IMPORTANT NOTE: THIS DOCUMENT IS STILL UNDER MODERATION PROCESS AND ALL SECTIONS ARE SUBJECT TO CHANGE.

Coursework Overview and Assessment Criteria

Module Title: Big Data & Infrastructure

Module Code: COM 745

Course / Year Group: MSc Internet of Things/Computer Science

Coursework / Exam Weighting: 100/0

Coursework Assessment Overview

This module is assessed by two pieces of coursework.

Coursework 1 consists of a single in class examination which will have a time limit of 60 minutes. Coursework 1 contributes to 25% of the overall mark for this module.

Coursework 2 is a practical skills assessment wherein students need to develop a solution and create a related presentation plus demonstrative video. Coursework 2 contributes to 75% of the overall mark for this module.

The university has a number of rules and regulations surrounding assessment, late submissions and illness. These are in the student guide [1] - ensure you read this and understand the impact of these rules and regulations.

These coursework assignments are detailed below.

Note: Students who submit coursework are declaring the following.

“I declare that this is all my own work. Any material I have referred to has been accurately referenced and any contribution of Artificial Intelligence technology has been fully acknowledged. I have read the University’s policy on academic misconduct and understand the different forms of academic misconduct. If it is shown that material has been falsified, plagiarised, or I have otherwise attempted to obtain an unfair advantage for myself or others, I understand that I may face sanctions in accordance with the policies and procedures of the University. A mark of zero may be awarded and the reason for that mark will be recorded on my file.”

Also note:

You will receive feedback as per University Guidance which is currently set at 20 working days after submission.

Coursework 1 – Practical Skills Assessment [25%]

Occurs:

Mid Semester [Week 4-7, exact day to be determined once timetabling is underway]

Feedback Date:

Within University guidelines, 20 working days after submission.

Related learning outcomes:

1. Demonstrate a comprehensive understanding of what is meant by big data and how a variety of database/data storage paradigms may be applied to address the challenges it presents.

During the delivery course of the module, students will be expected to complete a 60- minute, online test. This test will assess understanding of concepts which have been introduced and detailed until that point.

This exam will be set in the in the middle of the semester and will incorporate the following topics:

• General Database Concepts
• Relational Databases
• NoSQL Concepts
• Document Databases
• Timeseries Databases
• Graph Databases

Coursework 1 will be delivered, submitted and assessed through the Blackboard online learning environment.

This is a closed book examination and will occur ON CAMPUS.

N.B. unforeseen circumstances, such as inclement weather, may require rescheduling of this exam. In this eventuality, students and the course director will be consulted.

Coursework 2 – a set exercise [75%]

Released:

Week 4 - Semester 1 2024/25

Submission Deadline:

Week 11 – 8^th January 2025

Feedback Date:

Within 20 working days from submission, as per University Policy

Related Learning Outcomes:

1. Appraise the concepts behind a range of database/data storage paradigms and critically evaluate when to apply these paradigms to big data problems.
2. Autonomously and independently investigate deficiencies when interacting with a range of technologies and leveraging knowledge of these deficiencies to improve future practice.
3. Examine, select and autonomously apply skills to leverage data stored in a range of database/data storage paradigms.

The exercise will assess understanding of further concepts and demonstrate practical skills related to a data lake type environment, as taught within the module (such as Hadoop, Amazon EMR or Azure Data Lakes).

Students will be set an exercise where they will be expected to:

1. Identify and evaluate a number of publicly available datasets related to

educational attainment and crime rates.

These may be from open sources such as kaggle.com, data.gov.in, data.gov, data.stats.gov.cn, or data.gov.uk

1. Select appropriate datasets, as informed by their interests and the topic issued.
2. Integrate and import these datasets into a suitable data lake system, as covered within the module, while providing rationale for their choice.
3. Perform meaningful analysis of the data to derive some simple useful information, as can be obtained by the dataset selected.
4. Provide visualisation of the analysis through any data lake-associated technologies which the students deem suitable.

Once the solution is produced, students are required to produce presentation which incorporates a 5-minute [indicative] video capture demonstrating the solution (NOTE: The video is to demonstrate the solution. It is not to be a recording of the slides being presented).

Note: Students will submit a PowerPoint Slide deck containing an embedded video demonstrating the solution.

The presentation element, without video is worth 70% of this assessment and 52.5% of the overall module credit.

The video embedded in the presentation is worth 30% of this assessment and 22.5% of the overall module credit.

It is recommended to have 15 content slides which incorporates the below outline:

Slide 0. Title Slide. (0%)

Slides 1 - 3. Discussion of the problem and justification of the dataset (15%).

Slides 4 - 8. Overview of the technical solution developed (25%).

Slides 9 - 12. The analysis performed, and insight obtained (20%).

Slide 13. Functionality of the recorded demonstration (30%) [5-minute video].

Slide 14. Concluding comments (5%).

Slide 15. References (5%).

The assessment criteria for coursework 2 is presented as an appendix to this document.

N.B. Students should be aware of the plagiarism policy of the University and submit their coursework in accordance to this.

References

[1] “Ulster University Student Guide.” [Online]. Available: https://www.ulster.ac.uk/connect/guide.

[2] IEEE, “Manuscript Templates for Conference Proceedings.” [Online]. Available: https://www.ieee.org/conferences_events/conferences/publishing/templates.ht ml.

[3] IEEE, “IEEE Citation Reference.” [Online]. Available: https://www.ieee.org/documents/ieeecitationref.pdf.

[4] Mendeley Ltd, “Mendeley Citation Manager.” [Online]. Available: https://www.mendeley.com/.

Appendix I – assessment criteria coursework 2 COM745 – assessment criteria coursework 2