![𝐖𝐡𝐲 𝐜𝐡𝐨𝐨𝐬𝐞 𝐮𝐬 𝐟𝐨𝐫 𝐅𝐢𝐧𝐚𝐧𝐜𝐞?
✅ 𝐅𝐢𝐧𝐚𝐧𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: हामी तपाईंको वित्तीय विश्लेषणलाई सही दिशा दिन्छौं—बजेट, नगद प्रवाह, र जोखिम मूल्याङ्कनमा विशेषज्ञता।
✅ 𝐁𝐮𝐝𝐠𝐞𝐭 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠: तपाईंको वित्तीय लक्ष्यहरू हासिल गर्नका लागि ठोस र सटीक बजेट बनाउने।
✅ 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠: सही, पारदर्शी, र समयमै वित्तीय रिपोर्टिङ्ग प्रणालीमा मद्धत गर्दछौं।
✅ 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐧𝐜𝐲: हामी तपाईंलाई सही निवेश रणनीतिहरू छनोट गर्न र मुनाफा अधिकतम गर्न सहयोग गर्छौं।
✅ 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐓𝐡𝐞𝐨𝐫𝐲 𝐚𝐧𝐝 𝐏𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐠𝐲: वित्तीय सिद्धान्त र व्यावहारिक तरिकामा बलियो पकड़ राख्छौं।
✅ 𝐄𝐱𝐩𝐞𝐫𝐭 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐧𝐜𝐲 𝐟𝐨𝐫 𝐁𝐢𝐧𝐝𝐢𝐧𝐠 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: हामी तपाईंको सबै वित्तीय समाधानको लागि पेशेवर मार्गदर्शन र सहायता प्रदान गर्छौं।
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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.
