CSM6720 Advanced Data Analytics Assignment 2025-26 | Aberystwyth University

You can see the related solution of this brief: Computer Science Assignment Example

CSM6720 Advanced Data Analytics Assignment 2025-26

Overview

This is the 2025-2026 assignment for CSM6720, and comprises 50% of the total mark for this module. The remaining 50% is assessed with a two-hour written examination.

This assignment is handed out on Monday, 2nd March 2026. The hand-in deadline is 13:00 (BST) Tuesday, 5th May 2026, to TurnItIn via the CSM6720 Assessment and Feedback folder on Blackboard. Feedback is due on Wednesday, 27th May 2026.

The learning outcomes that are examined by this assignment are:

• Design and implement a NoSQL database with one or more data models.
• Design, perform, visualise and report exploration and analysis of a dataset. 

Specifically, this assignment requires you to:

• Create a MongoDB database using data contained within several CSV files.
• Create several queries to extract information from your MongoDB database.
• Visualise the results of your queries.
• Write a report summarising your findings and outlining the technical and scientific justifications behind your design choices.

Each of these components is worth 25% of the overall assignment.

The word count for this assignment is 4000 words ±10%. Include your word count at the beginning of the assignment. Work exceeding the word count by more than 10% will be penalised by a deduction of 5 marks for every 500 words over the limit. There is no penalty for being below the word limit. The word count includes everything except for appendices and the reference list. Please note that text in figures, headings and tables is all included in the word count. If you use footnotes, any textual comments apart from citations in the footnotes are also included in the word count.

Introduction

This assignment is based on the dataset: LEGO Database, a LEGO Parts/Sets/Colours and Inventories of every official LEGO set. The dataset comprises eight CSV files and is available for download on the Assessments and Feedback folder on the CSM6720 Blackboard page.

Tasks, their Mark Weighting and the Learning Outcome Assessed

Task 1: MongoDB Database (25%)(Learning Outcome 1)

This task requires you to understand the LEGO Database CSV files, and import the data contained within the files, into a MongoDB database.

In this section you should summarise the data set. Include:

• A textual description of each of the CSV files
• The number of records in each file
• A textual description of each column in each file, including the type of data contained.
• A diagram showing the structure of the complete database
• If there are any issues in the records (missing data, etc.), a description of these issues

Design and implement a process for importing the data into a MongoDB database. You should include a process diagram depicting how you created your MongoDB. Your process diagram should contain enough information for someone else to reproduce your work. This should also include any steps that you took to verify and clean the data. Include screenshots of MongoDB to support your communication of the process followed.

Use MongoDB Compass to import the LEGO data set and create your database. As a starting point, each CSV file should be imported as a separate collection.

If you write any MongoDB code to achieve this task, add this to the appendix and reference this code appropriately in the main body of your report.

Task 2: MongoDB Queries (25%)(Learning Outcome 2)

This task requires you to design and implement queries to answer the following five different questions:

• What is the total number of LEGO sets per year across the data set?
• What are the top five most popular bricks across all the LEGO sets in the data set?
• What are the top three most popular bricks per LEGO theme across all the LEGO sets in the data set for the year 1983?
• What is the average number of “Brick 2 x 2” LEGO bricks per set per year across all the LEGO sets in the data set?
• What are the percentages of colour for all the bricks across all the LEGO sets in the data set?
• Optional: if you are aiming for a mark in the 80% range, in addition to the previous five queries, you can design and answer your own questions. This optional work should be self-guided and demonstrate your ability to develop solutions to complex questions.

You must use MongoDB queries to answer these questions. Your queries must be runnable by your marker. Append your queries in the appendix, and reference this code appropriately in the main body of your report.

As a starting point, you will need to be able to write queries that combine data from across multiple collections. We’ve not covered this in the workshops, and you are required to do further reading to understand how this is achieved.

You are being assessed on your ability to write queries in response to questions about the data. Your report should focus on describing how and why your queries work. You could also include any considerations that you have made to optimise the performance of your queries.

Task 3: Data Visualisation (25%)(Learning Outcome 2)

This task requires you to select an appropriate visualisation method for each of the queries written in Task 2. You can use any technology to visualise your data, including Microsoft Excel, Python, JavaScript, etc. However, you must visualise the results generated by your MongoDB queries.

Include each of your visualisations within the main body of your report and cross-reference appropriately.

Include either the code used to generate your visualisations or a process diagram depicting how the visualisations were made in the appendix.

