Filter by keyword, subject, or both. Updates live as new model answers are added to our portal.
Machine Learning on Big Data
3,000 words
Machine Learning on Big Data using PySpark
This group assessment for the Machine Learning on Big Data module requires students to apply machine learning techniques to a large real-world dataset using PySpark DataFrame and Spark ML. The coursework is designed to develop practical understanding of big data processing, machine learning implementation, model optimisation and evaluation. Students work in groups of three or four and complete a machine learning project using a suitable large dataset obtained from a benchmarking source such as Kaggle, their workplace or another valid resource. The recommended dataset size is between 300 MB and 1 GB. The main task requires students to select a dataset containing multiple classes rather than a binary classification problem and develop an appropriate machine learning solution. The project begins with loading and preprocessing the data using PySpark DataFrame. Students are expected to address issues such as missing values, data normalisation, feature engineering and class imbalance. For text datasets, additional preprocessing techniques such as stemming, lemmatisation and TF-IDF may be applied. Students must then select and implement an appropriate machine learning method using PySpark MLlib or the Spark ML package. The brief permits approaches including multiclass classification, clustering, ensemble learning and text mining. Model parameters must be optimised using techniques such as grid search or random search. Students are required to evaluate their trained models using suitable metrics, including accuracy, F1-score, precision, recall and classification matrices where appropriate. The brief encourages students to maintain model accuracy and robustness above 90% for the highest possible mark and to discuss steps taken to address bias and variance. The results must be visualised or printed clearly, with appropriate interpretation and analysis of the findings. Students must also consider Legal, Social, Ethical and Professional (LSEP) issues throughout the project. Each student selects one LSEP principle and discusses relevant concerns such as dataset bias, privacy or ethical implications, together with suitable mitigation strategies. The final report should be approximately 3,000 words with a tolerance of ±10% and submitted as a single HTML report using the template provided on the module Moodle site. The report should consolidate the individual contributions of all group members into one comprehensive and user-friendly analytics report. The main assessment is weighted 60% for the report and 40% for the group presentation. The presentation is conducted online through Microsoft Teams, and all group members must participate. The assessment evaluates understanding of Spark, preprocessing, modelling, optimisation, evaluation and the ability to explain and interpret the implemented solution.
Read Model Answer →
Cloud and Big Data Technologies
2,500 words
Cloud and Big Data Technologies – Summative Assessment: Cloud and Big Data Security Application
This summative assessment for the CST4067 Cloud and Big Data Technologies module requires students to design, implement and evaluate an individual cloud and big data security application. The assessment focuses on applying techniques for the complex transformation and processing of data within distributed and cloud-based environments, while considering security, privacy and access-control requirements. Students are expected to develop a practical application and document its design, implementation and evaluation through a technical report and a short demonstration video. Students are given flexibility to select their own project idea, provided that the proposed application is appropriately scoped for the available development period and demonstrates relevant cloud, big data and security technologies. Suggested project areas include data science applications using SQL, MongoDB and cloud storage, privacy-preserving data processing such as Differential Privacy, multi-party authentication, group-based security and access control, Multi-Level Security, Attribute-Based Encryption, and distributed or cloud-based applications incorporating security protocols such as SSH, SSL or IPSEC and access-control mechanisms such as RBAC. The Design and Implementation Document should be no longer than 2,500 words and should explain the major design and implementation aspects of the project. Expected content includes an introduction covering the aims, objectives, project concept, security concepts and cloud technologies used; a requirements specification addressing programme behaviour and security requirements; analysis and design covering protocols, access control and interaction, sequence diagrams or process specifications; implementation details explaining what was achieved and how it was developed; and an evaluation and conclusion discussing successful and unsuccessful aspects, problems encountered and lessons learned. Relevant references, including tutorials, books and academic articles, should also be provided using Harvard or IEEE referencing. The assessment also requires students to submit the implemented Cloud and Big Data Security application together with a highlight demonstration video. The video must be no longer than seven minutes and should demonstrate the main features of the application, including relevant interactions, implementation highlights, security features and, where appropriate, attack scenarios. Assessment is based on the Design and Implementation Document, originality, advanced features, and the effort and quality demonstrated in the application. The assessment specification places particular importance on original development, clear documentation of any tutorials or existing resources used, and evidence that the student understands the technologies implemented. Suggested technologies and project ideas include Google Cloud, Hadoop, Spark, cloud storage, data pipelines, security protocols, access control and privacy-preserving techniques.
