CMP7206 Data Mining Level 7 Coursework Assignment Brief 2024-25 | BCU

Assessment Information

Assessment Summary (with type)

Prepare a presentation with your group in which you explain (with technical details) the usefulness of data mining for the application domain that you chose, common data mining techniques used in the literature for this application domain and the techniques that you are using to analyse a dataset of your choosing.

Each group member is expected to participate in the presentation and to answer questions about the work. Students are marked individually according to their participation and their answers to the questions.

Assessment Title: Assessment 1.1

Things to include: See details below in this document.

Completion of this Assessment will address the following Learning Outcomes:

• Critically review recent trends in data mining literature

Submission Information

Present any written aspects of the assessment using font size 11 and using 1.5 spacing to allow for comments and annotations to be added by the markers.

Complete the appropriate cover sheet for this assessment and append your work.

This assessment will be marked anonymously and should show your student number only. Submit this coursework assessment task via Moodle.

Late Submission

Assessments must be submitted in the format specified in the assessment task, by the deadline and to the submission point published on Moodle. Failure to submit by the published deadline will result in penalties, which are set out in Section 6 of the Academic Regulations.

Word Count

The maximum word count for this module assessment is shown on Page 1. A +10% margin of tolerance is applied, beyond which nothing further will be marked. Marks cannot be awarded for any learning outcomes addressed outside the word count.

The word count refers to everything in the main body of the text (including headings, tables, citations, quotes, lists, etc.). Everything before (i.e. abstract, acknowledgements, contents, executive summaries, etc.) and after (i.e. references, bibliographies, appendices, etc) is not included in the word count limit.

Referencing Style 

• BCU Harvard

Use of Artificial Intelligence

Whilst AI tools can help assist learning, when it comes to assessment, the Academic Misconduct Procedure is clear that this should be a student’s original work and not the work of other people or AI tools.

The Use of AI Tools – Student Guidelines document follows the same guidelines your lecturers use. If you are unsure of whether AI is appropriate within your work, please read the guidelines or ask your lecturer. For advice and guidance around academic writing, please visit the Centre for Academic Success.

Academic Integrity Guidance

Academic integrity is the attitude of approaching your academic work honestly by completing and submitting your original work, attributing and acknowledging your sources when necessary. Understanding good academic practice in written and oral work is a key element of academic integrity. It is a positive aspect of joining an academic community, showing familiarity with and acknowledging sources of evidence. The skills you require at higher education may differ from those learned elsewhere, such as school or college.

You will be required to follow specific academic conventions, which include acknowledging the work of others through appropriate referencing and citation as explicitly as possible. If you include ideas or quotations that have not been appropriately acknowledged, this may be seen as plagiarism, which is a form of academic misconduct. If you require support around referencing, please contact the Centre for Academic Success

It is important to recognise that seeking out learning about academic integrity will help reduce the risk of misconduct in your work. Skills such as paraphrasing, referencing and citation are integral to acting with integrity, and you can develop and advance these key academic skills through the Centre for Academic Success (CAS).

To learn more about academic integrity and its importance at university, you can access CAS resources on Moodle. Furthermore, you can book onto workshops and request 1-2-1 support around key academic skills.

Academic Misconduct 

Academic misconduct is conduct that has or may have the effect of providing you with an unfair advantage by relying on dishonest means to gain an advantage and which therefore compromises your academic integrity.

The Academic Misconduct procedure sets out the process we will follow and the penalties we may apply in cases where we believe you may have compromised your academic integrity by committing academic misconduct.

CMP7206  Task

The students are required to work in groups to prepare a presentation about one useful data mining application in a chosen domain.

Style

Presentation slides

Rationale

The first assessment is research-oriented. It helps the student to understand the recent trends and applications of Data Mining in practical domains. The students are required to work in groups to prepare a presentation about one useful data mining application in a chosen domain. (Assessment weighting is 30%).

CMP7206 Description

The following steps will be taken for assessment 1:

The first assessment is a presentation in which each group is required to prepare a presentation covering:

• What is the usefulness of the data mining application that they chose (e.g. Business, Finance, Education, Healthcare, Medicine, Manufacturing)
• What are the common data mining techniques found in the literature for the application domain?
• What is the motivation or the need for the DM application within the chosen domain?
• What are the goals of the DM application, and how does it address the domain problem?
• What is the impact of this application on the domain?
• Who is using the DM application?
• The nature of the data being used in this application, and how has it been collected?
• What types of DM techniques (s) have been exploited in the chosen application?
• What DM techniques that the team will be using to study your dataset and why?
• What are the challenges of using this application? How can it be improved?

Each group gets 20 minutes for their presentation

• A Question/Answer session for up to 10 minutes follows the presentation
• Each student supports a logical and coherent flow of the group presentation.
• Individual marks will be assigned to each student

Group formation and selected domains/applications should be finalised with the consent of the module tutor(s) by teaching week 3 of the semester. The presentations will be held in class on Friday, 1st of August 2025. The slide set specific to the student should be marked with the student's ID and name.

Grades will be broken down as follows:

• Quality of presentation and clarity of the slides (20%)
• The importance of DM application (10%)
• Technical details covered (10%)
• Presentation provides answers to the assessment questions (30%)
•  Participation in Q and session (30%)

Additional information

Examples of Data mining applications can be found in,
Examples of Data Mining
Data Mining Applications & Trends

For advice on writing style, referencing and academic skills, please make use of the Centre for Academic Success: Centre for Academic Success - student support | Birmingham City University (bcu.ac.uk)

Transferable skills:

• Problem solving
• Programming skills
• Analytical skills
• Teamwork
• Time management
• Project management
• Verbal and written communication skills

CMP7206 Marking Criteria:

Table of Assessment Criteria and Associated Grading Criteria

Submission Details:

Format: PowerPoint or PDF slides

Regulations:

• The minimum pass mark for a module is 50%
• Re-sit marks are capped at 50%

Full academic regulations are available for download using the link provided above in the IMPORTANT STATEMENTS section

Late Penalties

If you submit an assessment late at the first attempt, then you will be subject to one of the following penalties:

• If the submission is made between 1 and 24 hours after the published deadline, the original mark awarded will be reduced by 5%. For example, a mark of 60% will be reduced by 3%, so that the mark that the student will receive is 57%.
• If the submission is made between 24 hours and one week (5 working days) after the published deadline, the original mark awarded will be reduced by 10%. For example, a mark of 60% will be reduced by 6%, so that the mark the student will receive is 54%.
• If the submission is made after 5 days following the deadline, your work will be deemed a failure and returned to you unmarked.

The reduction in the mark will not be applied in the following two cases:

• The mark is below the pass mark for the assessment. In this case, the mark achieved by the student will stand
• where a deduction will reduce the mark from a pass to a fail. In this case, the mark awarded will be the threshold (i.e., 50%)

Please note:

If you submit a reassessment late then it will be deemed as a fail and returned to you unmarked.

Feedback:

Formative feedback will be provided throughout the semester, ensuring the students are on track, and allowing students to take corrective actions through formative evaluation. Summative feedback through detailed written reports against the two tasks will be provided on Moodle.

Marks and Feedback on your work will normally be provided within 20 working days of its submission deadline.

Where to get help

Students can seek reasonable feedback by contacting the module tutors via email or MS Teams.

Students can get additional support from the library support for searching for information and finding academic sources. 

The Centre for Academic Success offers 1:1 advice and feedback on academic writing, referencing, study skills and maths/statistics/computing. 

Fit to Submit:

Are you ready to submit your assignment? Review this assignment brief and consider whether you have met the criteria. Use any checklists provided to ensure that you have done everything needed.