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Financial Mathematics
Financial Mathematics: Portfolio Analysis, Risk and Volatility Modelling
This Financial Mathematics coursework focuses on the quantitative analysis of financial assets, portfolio construction, investment risk and volatility modelling. Students are required to work with historical daily share-price data for five stocks previously selected in their portfolio. The data should cover a sufficiently long period, with at least one year of observations, and should provide positive average returns. Students must briefly describe each selected company and the nature of its business before conducting the financial analysis. The first part of the coursework requires students to calculate the expected return and volatility of each of the five companies and analyse the correlations between their asset returns. These calculations provide the foundation for evaluating the risk and return characteristics of the individual assets and their potential contribution to a diversified portfolio. The second part focuses on portfolio optimisation. Students must use an appropriate Solver function to determine portfolio risk and the percentage investment allocated to each asset for a selected target return. The process must be repeated for different target returns to generate an efficient frontier curve. This analysis demonstrates the relationship between expected portfolio returns and the associated levels of portfolio risk. The third part requires students to calculate Sharpe ratios for a range of expected portfolio returns and volatilities obtained through the portfolio analysis. Using a risk-free investment with a guaranteed return of 1.5%, students must determine the equation of the Capital Market Line and discuss its economic significance in relation to investment decisions and portfolio performance. The fourth part applies linear regression analysis to calculate the beta of each asset in the portfolio and discuss the significance of beta as a measure of systematic risk. Students must also estimate the portfolio's Value at Risk at the 5% level and discuss the contribution of each individual asset to the estimated portfolio VaR. The fifth part focuses on financial volatility modelling using R. Students must estimate the volatility of a selected individual asset using ARCH/GARCH models and their extensions, identify the most appropriate model and provide an explanation supporting the model selection. Finally, students must present their findings in non-technical language suitable for a potential investor. The conclusion should identify the implications of the analysis for selecting an efficient portfolio and discuss other relevant performance measurements. The coursework requires clear explanations of the methods and formulae used in Excel worksheets and R outputs, while unnecessary explanations of portfolio theory and the Capital Asset Pricing Model should be avoided.
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Applied Modelling and Visualisation
2,500 words
Applied Modelling and Visualisation – Hexawing Airways Passenger Satisfaction Analysis
This assessment is a 2,500-word consultancy report for the Applied Modelling and Visualisation module within the MSc Management with Data Analytics programme at BPP University. The assignment requires students to work as Data Analytics Consultants for the fictional Hexawing Airways and analyse a passenger satisfaction dataset containing more than 103,000 records from the airline's database. The purpose of the assessment is to apply machine learning, data analysis and visualisation techniques to identify factors that influence passenger satisfaction and communicate meaningful findings to a varied corporate audience, including the Chief Executive Officer, senior flight personnel and cabin crew. The dataset contains a range of passenger and flight-related features, including gender, satisfaction status, age, age band, type of travel, travel class, flight distance, destination, continent and ratings for services such as inflight Wi-Fi, online booking, gate location, food and drink, online boarding, seat comfort, inflight entertainment, onboard service, leg room, baggage handling, check-in service, inflight service and cleanliness. It also includes departure and arrival delay information. These variables provide the basis for exploratory analysis, predictive modelling and visual communication of passenger satisfaction patterns. The assessment requires the development of a data-driven solution using Python and relevant Python libraries. Students must follow an established analytical methodology such as PPDAC or CRISP-DM and demonstrate an Extract, Transform and Load process, data preparation, exploratory data analysis and appropriate visualisations. Two analytical models must be selected, trained and tested to predict passenger satisfaction. The available modelling approaches include Logistic Regression, Naive Bayes, Decision Tree, Bagging, Random Forest, AdaBoost, XGBoost, Artificial Neural Networks or another appropriate state-of-the-art algorithm. The second task requires critical analysis of the two selected models, including their strengths and limitations, an explanation of the chosen loss function, discussion of accuracy metrics and a comparison table of model performance. The third task focuses on communicating findings through data visualisation, including outputs such as correlation matrices, heat maps and confusion matrices. The analysis should explain how exploratory data analysis guided model selection and how visualisation techniques communicate insights effectively. The final report should demonstrate the ability to formulate data-driven solutions, critically evaluate analytical models and appraise data visualisation techniques. The assessment also requires independent research, appropriate academic referencing and supporting evidence from the analytical process. A pre-run Python notebook must be embedded in the MS Word submission or provided through an appropriate shared link.
