsory questions (equally weighted).

Coursework (class test) is an examination question presented to the students mid-Semester
Feedback Provided via practical class sessions, class test, and after the exam.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)Fundamentals of Data Management2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Identify common data formats and understand applicable usage of these.
  2. Demonstrate both an understanding of data storage techniques and an ability to apply these techniques
  3. Explain and appreciate the importance of long-term data management
  4. Analyse important distributed data processing models
  5. Synthesise these concepts to address data management problems
Reading List
Made available via Learn
Additional Information
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
Special Arrangements 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 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).
KeywordsFDM,EPCC,Data Science,Data Management,Data Processing,Databases,NoSQL,HPCwDS,MapReduce,Spark,Data
Course organiserDr Adam Carter
Tel: (0131 6)50 6009
Course secretaryMiss Jemma Auns
Tel: (0131 6)51 3545
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