Core Module Information
Module title: Data Management and Processing

SCQF level: 10:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET10416
Module leader: Dimitra Gkatzia
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science


Description of module content:

This module will explore and develop data management and processing solutions that will work on dirty, complex, real-world data. This module will examine the key concepts of data warehousing, data cleaning, and data processing in the context of business requirements and focus on how to combine these steps into a coherent data processing pipeline.
First, modern tools and techniques in data management will be examined, with the emphasis on good practice and professional approaches of storing and handling data. Next, the module will examine ways of cleaning noisy real-world data in order to make it suitable for data processing. Finally, data processing and collation techniques such as Machine or Deep Learning will be applied to the data to extract structure and elicit comprehension of the data. Throughout the module, advantages and disadvantages of using local and cloud approaches will be explored, alongside discussing common parallel approaches to facilitate faster solutions.
In short, the goal of this module is to allow students to understand a data processing pipeline from raw data to final delivery. It will cover:
• Data warehousing and storage techniques
• Data cleaning techniques
• A discussion of cloud approaches
• Data processing and collation techniques
• An introduction to parallel data pipeline approaches

Learning Outcomes for module:

On completion of this module you will be able to:
LO1: Compare different data warehousing techniques and technologies related to data management
LO2: Appraise different methods of data cleaning in the context of large or complex data sets
LO3: Critically reflect on the advantages of local and cloud solutions for data processing
LO4: Integrate industry-standard data collation techniques
LO5: Create a data processing and management pipeline from raw data to a final delivery

Full Details of Teaching and Assessment
2022/3, Trimester 3, BLENDED,
Occurrence: 001
Primary mode of delivery: BLENDED
Location of delivery: MERCHISTON
Member of staff responsible for delivering module: Dimitra Gkatzia
Module Organiser:

Learning, Teaching and Assessment (LTA) Approach:
The student must be employed as a Graduate Level Apprentice to complete this module. The students taking this module are primarily based in and around Edinburgh. The delivery is monthly day release, face to face. For a module this equates to 2 hours per week of lectures, supported by online materials.
This module adopts a blended approach within formative laboratory-based practicals, discussion tutorials and seminars, and lectures. Practical instruction is supported with virtual learning environment (VLE) resources aimed at reinforcing some of the principles discussed in lectures and tutorials, with further directed study also provided. Practical labs are supported by a workbook where students complete work assignments and also describe the methodologies that they have undertaken (LO 4 - 5).
Self-study readings, supported by online discussions forum hosted through the VLE, will develop skills as independent learners (LO 1 - 3). Formative feedback will be provided via online quizzes (LO 1 - 5). The lecture programme will be enhanced by material from guest speakers (where appropriate) and will be made available online.

Formative Assessment:
There will be a series of end-of-section online quizzes that will provide formative assessment throughout the course. In general, these can be taken when appropriate for a student, in order to facilitate their own learning. Formative feedback on practical work can also be provided through the online discussion forum and day release sessions. Reflective Exercises will enable students to apply theory to practice – this is not assessed, but it will support personal and professional development.

Summative Assessment:
Summative assessment will be in the form of a single component, which will consist of an ongoing software development task and associated written documentation worth 100% of the final mark (covering LOs 1 - 5). We would expect this documentation to reflect on how these tools could be used to improve data management and processing within the student’s workplace.
This component will be broken down into two elements, due week 7 and 13. The first element will be submitted in week 7 in order to give interim summative feedback (30%: L.Os 1, 2). The second element will be submitted in week 13 (70%: LOs 3, 4, 5), after which final summative assessment will be given.

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Practical classes and workshops 21
Online Guided independent study 179
Total Study Hours200
Expected Total Study Hours for Module200

Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Project - Practical 30 1,2 7 HOURS= 12
Project - Practical 70 3,4,5 13 HOURS= 28
Component 1 subtotal: 0
Component 2 subtotal: 0
Module subtotal: 0

Indicative References and Reading List - URL:
Contact your module leader