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Cyber Security / Digital Forensics
3,877 words
Digital Forensics Investigation: USB, Memory and Windows Registry Analysis
This Digital Forensics Investigation Report presents a multi-source forensic examination involving removable media, volatile memory and a Windows disk image. The work is undertaken within an MSc Cyber Security context and demonstrates the application of forensic procedures, specialist analysis tools and evidential reasoning to investigate suspected criminal and malicious activity. The report places particular emphasis on maintaining evidence integrity, reconstructing activity across different forensic sources and correlating artefacts to produce defensible investigative findings. DF_Report_Sathiyaprakash The first part investigates a FAT32 USB forensic image associated with suspected video piracy and other potentially criminal activity. The examination uses tools including FTK Imager, Autopsy, Sleuth Kit, ewfmount, cryptographic hashing utilities and VeraCrypt. The methodology includes pre- and post-examination hash verification, read-only mounting, filesystem enumeration, deleted-file analysis and low-level sector examination. The investigation identifies deleted artefacts, portable anti-forensic utilities, browser evidence and an encrypted VeraCrypt container concealed within unallocated disk space. DF_Report_Sathiyaprakash The USB investigation also demonstrates the importance of evidence integrity and chain of custody. MD5 and SHA-256 hashes are used to establish and later confirm the integrity of forensic copies, while analysis is conducted without modifying the original evidence. The report examines filesystem structures, deleted files, anti-forensic tooling and encrypted data and records evidence handling through a formal chain-of-custody process. DF_Report_Sathiyaprakash DF_Report_Sathiyaprakash The second part focuses on volatile-memory forensics using a Windows memory image. Volatility 3 is used to identify the operating-system profile, reconstruct process hierarchies, inspect process ownership and security identifiers, and extract suspicious process memory. Particular attention is given to AtomicService.exe, which is observed running with SYSTEM privileges and associated with the Atomic Red Team framework and MITRE ATT&CK technique T1543.003 – Windows Service. The investigation also considers PowerShell activity, process execution timelines and indicators of suspicious behaviour. DF_Report_Sathiyaprakash DF_Report_Sathiyaprakash The third part conducts Windows Registry forensic analysis on the WinRegEvidenceP3.vhd image. Registry artefacts are examined using RegRipper, with analysis covering SYSTEM, SOFTWARE, SAM and NTUSER.DAT hives. The investigation evaluates system configuration, user accounts, application execution and persistence evidence using artefacts such as Run keys, BAM, Prefetch and scheduled tasks. These findings are then correlated with evidence recovered from volatile memory to reconstruct the sequence of suspicious activity. DF_Report_Sathiyaprakash Across the report, evidence from disk, memory and the Windows Registry is combined to reconstruct malicious activity and identify persistence mechanisms, elevated processes, suspicious user accounts and adversary-simulation tools. The analysis maps relevant behaviour to the MITRE ATT&CK framework and considers both technical findings and their evidential significance. The report therefore demonstrates practical competence in forensic acquisition principles, artefact analysis, timeline reconstruction, malware and process investigation, evidence correlation and professional reporting. Important for the Reference Library: this upload contains an actual student name on the cover page and detailed case evidence. Since your Reference Library says there is no student record behind uploaded past work, I would use the generic title and overview above rather than copying the student-identifying cover-page information into the public metadata. DF_Report_Sathiyaprakash
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Machine Learning / Cloud Computing / Artificial Intelligence
4,004 words
Cloud-Based Machine Learning for Financial Fraud Detection
This Machine Learning on Cloud assessment requires students to design, implement and critically evaluate a cloud-oriented machine-learning solution for financial fraud detection. Working as a group, students address a scenario in which a financial-services organisation requires an automated model capable of detecting fraudulent transactions, reducing financial losses and improving customer security. The project combines machine-learning development with critical evaluation of cloud infrastructure, data preparation, model performance and responsible AI considerations. NUL - LD7187 -Assessment Brief … The project begins with a cloud feasibility study comparing at least two major machine-learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Students evaluate factors including performance, scalability, cost, compliance, system integration and vendor lock-in before providing a justified recommendation. The project then moves into Exploratory Data Analysis, where patterns, anomalies and correlations within the fraud dataset are investigated using visualisations such as heatmaps, histograms and boxplots. NUL - LD7187 -Assessment Brief … A substantial part of the assessment focuses on data preprocessing and class imbalance. Students are expected to clean and transform the dataset, apply