In this section, you should underpin the design choices that you have made using appropriate resources. These design choices should include elements like your choice of visualisation type, and the choice of colours, fonts, labels, etc. used. Appropriate resources could include ISO Standards, scientific papers, and technical guides.

Task 4: Report (25%) (Learning Outcomes 1 and 2)

This task requires you to present your work in a 4000 word ±10% written report. The report should be well written, readable, referenced appropriately, and appropriately structured (divided into appropriate sections, and including an introduction and conclusion).

Your report should outline the decisions you have made in the development of your solutions to Tasks 1, 2, and 3. Those decisions should be underpinned by technical best practices and scientific theory. Those technical best practices and scientific theories should be referenced appropriately.

At the master's level, we are not just assessing your ability to write code, but also that you can assimilate best practices and scientific theories to underpin the choices that you make. Your report must demonstrate this to be awarded a pass mark or higher.

Assessment Criteria

Your assignment will be assessed according to the department’s assessment criteria for essays (see Student Handbook Appendix AC) and marked based on the report you submit.

Your work will be assessed based on the following criteria:

• The breadth and depth of relevant knowledge and research that you demonstrate. Simply presenting a working solution will not be enough to gain a pass mark. You are expected to consult other sources and use those sources to underpin the design and implementation choices that you make.
• The level of understanding shown, your personal reflection thereof
• The structure of your answers and the logical coherence that they display
• The general quality of your writing

Marks will be awarded using the following marking scheme:

• Task 1: MongoDB Database 25%
• Task 2: MongoDB Queries 25%
• Task 3: Data Visualisation 25%
• Task 4: Report: 25%

The marking schema for the report is as follows:

Distinction(70+%): The submitted work is conducted at an expert level. Data exploration is conducted to an expert level and fully documented in the report. The data is thoroughly examined and issues dealt with in a manner that is logical, robust, and documented with excellent reasoning. Requested visualisations are complete, based on demonstrably reliable queries on appropriately organised data. Issues of reliability are appropriately addressed either through data cleaning (where appropriate) or through documentation in the report; in either case, there is an excellent discussion of the issues in the report.

To be awarded above 79%, you must include additional queries that demonstrate an advanced understanding of the dataset and MongoDB by completing point 6 in Task 2, including visualising the results appropriately in Task 3.

Merit (60-69%): The submitted work is conducted to a high level with few mistakes. Data exploration is conducted to a high level and is well documented in the report. The data is properly examined and issues dealt with in a manner that is logical, robust, and documented with good reasoning. Requested visualisations are complete, based on reliable queries on appropriately organised data. Issues of reliability are appropriately addressed either through data cleaning (where appropriate) or through documentation in the report, in either case, there is an appropriate discussion of the issues in the report.

Pass (50-59%): The submission is a working solution that meets the technical requirements outlined in this assignment brief. The raw data is examined and issues dealt with in a manner that is mostly logical, robust, and documented with reasoning. Requested visualisations are largely complete with a few omissions or errors. Requested visualisation based on queries that generally capture the requested information. Issues of reliability are addressed to some extent either through data cleaning (where appropriate) or through documentation in the report, in either case, there is some discussion of the issues in the report.

Fail (<50%): The submitted work fails to satisfy the criteria above.

Plagiarism and Unfair Practice

Note: this is an individual assignment and must be completed as a one-person effort by the student submitting the work.

Written assignments MUST be in your own words. Do NOT take shortcuts by copying even the smallest amount of material from anywhere (including the internet, books or another student’s text). This is plagiarism and will be detected. N.B. Presenting work generated by AI as if it were your own is Unacceptable Academic Practice, and you must not do this. AU procedures and penalties for 'Unacceptable Academic Practice' are strict and some may prevent you from qualifying for your degree see: https://www.aber.ac.uk/en/academic-registry/handbook/regulations/uap/

You can read more about Academic integrity using the following links:

• Student guide to Referencing and Plagiarism
• Regulation on Unacceptable Academic Practice
• Guidance on Using Generative AI

Your submission of work, by the method as described below, will be deemed to imply a declaration by you that the report is your own work.

Submission

Your submission must be a single pdf format file which must be less than 10 MB in size. We require you to submit your 4000 word ±10% report electronically via the TurnItIn submission portal in the CSM6720 Assessment and Feedback folder on Blackboard.

The submission deadline for this assignment is before 13:00 (BST) Tuesday, 5th May 2026.