Read Model Answer →
Big Data and Cloud Computing
2,500 words
Big Data and Cloud Computing: FieldVision Cloud-Based Big Data Solution for AgroNova
This assessment is a 2,500-word individual report for the Big Data and Cloud Computing module within the MSc Management with Data Analytics programme at BPP University. The report is based on a fictional agricultural technology company, AgroNova, and its FieldVision project. AgroNova provides climate change and crop monitoring services to farmers in the UK and intends to expand internationally. However, its existing ageing infrastructure and manual processes present challenges to international expansion and the development of data-driven decision-making. The FieldVision project aims to use Internet of Things (IoT) technology and cloud-based big data solutions to collect, store and analyse real-time information from agricultural environments. The report requires students to act as a big data and cloud solution consultant and provide recommendations to senior management at AgroNova. The project involves IoT devices such as CropCam aerial cameras supplied by HydroSense and SmartHarvester solar-powered sensors supplied by SoilTech. These technologies collect information including crop imagery, soil moisture, temperature and humidity. The resulting data can support crop monitoring, risk assessment, early warning alerts, irrigation decisions, crop-failure claims validation and other farming-related insights. The first task focuses on Big Data Requirements and Storage Solutions. Students must identify the requirements arising from the scenario and critically evaluate a range of cloud-based big data storage solutions. The evaluation should consider factors such as capacity, functionality and costs. The second task requires students to propose one appropriate cloud-based solution architecture supported by an architecture diagram showing the essential components from data sources through to reporting. The selected architecture must be analysed in relation to AgroNova's requirements and the storage solutions considered in Task 1. The third task addresses Project Risks and Issues. Students must critically appraise the risks and issues associated with deploying the proposed cloud-based big data solution and identify appropriate mitigation approaches. These issues should be connected directly to the storage solutions and proposed architecture. The scenario highlights concerns from AgroNova's CISO, CFO and Chief Reputation Officer regarding potential data breaches, high costs and poor returns on investment, making security, financial viability and organisational risk important considerations. The report should contain an approximately 200-word introduction, an 800-word analysis of Big Data Requirements and Storage Solutions, a 500-word Proposed System Architecture section supported by relevant diagrams, an 800-word Project Risks and Issues section, and an approximately 200-word conclusion. Harvard referencing, academic research and appropriate supporting appendices are also required. The assessment addresses three learning outcomes: designing an architecture that supports complex data collection, critically evaluating data storage solutions from an enterprise systems perspective, and critically appraising issues involved in enterprise-system deployment.
