Brief Coursework 1 – Presentation (Practical) [25%]

Submission Deadline: 3rd November 2025

Feedback date: 1st Dec 2025

Module Title: Intelligence Engineering and Infrastructure

Module Code: COM774 (79166)

Semester (s) Taught: One

Course / Year Group:  MSc CS/AI/AI(CUQ)/7

Coursework / Exam Weighting: 25% (of Total coursework)

This module is assessed by two pieces i.e. coursework CW1 and CW2.

Coursework 1 is explained in the document as follows:

Coursework 1 is a practical assignment presented in the form of a video presentation. It focuses on the selection and pre-processing of a dataset and is designed to assess students’ fundamental understanding of data. The primary objective is to identify and prepare a suitable dataset that can be used to address a DevOps-related problem. This dataset will serve as the foundation for Coursework 2, in which a machine learning (ML) solution will be developed. At this stage, students are required to justify the suitability of their chosen dataset and demonstrate any necessary data cleaning or pre-processing steps. The deliverable for this assignment is a 7.5-minute video presentation. Coursework 1 accounts for 25% of the overall module mark. Feedback from this test feeds-forward into future exercises.

The MLOps process is generally structured into seven key stages: beginning with the Data Pipeline, followed by Model Development, CI/CD, Deployment, Monitoring, Retraining, and finally, Governance. The Data Pipeline is a series of processes that move data from one system to another, applying necessary transformations along the way. In MLOps and data engineering, it acts as the backbone of the entire workflow, ensuring that raw data is consistently collected, cleaned, and prepared so it can be effectively used by machine learning models or analytics. In this coursework (CW1), our focus will be on this first stage “the Data Pipeline” as it provides the foundation upon which all the other steps are built while the remaining stages will be assessed in Coursework CW2.”

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.

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

Related Learning Outcomes:

1. Appraise the concepts behind a range of machine learning operations paradigms and critically evaluate when to apply these paradigms to intelligence engineering problems.
2. Autonomously and independently evaluate deficiencies when interacting with a range of technologies and leveraging knowledge of these deficiencies to improve future practice.

Requirements:

You are required to:

1. Identify a suitable dataset

• Select a dataset that can be applied to a DevOps-related problem (e.g., CI/CD pipeline failures, incident management, monitoring, or code quality).
• Ensure the dataset is realistic, relevant, and feasible for further analysis in Coursework 2.
• If it is difficult to obtain a dirty dataset, generate anomalies within a clean dataset (using a synthetic dataset) to simulate one.

1. Justify dataset selection

• Provide clear reasoning for why the dataset is appropriate.
  • Explain its relevance to DevOps and its potential for solving a real-world problem.

1. Clean and pre-process the dataset

• Demonstrate data preparation steps such as handling missing values, removing duplicates, encoding categorical data, normalization/scaling, and feature engineering where appropriate.
• Explain the purpose and impact of each step.

1. Prepare and deliver a 7.5-minute video presentation

• Present your findings and process in a clear, logical, and professional manner.
• Use visuals (e.g., slides, data exploration outputs) to support your explanation.
• Structure your presentation with an introduction, dataset overview, justification, cleaning process, and conclusion.

Assessment Criteria:

Your submission will be assessed against the following criteria:

1. Dataset Identification & Relevance (10%)

• Ability to select a dataset that is clearly linked to a DevOps-related problem.
• Relevance, appropriateness, and feasibility of the dataset for further analysis in Coursework 2.

1. Justification of Dataset Selection (15%)

• Quality of reasoning for why the dataset is suitable.
• Explanation of its usefulness, practical value, and potential for solving a DevOps problem.

1. Data Cleaning & Preprocessing (50%)

• Evidence of appropriate data preparation steps.
• Handling of missing values, duplicates, data types, categorical encoding, normalization/scaling, and (where relevant) feature engineering.
• Clarity of explanation and rationale for each step.

1. Presentation Quality & Structure (15%)

• Organization and logical flow of the video presentation.
• Clarity of structure (introduction, dataset overview, justification, cleaning process, conclusion).
• Time management (approx. 7.5 minutes).

1. Communication & Engagement (10%)

• Effectiveness of delivery, clarity of explanations, and ability to engage the audience.
• Appropriate use of visuals to support understanding.

Note: Total weighting: 100% (equivalent to 25% of overall module grade)

Submission Guidelines:

Prepare your presentation slides

• Create your presentation slides in PowerPoint format (.pptx).
• Ensure slides are clear, well-structured, and support the flow of your video presentation.

Record your video presentation

• Record a 7.5-minute video presentation incorporating your PowerPoint slides.
• The video should clearly explain your dataset selection, justification, cleaning, and pre-processing steps.
• Ensure both audio narration and visuals (slides, code examples, or demonstrations) are clear and professional.

Combine slides and video

• Save your presentation as a single file that includes both your video recording and slides.
• Check playback to ensure that audio, visuals, and timing work correctly.

Final checks

• Verify that your submission is complete, professional, and within the time limit.
• Ensure your name, student ID, and module code are included on the first slide.
• Confirm that the file format is accepted (PowerPoint with embedded video).

Upload to Blackboard

• Log in to Blackboard and navigate to the Assessment 1 submission folder.
• Upload your final presentation file before the submission deadline.
• Double-check that the correct version has been uploaded.

Plagiarism and academic integrity

• Ensure your submission complies with the University’s plagiarism policy.
• Any use of external sources, datasets, or code must be appropriately referenced.

Note: According to Ulster University Assessment Code of Practice, where submitted work exceeds the agreed assessment limit, a margin of up to +10% of the work limit will be allowed without any penalty of mark deduction. If the work submitted is significantly in excess of the specified limit (+10%), there is no expectation that staff will assess the piece beyond the limit or provide feedback on work beyond this point. Markers will indicate the point at which the limit is reached and where they have stopped marking. A mark will be awarded only for the content submitted up to this point. No additional deduction or penalty will be applied to the overall mark awarded. The student is self-penalising as work will not be considered/marked.

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

N.B. The students are required to implement this solution using the concepts and techniques which were the focus of the teaching materials in this module. This may broadly have a focus on a hosted/cloud native design or a containerised solution. It is recommended that students appraise both these design approaches in their slides.

References

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

[2] Academic Integrity and Plagiarism

(https://www.ulster.ac.uk/student/exams/cheating-and-plagiarism)

[3] https://ulster.sharepoint.com.mcas.ms/sites/AcademicIntegrity