Understand the scope of Data Science and its key concepts, from academic, scientific and industrial point of view.
COMP1857 Introduction to Data Science
RESIT Coursework (Research Article) Specification
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COMP1857 (2024/2025) |
Introduction to Data Science |
Contribution: 100% of course |
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Module Leaders: |
Coursework: Research Article |
Deadline Date: 11/07/2025 @ 23:30 UK time |
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This coursework should take an average student approximately 50 hours |
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Learning outcomes: 1. Understand the scope of Data Science and its key concepts, from academic, scientific and industrial point of view. 2. Identify appropriate academic and scientific resources and utilise them for producing research articles on selected Data Science topics by employing an appropriate formal academic writing style. 3. Understand a wide range of Data Science topics and how they influence different aspects of science and technology by familiarising with key academic and scientific publications. |
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Plagiarism is presenting somebody else`s work as your own. It includes: copying information directly from the Web or books without referencing the material; submitting joint coursework as an individual effort; copying another student`s coursework; stealing coursework from another student and submitting it as your own work. Suspected plagiarism will be investigated and if found to have occurred will be dealt with according to the procedures set down by the University. Please see your
Student handbook for further details of what is / isn`t plagiarism.
All material copied or amended from any source (e.g. internet, books) must be referenced correctly according to the reference styleyou are using. Your work will be submitted for plagiarism checking. Any attempt to bypass our plagiarism detection systems will be treated as a severe Assessment Offence.
Coursework Submission Requirements
- An electronic copy of your work for this coursework must be fully uploaded by 23:30 UK time on 11/07/2025.
- For this coursework you must submit a PDF document containing your report and a screencast explaining the research article content and the methodology used to collect data/info and conduct the research. In general, any text in the document must not be an image (i.e.must not be scanned) and would be generated from Microsoft Word Online ("Save As ... PDF").
- Make sure that any files you upload are virus-free and not protected by a password or corrupted otherwise they will be treated as null submissions.
- Feedback on your work will be available from Moodle. The grade will be made available in the portal.
- All coursework must be submitted as above. Under no circumstances can they be accepted by academic staff.
• Failure to submit a screencast explaining the research article content and the methodology used to collect data/info and conduct the research will result in a failed assessment (zero)
Important: The following requirements must be met. Failure to comply will result in a failed assessment (zero):
1. Screencast Submission:
- You must submit a screencast with clear audio commentary explaining the research article content and the methodology used to collect data/information and conduct the research.
- You must write your research article exclusively using only Microsoft Word Online and share the document with your tutors, granting `Can Review` access.
2. Microsoft Word Online Usage:
These requirements are mandatory and failure to meet them will result in a failure (zero) for the coursework assessment.
The University website has details of the current Coursework Regulations, including details of penalties for late submission, procedures for Extenuating Circumstances and penalties for Assessment Offences.
Coursework Regulations
- If you have been granted Extenuating Circumstances (ECs), you may submit your coursework up to 10 working days after the published deadline without penalty. However, this is subject to your claim being accepted by the Faculty Extenuating Circumstances Panel.
- Late submissions will be dealt with in accordance with University Regulations.
- Coursework submitted more than two weeks late may be given feedback but will be recorded as a non-submission regardless of any extenuating circumstances. The only exception to this is where your EC claim outcome has granted you a deferral.
- Do not ask lecturers for extensions to published deadlines - they are not authorised to award an extension.
Please refer to the University Portal for further detail regarding the University Academic Regulations concerning Extenuating Circumstances claims.
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Coursework Specification:
- Select a Data Science related topic.
- Write a Research Article on the selected topic, which will be the title of the Research Article.
The article is expected to meet the following criteria:
- The Research Article is individual work conducted independently by the student.
- The Research Article is expected to be 2000 words, excluding references.
- The article clearly identifies a specific Data Science or Data Science related topic.
- The article should be written in academic language and should refer to the academic literature in the specific field which is relevant to the topic.
- The article should use referencing and in-text citation using Harvard referencing style.
- The article should include an introduction to the topic, a thorough literature review, well- presented main discussions or arguments, a set of relevant conclusions, and list of references.
- You must select a specific topic within the field of Data Science rather than a broad and general area.
- Clearly define the research problemaimquestion in the introduction of your article.
Topic selection:
Examples:
- Too Broad: "Machine Learning"
Specific: "The Role of Transfer Learning in Reducing Computational Costs for NLP Models"
- Too Broad: "Big Data Analytics"
Specific: "Optimising Real-Time Fraud Detection in Banking Using Big Data Tools"
- Too Broad: "Data Visualisation"
Specific: "Effective Dashboard Design for Monitoring COVID-19 Vaccination Campaigns"
Indicative list of general Data Science areas (topics can be chosen from other Data Science related areas as well):
- Machine Learning
- Artificial Intelligence
- Data Mining
- Big Data Analytics
- Predictive Analytics
- Natural Language Processing
- Computer Vision
- Image Processing
- Time Series Analysis
- Social Network Analysis
- Recommender Systems
- Data Visualisation
- Sentiment Analysis
- Web Scraping and Text Mining
- Healthcare Data Analytics
- Financial Data Analysis
- Business Intelligence
- Ethical and Legal Issues in Data Science
You can select any of these areas or choose another Data Science related area for your research article. Important: The following requirements must be met. Failure to comply will result in a failed assessment (zero):
3. Screencast Submission:
- You must submit a screencast with clear audio commentary explaining the research article content and the methodology used to collect data/information and conduct the research.
