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Session: 2022/23
Last modified: 09/02/2021 14:37:30
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Title of Module: Big Data Forensics |
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Code: COMP11078 |
SCQF Level: 11
(Scottish Credit and Qualifications Framework) |
Credit Points: 10 |
ECTS: 5
(European Credit Transfer Scheme) |
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| School: | School of Computing, Engineering and Physical Sciences |
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| Module Co-ordinator: | Jose
Alcaraz Calero |
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| Summary of Module |
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The module aims to furnish students with the specialised understanding and practical skills required to conduct the identi cation, collection, and analysis of large-scale big data systems. The module will examine the internals and architecture of big data systems, the big data forensic evidence ecosystem and forensic artefacts in le system and application data, together with the techniques to perform forensic analysis to uncover events and statistical information (e.g., filee carving, cluster analysis, statistical analysis, etc.). Data visualisation techniques will also be examined. Students will gain con dence through the application of knowledge in practical exercises and case studies.
- This module will work to develop a number of the key 'I am UWS' Graduate Attributes to make those who complete this module:
Universal
• Critical Thinker
• Ethically-minded
• Research-minded
Work Ready
• Problem-Solver
• Effective Communicator
• Ambitious
Successful
• Autonomous
• Resilient
• Driven
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| Module Delivery Method |
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| Face-To-Face | Blended | Fully Online | HybridC | HybridO | Work-based Learning |
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Face-To-Face
Term used to describe the traditional classroom environment where the students and the lecturer meet synchronously in the same room for the whole provision.
Blended
A mode of delivery of a module or a programme that involves online and face-to-face delivery of learning, teaching and assessment activities, student support and feedback. A programme may be considered “blended” if it includes a combination of face-to-face, online and blended modules. If an online programme has any compulsory face-to-face and campus elements it must be described as blended with clearly articulated delivery information to manage student expectations
Fully Online
Instruction that is solely delivered by web-based or internet-based technologies. This term is used to describe the previously used terms distance learning and e learning.
HybridC
Online with mandatory face-to-face learning on Campus
HybridO
Online with optional face-to-face learning on Campus
Work-based Learning
Learning activities where the main location for the learning experience is in the workplace.
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| Term(s) for Module Delivery |
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(Provided viable student numbers permit).
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| Term 1 | | Term 2 | | Term 3 | |
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| Learning Outcomes: (maximum of 5 statements) |
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On successful completion of this module the student will be able to:
L1.
Demonstrate a critical understanding of the specialised theories, concepts and principles of big data technology and data mining algorithms and analytics in the context of a forensic investigation.
L2.
Apply knowledge, skills and understanding in using the principal skills, techniques, practices required to identify potential sources of admissible evidence and formulate a strategy, and execute the acquisition and analysis of evidence.
L3.
Analyse and critically evaluate evidence and procedures specific to the use of big data forensic techniques and appreciate the challenges. |
| Employability Skills and Personal Development Planning (PDP) Skills |
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| SCQF Headings |
During completion of this module, there will be an opportunity to achieve
core skills in:
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| Pre-requisites: |
Before undertaking this module the student should have
undertaken the following:
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Module Code:
| Module Title:
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| Other: | |
| Co-requisites | Module Code:
| Module Title:
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* Indicates that module descriptor is not published.
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| Learning and Teaching |
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| The module will be delivered by means of lectures and supervised hands-on lab work. Lectures will cover the theoretical background and practical applicability in real life problems. Concepts will be introduced by posing a practical problem and working out the needed theoretical knowledge to solve them. The delivery will encourage student participation to ensure an active learning experience. Group discussions will be held to promote critical thinking and boost informed decisions on the suitability of different state-of-the-art methods. Lab exercises will help student develop their knowledge in incremental fashion using a learning-by-doing approach. This will support the development of knowledge and understanding of the topics. |
Learning Activities
During completion of this module, the learning activities undertaken to
achieve the module learning outcomes are stated below:
| Student Learning Hours
(Normally totalling 200 hours):
(Note: Learning hours include both contact hours and hours spent on other learning activities) |
| Lecture/Core Content Delivery | 10 |
| Tutorial/Synchronous Support Activity | 5 |
| Laboratory/Practical Demonstration/Workshop | 20 |
| Independent Study | 65 |
| 100
Hours Total
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**Indicative Resources: (eg. Core text, journals, internet
access)
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The following materials form essential underpinning for the module content
and ultimately for the learning outcomes:
Joe Sremack. (2015) Big Data Forensics – Learning Hadoop Investigations. Packt Publishing
Prakash, P.K.S and Rao, A.S.K. (2017) R Deep Learning Cookbook: Solve complex neural net problems with TensorFlow, H2O and MXNet. Packt Publishing
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(**N.B. Although reading lists should include current publications,
students are advised (particularly for material marked with an asterisk*) to
wait until the start of session for confirmation of the most up-to-date
material)
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| Engagement Requirements |
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In line with the Academic Engagement Procedure, Students are defined as academically engaged if they are regularly engaged with timetabled teaching sessions, course-related learning resources including those in the Library and on the relevant learning platform, and complete assessments and submit these on time. Please refer to the Academic Engagement Procedure at the following link: Academic engagement procedure |
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Supplemental Information
| Programme Board | Computing |
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| Assessment Results (Pass/Fail) |
No
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| Subject Panel | Business & Applied Computing |
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| Moderator | Paul Keir |
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| External Examiner | TBC |
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| Accreditation Details | |
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| Version Number | 1.03 |
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| Assessment: (also refer to Assessment Outcomes Grids below) |
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| Coursework (100%) |
(N.B. (i) Assessment Outcomes Grids for the module
(one for each component) can be found below which clearly demonstrate how the learning outcomes of the module
will be assessed.
(ii) An indicative schedule listing approximate times
within the academic calendar when assessment is likely to feature will be
provided within the Student Handbook.)
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Assessment Outcome Grids (Footnote A.)
Footnotes
A. Referred to within Assessment Section above
B. Identified in the Learning Outcome Section above
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Note(s):
- More than one assessment method can be used to assess individual learning outcomes.
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Schools are responsible for determining student contact hours. Please refer to University Policy on contact hours (extract contained within section 10 of the Module Descriptor guidance note).
This will normally be variable across Schools, dependent on Programmes &/or Professional requirements.
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| Equality and Diversity |
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This module is suitable for any student. The assessment regime will be applied flexibly so that a student who can attain the practical outcomes of the module will not be disadvantaged. When a student discloses a disability, or if a tutor is concerned about a student, the tutor in consultation with the School Enabling Support co-ordinator will agree the appropriate adjustments to be made.
UWS Equality and Diversity Policy |
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(N.B. Every effort
will be made by the University to accommodate any equality and diversity issues
brought to the attention of the School)
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