Information Technology MSc
Databases and Data Analytics (M) COMPSCI5076
- Academic Session: 2026-27
- School: School of Computing Science
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 1
- Available to Visiting Students: No
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
The aim of this course is to introduce students to the concepts of information management by way of databases, including relational databases, data mining/recommendation systems, functional dependency and normalization theories, NoSQL/Big Data Systems, and large-scale analytics paradigms. The course will provide students with the opportunity to develop skills which will assist them to manage information in the current digital age.
Timetable
Two one-hour lectures and a three hour drop in lab session per week supported by a tutor.
Excluded Courses
Information Systems and Databases
Co-requisites
None
Assessment
Exam 80%, Written Assignment 20%
Main Assessment In: December
Are reassessment opportunities available for all summative assessments? No
It is the default expectation that all courses will offer opportunities for reassessment or deferred assessment. Where it is not possible to offer this in some assessment components, the grade achieved at the first attempt will be counted towards the final course grade, and any exceptions for this course are described below.
[No exceptions]
Course Aims
To provide students with opportunities to develop an understanding of modern methods of managing computerised information. This includes the principles and fundamentals of relational database conceptual design, relational algebra, preliminaries in relational database systems, introduction to NoSQL and Big Data systems.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
1. Design a relational database using entity relationship diagrams and following the principles of normalization;
2. Use a database management system (DBMS) to construct and manage a relational database;
3. Use the Structured Query Language (SQL) to construct SQL statements issued over relational databases;
4. Apply normalisation techniques and functional dependency to a relational database schema;
5. Explain how databases can handle physical design and heuristic optimization based on relational algebra;
6. Analyse the execution efficiency of SQL queries to heuristically optimise queries across centralised databases.
7. Evaluate what the most appropriate relational schema design is for a given context and discuss related issues.
8. Explain how the basic NoSQL and Big Data Systems manage large-scale data.
9. Analyse the fundamental data analytics tasks using the Map Reduce paradigm.