Read Model Answer →
Big Data Analytics / Machine Learning
3,000 words
Machine Learning on Big Data Using PySpark: Large-Scale Data Analysis and Predictive Modelling
This group-based Machine Learning on Big Data project requires students to apply machine learning techniques to a large real-world dataset using PySpark DataFrames and Spark machine-learning libraries. Students select a substantial dataset, ideally between approximately 300 MB and 1 GB, from sources such as Kaggle, workplace data or other valid repositories, and develop an end-to-end big-data analytics workflow. CN7030 CRWK 26T1 The project begins with data loading and preprocessing using PySpark. Students are expected to handle missing values, perform data normalisation and feature engineering, identify class imbalance and propose appropriate mitigation strategies. Where text datasets are selected, additional preprocessing may include stemming, lemmatization and TF-IDF representation. CN7030 CRWK 26T1 The modelling stage requires implementation of an appropriate machine-learning approach using PySpark MLlib or Spark ML. The brief expects a multiclass rather than binary classification problem and allows techniques including multiclass classification, ensemble learning, clustering and text mining. Students must justify their model choice and consider model robustness, bias and variance when attempting to improve predictive performance. CN7030 CRWK 26T1 Students then perform hyperparameter tuning using techniques such as grid search or random search and evaluate the resulting model with appropriate measures. Relevant evaluation outputs may include accuracy, F1-score, precision, recall and a confusion matrix. Results should also be visualised or clearly presented and interpreted to identify meaningful patterns and performance characteristics. CN7030 CRWK 26T1 The project additionally requires consideration of Legal, Social, Ethical and Professional (LSEP) issues. Students discuss potential ethical concerns associated with their dataset, including bias and privacy risks, and propose suitable mitigation strategies. The final work is consolidated into a single user-friendly HTML analytics report that clearly presents the group's preprocessing, modelling, optimisation, evaluation and interpretation. CN7030 CRWK 26T1 CN7030 CRWK 26T1 Overview word count: approximately 335 words. If you are also uploading the presentation separately to the Reference Library, that should be a second entry under “Presentations and Academic Posters”, because the presentation forms a distinct 40% component and assesses understanding of Spark, preprocessing, modelling, optimisation, evaluation and responses to examiner questions. CN7030 CRWK 26T1
Read Model Answer →
Data Warehousing and Big Data
Data Warehousing and Big Data – Inventory Management Data Warehouse
This individual coursework for CS7079 Data Warehousing and Big Data requires students to design, implement and test a data warehouse based on a business case scenario and then export and migrate data to a Big Data platform for further processing. The assessment focuses on the inventory management business process of ABC Consumer Electronics Outlet Ltd, a multi-channel consumer electronics retailer operating from six stores around London and conducting online business across the UK and Europe. The company manages more than 10,000 products across approximately ten categories and more than 200 brands. The company already uses cloud-based and business applications including Vend, Linnworks and Xerox, which generate large volumes of transactional data. However, these datasets are stored separately by the individual applications, making it difficult for managers to produce integrated reports and perform analysis to support business decision-making. A business analyst has therefore recommended the development of a data warehouse capable of meeting the company's reporting and analytical requirements. For this coursework, the solution is focused specifically on the inventory management business process. The inventory management case involves three main business activities: sending purchase orders to suppliers when stock reaches minimum levels, receiving purchase orders and storing stock in appropriate locations, and controlling and maintaining stock levels including adding new products and adjusting existing stock. The required analysis includes daily stock levels for the previous month, weekly identification of products at minimum stock levels, analysis of stock levels by brand, product type and supplier, daily and weekly sent and received stock orders, and analysis of received stock orders by supplier and month. Students must analyse and design a dimensional data model that represents these business activities. This includes defining the grain of three central fact tables, identifying appropriate dimensions, defining dimension attributes and fact measures, and producing simple star schemas showing the relationships between fact tables and dimensions. The design must be justified according to the available data sources and the reporting and analysis requirements. The implementation stage requires students to create the relational database using Microsoft SQL Server Management Studio. Students must create the database, dimension tables and fact tables, establish appropriate primary-key and foreign-key constraints, and demonstrate implementation and testing through SQL commands and their results. Test data must then be populated into the data warehouse. The Big Data component requires migration of test data from the data warehouse to an Apache Hadoop platform using the Hortonworks Data Platform. Students must export the data warehouse data to an external data file, migrate the file into Apache HDFS, create a suitable data structure for loading the data into Hive, and demonstrate Apache Pig for manipulating the loaded data. Implementation and testing of the Big Data storage environment must also be demonstrated through commands and results. The coursework concludes with a personal reflective section in which students discuss what they have learned throughout the overall coursework and the challenges encountered during the process. The final submission is expected to be a well-written, structured and well-presented report combining data warehouse design, database implementation, testing, Big Data migration and reflective learning.