- You must write your research article exclusively using only Microsoft Word Online and share the document with your tutors, granting `Can Review` access.
4. Microsoft Word Online Usage:
These requirements are mandatory and failure to meet them will result in a failure (zero) for the coursework assessment
Deliverable:
A single PDF document of a research article with the following content:
- Title page
- Introduction to the topic
- Literature review
- Main discussions/arguments
- Conclusions
- References
B. A screencast explaining the research article content and the methodology used to collect data/info and conduct the research. Failure to submit a screencast explaining the research article content and the methodology used to collect data/info and conduct the research will result in a failed assessment.
COMP1857 Weighted Marking Scheme
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Assessment Criteria |
Marks available (%) |
Marks given |
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Relevance and specificity of the selected topic/problem/innovation |
10 |
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Accurate spelling, grammar and writing style |
10 |
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Use of suitable academic language |
10 |
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Clear structure of the article |
10 |
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An appropriate introduction with clearly defined research problem/aim/question |
10 |
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Focused and relevant literature review |
10 |
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Clearly stated main arguments and discussions |
10 |
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Clear and evidence-based conclusions |
10 |
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The article references relevant and recent books, articles and papers |
5 |
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Consistent use of Harvard referencing style |
5 |
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Screencast submission with clear audio commentary explaining content and methodology |
10 |
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Final Mark [%] |
100 |
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Marker name: |
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The marking rubric for coursework |
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0-29% Fail |
30-39% Fail |
40-49%Satisfactory |
50-59% Good |
60-69% Very Good |
70-79% Excellent |
80-100% Exceptional |
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D1: Knowledge and Specificity of Topic: Assess the depth of understanding and relevance of the selected topic in Data Science. |
Topic is irrelevant or lacks focus. Does not demonstrate understanding of Data Science concepts or fails to address a specific research aim. |
Topic is weakly defined with limited specificity. Demonstrates minimal understanding and lacks a clear focus. |
Topic is somewhat relevant and specific but lacks clarity. Limited exploration of specific aspects. |
Topic is relevant and reasonably specific. The research problem is adequately defined but lacks depth. |
Topic is specific, relevant, and demonstrates a clear understanding of the research aim. |
Topic is highly specific, relevant, and innovative. |
Topic is exceptionally specific, innovative, and highly relevant. Profound understanding of the research aim is demonstrated. |
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D2: Literature Review and Critical Analysis: Evaluate the student`s ability to critically review and analyse sources. |
Limited or no review of literature. Lacks references or understanding of key works. |
Weak and incomplete review. Minimal reference to academic works and lack of critical analysis. |
Adequate review with some relevant references. Limited critical engagement. |
Good review with relevant references. Demonstrates some critical analysis but lacks depth. |
Thorough literature review with clear engagement and critical analysis. |
Comprehensive and insightful review of relevant literature. |
Exceptional review with critical engagement, synthesis, and innovative connections between sources. |
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D3: Main Arguments and Discussions: Assesses the originality and evidence-based reasoning in arguments and discussions. |
Lacks clear arguments or discussions. Does not engage meaningfully. |
Weak arguments with limited depth. Superficial and unstructured discussions. |
Basic arguments and discussions with limited depth or clarity. |
Good arguments and discussions with reasonable depth. Some sections lack clarity or evidence. |
Strong, well-structured arguments and discussions. Evidence- based reasoning is demonstrated. |
Excellent arguments with clear evidence-based reasoning and strong critical thinking. |
Outstanding arguments. Highly original, evidence- based, and deeply analytical. |
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D4: Writing Style, Structure, and Presentation Evaluates coherence, organisation, and clarity of writing. |
Disorganised structure and unclear writing. Significant grammatical errors. |
Weak structure with limited coherence. Frequent errors in grammar. |
Basic structure and adequate writing. Some errors in grammar or tone. |
Good structure with mostly clear writing. Minor grammatical errors. |
Very good structure and clear, well-written content. Minimal errors. |
Excellent structure and writing style. Clear, concise, and engaging. |
Exceptional writing style, structure, and presentation. Highly professional and error- free. |
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D5: Conclusions and Evidence-Based Reasoning: Examines the quality and insight of the research conclusions. |
Lacks conclusions or evidence-based reasoning. |
Weak and unsupported conclusions. |
Basic conclusions somewhat supported by content. |
Clear and reasonably supported conclusions. |
Well-supported and insightful conclusions. |
Excellent conclusions demonstrating strong analytical skills. |
Outstanding, evidence- based conclusions. Highly analytical and insightful. |
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D6: Screencast Submission Assesses clarity, engagement, and explanation of content in the screencast. |
No screencast submitted or unclear presentation. |
Weak screencast with limited explanation. |
Basic screencast with minimal explanation. |
Good screencast with clear explanation but lacks depth. |
Very good screencast with clear, engaging explanations. |
Excellent screencast with in- depth explanation. |
Outstanding screencast with exceptional clarity and depth. |
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D7: Referencing Evaluates consistency and adherence to Harvard referencing style. |
Minimal or no references provided. Inconsistent style. |
Limited referencing with inaccuracies. |
Basic referencing with some inconsistencies. |
Good referencing with minor errors. |
Very good referencing with minimal errors. |
Excellent referencing with consistent style. |
Exceptional referencing. Flawless and well- integrated. |