Information regarding our 2023/24 admissions cycle

Our 2023/24 postgraduate taught admissions cycle will open on Monday, 10 October. For most programmes, the application form will not open until this date.

MSc Data Science (Applied Urban Analytics)

Year of entry: 2023

Course unit details:
Understanding Databases

Course unit fact file
Unit code DATA70141
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by
Available as a free choice unit? No

Overview

The unit aims to address the theory and the role of databases in organisations and help students to gain practical experience in:

  • designing and implementing databases to solve practical problems according to the relational principles
  • designing and implementing databases in non-relational formats
  •  using a contemporary Database Management System, MySQL, Python and PHP for database programming
  • designing and implementing distributed databases for data intensive systems.

Aims

The unit aims to address the theory and the role of databases in organisations and help students to gain practical experience in:

  • designing and implementing databases to solve practical problems according to the relational principles
  • designing and implementing databases in non-relational formats
  •  using a contemporary Database Management System, MySQL, Python and PHP for database programming
  • designing and implementing distributed databases for data intensive systems.

Learning outcomes

Students should be able to:

  • Demonstrate understanding of the role and importance of data and information in organisations;
  • Demonstrate understanding of the principles and objectives of data modelling;
  • Demonstrate understanding of the principles of the relational model;
  • Demonstrate competence in relational database design and implementation using a contemporary database management system and MySQL;
  • Demonstrate understanding of the principles of non-relational data models;
  • Demonstrate competence in non-relational database design and implementation;
  • Demonstrate the ability to identify and classify data quality requirements, and approaches to their resolution;
  • Apply data modelling for a specific business problem;
  • Demonstrate competence in distributed database design and implementation;
  • Demonstrate competence in database programming using Python and PHP;
  • Demonstrate competence in designing and implementing distributed databases for data intensive systems like Twitter and Uber.

Teaching and learning methods

Each session will include a practical component where students will be asked to demonstrate an understanding of the topics covered in the asynchronous e-learning materials; each set of asynchronous materials will consist of about an hour of video content, coupled with quizzes and activities, examples and practice exercises to ensure knowledge and understanding.

Assessment methods

Assessment Task Length How and when feedback is provided Weighting
Weekly self-assessment quizzes on Blackboard (formative) MCQ quizzes Auto-online 0%

Coursework 1 (Individual work) has 3 components:
1. Design of ER scheme for given database
2. Relational scheme for given database
3. SQL query report

Parts 1&2 (annotated diagrams)

Part 3: report (1000 words) + screenshots

Written and/or verbal 30%

Coursework 2 (group work) has 2 components
1. Design and implementation of NoSQL database + report
2. Individual personal reflection essay

Part 1 1500-200 words

Part 2 1000 - 1500 words

Written and/or verbal 30%
Online exam 2 hours   40%

 

Feedback methods

 Students will receive the following feedback:

  • Automated feedback from quizzes
  • Feedback reports from staff following their coursework submissions
  • In-person feedback during the synchronous practical sessions (if requested)

Teaching staff

Staff member Role
Thomas Carroll Unit coordinator
Rotimi Ogunsakin Unit coordinator

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