Read Model Answer →
Machine Learning and Big Data
2,000 words
Machine Learning for Big Data
This assignment for the Machine Learning and Big Data module requires students to produce a 2,000-word individual report demonstrating their understanding and practical application of machine learning techniques to big data. The assessment is worth 15 credits and is structured around five interconnected areas: data in big data, machine learning architecture, model deployment, model evaluation, and the machine learning lifecycle. The first part focuses on identifying and evaluating suitable datasets for a selected big data topic and determining whether the datasets are appropriate for the intended machine learning application. Students are expected to examine the characteristics of their data and apply appropriate pre-processing approaches, including consideration of attribute selection and data preparation. The second part addresses machine learning modelling architecture. Students must develop an appropriate architecture for their big data application and may compare alternative architectural approaches. The report should explain the selected machine learning techniques and demonstrate how they operate as part of the proposed system. Practical considerations such as performance, scalability, fault tolerance, technology usage and reliability should also be considered. The third part requires students to implement and deploy the proposed data and machine learning model. This includes testing, visualising and evaluating the resulting outcomes. The fourth part requires critical evaluation of the dataset selection, modelling design, implementation and application, including assessment of whether the selected machine learning techniques are appropriate for the intended purpose. The final part focuses on the complete project lifecycle. Students are expected to critically reflect on the work undertaken, identify what they have learned, evaluate the development process and explain how the machine learning application could be improved in a future implementation. The assessment develops five learning outcomes covering big data sources and applications, machine learning techniques, practical application of machine learning tools, critical evaluation of techniques and tools, and the ability to follow a complete big data analysis lifecycle. The marking criteria allocate 20% to each of these five areas. The assignment is submitted as an individual written report. The brief states that Microsoft Word should be used rather than PDF and requires students to acknowledge sources and any AI tools used in accordance with the stated AI policy.
Read Model Answer →
Emerging Technology and Cloud Computing
5,000 words
Emerging Technology and Cloud Computing – SafeCloud Project
This MSc Management coursework for BPP University’s Emerging Technology and Cloud Computing module is a 5,000-word formal business report based on the SafeCloud project at AlwaysUp Ltd., a manufacturing company specialising in power-electric equipment for buildings and critical installations. The assignment examines how emerging technologies, Big Data and cloud computing can support AlwaysUp Ltd.’s international expansion, real-time equipment monitoring, personalised preventive maintenance, data-driven decision-making, and secure management of equipment and client information. The company is seeking to expand from the UK into European markets through distributor partnerships and ultimately achieve global reach. The report requires the identification and critical evaluation of two emerging technologies that can improve AlwaysUp Ltd.’s support and equipment-care services. Students must evaluate the benefits and limitations of each technology within a manufacturing context and use real-world examples to support the analysis. The assignment specifically assesses the ability to demonstrate a comprehensive understanding and critical evaluation of emerging technologies in business. The second task focuses on designing and evaluating a cloud-based Big Data architecture that integrates the two selected emerging technologies. The proposed architecture must include an architecture diagram and should be evaluated in terms of scalability, security, cost-effectiveness, real-time data processing and decision-making. The solution should address AlwaysUp Ltd.’s business requirements and enable the collection, storage and analysis of equipment-related data. The third task examines data protection, ethical considerations, project risks and resource requirements associated with implementing the proposed solution. This includes consideration of data protection and ethical issues arising from the selected technologies, implementation risks, and the human, technological and other resources required. The fourth task analyses the strengths and weaknesses of the combined emerging technologies and Big Data architecture and considers their application to AlwaysUp Ltd.’s business strategy. The report must conclude with a proposed route forward based on the findings. The required report structure consists of an introduction of approximately 500 words, four main tasks of approximately 1,000 words each, a conclusion of approximately 500 words, Harvard-style references and optional appendices.
