CP60056E, CP6CS56E, CP6HA50E, CP6UA56O, CP6SL50O, CP6GR56O Databases and Analytics Assignment 2026 | UWL

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Published: 23 Apr, 2026

Category	Assignment	Subject	Computer Science
University	University of West London	Module Title	CP60056E, CP6CS56E, CP6HA50E, CP6UA56O, CP6SL50O, CP6GR56O Databases and Analytics

Academic Year	2026

CP60056E, CP6CS56E, CP6HA50E, CP6UA56O, CP6SL50O, CP6GR56O Databases and Analytics Assignment

Structure of Assignment

This assignment has two elements. The learning outcomes of the module are assessed by the successful completion of the elements.

Element	Type	Weighting	Due Date
1	Coursework	80	Coursework (Due on: Week 13) 12 MAY 2026 & 23:59 PM
2	Demo Presentation (In-person demonstration)	20	Week 12 - Week 14 (Lecture and Seminar Lab Sessions), 12:00 PM to 3:00 PM

Learning Outcomes

Apply SQL in R analytics for writing efficient database queries.
Build NoSQL databases using Python within MongoDB.
Implement indexing and query optimisation strategies using Python within MongoDB.
Develop big data analytics applications using Python and R within MongoDB.

Based on the case study provided on Blackboard in the Assessment folder, students are required to carefully review and analyse the given scenario using critical thinking and analytical skills. Students must identify the existing problems, challenges, and inefficiencies within the current system and propose appropriate data-driven solutions. This involves analysing the dataset using different data analytics methods and applying suitable data visualisation techniques to gain meaningful insights and support decision-making. Furthermore, students are expected to design and develop a MongoDB database based on the case study requirements, ensuring proper data modelling, storage, and management. Students should also apply appropriate database queries, optimisation techniques, and analytical tools using SQL, R, and Python to process the data efficiently and present clear and justified results. The overall objective is to demonstrate the ability to integrate database technologies and analytics methods to solve real-world problems effectively.

This module is assessed through two summative components: a coursework assignment worth 80% of the total module mark and a demo presentation worth 20%.

Section	Criteria	Issues	Mark	Details
Coursework 1	Title			Coursework
	Task details			Tasks: The coursework will be based on a real-world case study, which will be released to students via Blackboard during the semester. Students are required to prepare their submission by applying the database and analytics techniques covered in the module, including SQL, R analytics, Python data processing, MongoDB development, and query optimisation strategies. The coursework is designed to assess students’ ability to analyse the case study, develop appropriate relational and NoSQL database solutions, and demonstrate effective query performance and optimisation skills. This assessment addresses the module learning outcomes LO1–LO3. Note: Your assignment must be submitted as a single Word or PDF report.
Marking Guide	Criteria	Issues	Mark	Marking breakdown where appropriate
	SQL in R	SQL syntax, filtering, optimisation, interpretation	15	Correct application of SQL queries within R, efficient data retrieval and manipulation, optimisation of queries, and clear interpretation of results.
	R analytics	Statistical analysis, data manipulation, visualisation	15	Appropriate use of R for data analysis, application of statistical methods, effective data visualisation, and clear interpretation of analytical outputs.
	Python data processing	Pandas, NumPy, data analysis, charts	20	Effective use of Python for data processing, analysis and visualisation, correct implementation of analytical methods, and clear presentation of results.
	MongoDB development	PyMongo, CRUD operations, NoSQL design	20	Design and implementation of MongoDB database, correct use of CRUD operations, appropriate NoSQL data modelling, and integration using Python.
	Query optimisation strategies	Indexing, explaining plans, performance tuning	10	Implementation of indexing, evaluation of query performance, application of optimisation techniques, and justification of optimisation decisions.

Section	Criteria	Issues	Mark	Details
Demo Presentation	Title			In-person demonstration
	Task details			Tasks: All students will complete an in-person demo presentation during Weeks 12-14. This component assesses students’ ability to develop and demonstrate a big data analytics application using Python and R within MongoDB, addressing Learning Outcome LO4. For the in-person demonstration, students are not required to prepare PowerPoint slides. Each student must attend the seminar lab at their scheduled time and answer questions during the session. Students will be asked to apply the correct instructions and write the appropriate syntax on the computer in real time, and demonstrate the correct output. During the semester, students have been introduced to various commands in R, Python, and MongoDB. Therefore, students are expected to be able to identify the correct commands for the given questions, enter them in the lab computer, and produce the correct results. Please note that the questions will be different for each student.
Marking Guide	Criteria	Issues	Mark	Marking breakdown where appropriate
	R in SQL	Integration of SQL and R analytics	6	Integration of SQL within R, correct execution of queries, and clear interpretation of outputs.
	Python	data processing, data manipulation, visualisation	6	Application of Python for data analysis, correct implementation, and demonstration of analytical results.
	MongoDB	Aggregation, scalable analytics, integration with Python	8	Development and use of the MongoDB database, integration with Python, and demonstration of analytics capabilities.

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