Core Module Information
Module title: Data Management and Processing

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

Module code: SET10115
Module leader: Thomas Methven
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:

Upon 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 1, BLENDED, Edinburgh Napier University
Occurrence: 001
Primary mode of delivery: BLENDED
Location of delivery: MERCHISTON
Partner: Edinburgh Napier University
Member of staff responsible for delivering module: Thomas Methven
Module Organiser:

Learning, Teaching and Assessment (LTA) Approach:
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 worksheets where students complete work assignments and also describe the methodologies that they have undertaken. Practical work will also focus on specific examples that build up to a coherent body of knowledge within the workbook (LO 4 - 5).
There is one two-hour lecture each week, covering both the theory and practical material (LO 1 - 3). Lectures are also supported by directed study, with the course texts utilised extensively as further reading each week. The lectures provide the discussion on the ideas that are applied in the practical sessions. The lectures will also highlight the advantages and disadvantages of formal approaches, compare said approaches against other software development methods, and integrate a formal method into existing development processes.

Formative Assessment:
To support formative feedback, the Software Engineering subject group utilise a lab based teaching approach across their provision. During these lab sessions, staff will discuss and evaluate student progress and provide feedback on how well they are progressing with their work. All modules in the subject group also require students to demonstrate their coursework on submission to provide further formative feedback on how the work could be improved

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 Lecture 24
Face To Face Practical classes and workshops 24
Independent Learning Guided independent study 152
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= 12.00
Project - Practical 70 3,4,5 13 HOURS= 28.00
Component 1 subtotal: 30
Component 2 subtotal: 70
Module subtotal: 100

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