Read Model Answer →
Big Data Analytics / Data Analytics
4,000 words
Big Data Analytics Using Python and Business Intelligence with Tableau
This individual Big Data Analytics assessment requires students to critically analyse data using programming languages, statistical techniques, data visualisation methods and business intelligence software. The assessment combines practical data analytics using Python with interactive business intelligence and dashboard development using Tableau, requiring evidence of technical implementation, research, critical appraisal and justification of the selected analytical approaches. The first section, worth 70%, is based on a dataset containing accidental drug-related deaths recorded in Connecticut between 2012 and 2024. The dataset contains 12,964 observations and 49 variables. Students are required to conduct exploratory data analysis using Python, develop three to four research questions, formulate null and alternative hypotheses, and apply appropriate statistical methods. The analytical process also requires an evaluation of alternative technologies and methodological approaches, supported by relevant research. Students must justify their selected methodology and present a clear workflow diagram. The solution-development stage involves data preprocessing, descriptive statistical analysis, visualisation, answering the research questions and conducting statistical significance testing to determine whether the null hypothesis should be accepted or rejected. Evidence of coding and a link to working code are also required. The final Python component requires evaluation of the findings, consideration of limitations and recommendations for future development using emerging technologies. The second section, worth 30%, focuses on Business Intelligence using Tableau and uses a historical Olympic Games dataset containing 271,116 rows and 15 columns. Students analyse relationships between medals and host cities, athlete age and medal type, season and medal counts, and sex and medal type. The section culminates in an interactive Tableau dashboard containing at least four interconnected sheets, where changes to relevant parameters are reflected across the dashboard. Overall, the assessment develops practical competence in Python-based analytics, statistical reasoning, research-question development, hypothesis testing, data visualisation and interactive business intelligence dashboard design. Overview word count: approximately 350 words. AI note: the brief permits AI for limited support such as grammar, structure, organisation of ideas and suggestions, but states that the main content, analysis and conclusions must remain the student's own work. AI use must also be declared.
Read Model Answer →
Cloud Computing / Big Data Technologies / Cyber Security
2,500 words
Cloud and Big Data Security Application: Design, Implementation and Evaluation
This assessment for the Cloud and Big Data Technologies module requires students to design, implement and evaluate an individual cloud-based or distributed data application. The project focuses on practical solutions involving the complex transformation, processing, storage and security of big data within cloud environments. Students are expected to demonstrate how distributed data can be organised in the cloud, how data pipelines can be used to access or process distributed databases, and how appropriate security controls can be incorporated into the resulting architecture. Students have considerable freedom when selecting their application. Suggested project directions include developing a data-science solution using SQL or MongoDB with cloud storage and an appropriate security policy; implementing privacy-preserving distributed processing using techniques such as Differential Privacy; creating multi-party authentication and group-based access-control mechanisms; or designing Multi-Level Security, Attribute-Based Encryption or Role-Based Access Control solutions. Projects may also examine distributed or cloud applications using security protocols such as SSH, SSL or IPsec. Creativity and originality are explicitly encouraged. The written component is a Design and Implementation Document of no more than approximately 2,500 words. It should present the project aims and objectives, application concept, cloud and security technologies, functional and security requirements, architecture and design decisions, protocols, access-control mechanisms, implementation process, achievements, problems encountered and overall evaluation. Relevant diagrams, such as interaction or sequence diagrams, may be used to explain system behaviour and architecture. The assessment also requires submission of the functioning Cloud and Big Data Security application and a 7-minute highlight demonstration video. The video should demonstrate the application's major features, implementation details, security functionality and, where appropriate, attack scenarios. Assessment places strong emphasis on the quality of the design and implementation documentation, originality, use of advanced features, and the overall effort and technical quality of the completed application. Students are therefore expected to demonstrate independent development rather than simply reproduce an existing tutorial.
Read Model Answer →