![CSC 40054 Coursework Brief Template 2024 – 2025 – FINAL[1]](https://wp.fifu.app/assignmentnepal.com/aHR0cHM6Ly9kcml2ZS5nb29nbGUuY29tL3VjP2lkPTFqbUZ0N0d0eFpnbGxKLThjT09LaC1RZk55ZjcxbnpKXw/58458608d59b/csc-40054-coursework-brief-template-2024-2025-final1.webp?w=0&h=0&c=0&p=835 "CSC 40054 Coursework Brief Template 2024 – 2025 – FINAL[1]")
Assessment Brief
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Module Title |
Databases and Data analytics |
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Module Code |
CSC-40054 |
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Assessment Type |
Exercise |
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Assessment Title |
Coursework |
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Weighting (% of module mark) |
50% |
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Assessment Length (word count or equivalent) |
3000 words |
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Submission Deadline (date and time) |
13 March 2025 |
1 pm |
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Format of Submission |
Your submission should be in the form of a single zipped file. This zipped file should be named with your Student Number. The zipped file should contain your report in two formats MS Word and PDF. So, it should contain ONLY TWO files. Any other submitted file will not be marked. |
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Feedback Release Date [please ensure that this aligns with the requirements of Section 8 of the Assessment and Feedback Code of Practice. |
03/04/2025 |
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Staff contact details |
k.mistry@keele.ac.uk to arrange a meeting. |
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Assessment Details:
One report should be submitted that covers the following two tasks:
- Part I: Database management
- Part II: Data analytics
A report (maximum 3000 words) on the accessing, storage, manipulation and analysis of data available from an internet-based data repository. The code needs to be submitted as an appendix. The appendix does not count for the word count.
For each task, you should include the screenshot of the output after you run the code (when its execution finishes successfully). Failing to include the output screenshot will cause you to lose marks.
The codes should be included as the appendix at the end of the report. You should clearly specify which code is related to which task. Otherwise, they will be marked zero.
Screenshots of codes are not accepted in the appendix and will score zero. You should include the original code with the correct indentation so anyone can copy and paste the python code into an editor and run it.
If you have written the code, but it doesn’t run correctly, mention it in your report.
An example of the accepted appendix is shown at the end of the coursework.
Module Learning Outcomes:
In this assessment the following module learning outcomes will be assessed:
ILO1: Evaluate machine learning methods in the context of statistical analysis of data representing social or natural systems. Develop advanced applications of statistical data analytics techniques using an advanced specialist programming language (e.g. Python). Assess the options of storing, managing and manipulating very large volumes of data in the context of research or business organisations.
ILO2: Evaluate available data and determine how best to analyse the information available to provide required outcomes. Assess a range of statistical approaches and apply the correct statistical approaches to extract information from a set of data typically available in a modern business or research organisation:
Assessment Criteria:
Each question includes a breakdown of marks. Both questions will be marked out of 100%. The mean of the two marks determines your assessment mark.
Feedback to Students:
Feedback will be provided alongside marks three weeks after the original deadline.
Inclusive Practice:
This assessment has been adapted to be inclusive by means of permitting usage of AI tools to help with grammar when writing. You are reminded that should you require additional support; student services can assist with these matters.
Use of Artificial Intelligence (AI):
AI can be used as a research tool to identify key themes within the literature, or to check spelling and grammar and improve sentence structure. However, the use of generative artificial intelligence (AI), such as Google Gemini/Microsoft Bing/Microsoft Copilot/Open AI/ChatGPT (any version) to complete this assessment is strictly prohibited.
Academic Misconduct:
Academic misconduct is doing something that could give you an unfair advantage in an assessment. It includes, but is not limited to, the following: plagiarism; collusion; contract cheating; cheating in an examination; falsification of data or sources; falsification of official documents or signatures. The University treats academic misconduct very seriously and penalties will be given for proven cases, including termination of studies in serious cases. It is therefore very important that you understand how to prepare and take assessments honestly. In order to assist you with this there are various resources and help available both as part of your programme of study and also centrally. For more information please visit: https://www.keele.ac.uk/students/academiclife/appeals-complaints-conduct/studentacademicconduct/
Academic Skills Support:
The Academic and Digital Skills team provide a range of additional online resources (e.g., study guides, Sways, Podcasts, workshops etc) to help you with your academic work and assessments. You can find more information here.