scaling and encoding, perform feature engineering and investigate techniques such as SMOTE, undersampling and cost-sensitive learning. These decisions must be justified in terms of their potential effect on predictive performance. NUL - LD7187 -Assessment Brief … Students must then select and train at least two machine-learning models. Suggested algorithms include Logistic Regression, Random Forest, XGBoost and Neural Networks. Appropriate cross-validation and hyperparameter-tuning procedures should be applied, followed by systematic evaluation using precision, recall, F1-score, AUC and precision-recall curves. Supporting visualisations should include confusion matrices, ROC curves and feature-importance analysis. NUL - LD7187 -Assessment Brief … The final component addresses professionalism and ethics in cloud-based AI, including bias, fairness, transparency, data privacy and environmental sustainability. Overall, the project integrates cloud-platform selection, exploratory analytics, preprocessing, imbalanced-data handling, predictive modelling, model evaluation and ethical AI into an applied financial fraud-detection solution. NUL - LD7187 -Assessment Brief … Note: the uploaded brief does not explicitly name a referencing system. If your portal requires a selection, I would use Not specified rather than assume Harvard.
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Statistical Programming / Data Science / Business Analytics
Statistical Programming with R: Data Analysis, Probability, Regression and Business Decision-Making
This Statistical Programming assessment evaluates students' ability to apply statistical techniques and R programming to practical data-science and business decision-making problems. The individual assessment combines descriptive statistics, data preparation, visualisation, probability, regression, correlation and sampling, requiring students to demonstrate both conceptual statistical understanding and practical implementation in RStudio. The module learning outcomes emphasise the application of statistical methods to large and real-world datasets, critical evaluation of analytical techniques and awareness of legal, cultural and ethical issues associated with data analysis and dissemination. KL7012 - Statistical Programmin… The early tasks examine fundamental statistical reasoning. Students interpret weight-loss data comparing exercise classes with gym-only workouts using sample size, mean, mode and standard deviation, and explain an appropriate method for dealing with missing data, including its advantages and disadvantages. KL7012 - Statistical Programmin… A substantial practical component uses a cystic fibrosis dataset containing variables such as age, sex, height, weight, body-mass-related measurements, forced expiratory volume, residual volume, functional residual capacity, total lung capacity and maximum expiratory pressure. Students import the data into an R data frame, generate descriptive summaries and interpret the results. They then use scatterplots to investigate relationships between variables and sex-stratified boxplots to identify possible outliers. KL7012 - Statistical Programmin… The assessment also covers major probability models. Students apply probability concepts to healthcare survival, helpdesk email arrivals and fuel-demand scenarios, while also discussing how changing assumptions or real-world conditions can affect interpretation. These exercises assess understanding of statistical distributions and their application to operational and managerial decision-making. KL7012 - Statistical Programmin… Further analytical tasks examine linear regression and correlation. Students analyse the relationship between temperature and converted sugar in a chemical process, use a regression model to estimate the expected response at a specified temperature, and interpret relevant summary statistics. They also calculate and evaluate the suitability of a correlation coefficient for examining the relationship between advertising activity and product purchases. KL7012 - Statistical Programmin… The final and most substantial task involves a real-world M1 traffic-speed investigation for a manufacturing organisation. Students must design an appropriate sampling strategy, collect data from the specified Traffic England source, conduct statistical analysis in RStudio and develop evidence-based conclusions. The statistical report for this task is limited to 1,500 words and should include sampling methodology, collected data, statistical analysis, results, conclusions and relevant background research, supported by appropriate graphs, tables and charts. Raw data and RStudio calculations must be included in an appendix. KL7012 - Statistical Programmin… Overall, the assessment integrates statistical theory with R-based practical analysis, covering descriptive statistics, probability, visualisation, missing-data treatment, regression, correlation, sampling and critical interpretation of results in healthcare, operational and business contexts.
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Team Research and Development
Team Research and Development – Research Question, Hypothesis and Statistical Data Analysis
This group assessment for 7COM1079 – Team Research and Development requires students to collaboratively select a dataset and conduct a structured research investigation using data analysis. The assessment is worth 40% of the module mark and requires group members to work together to develop and submit a final research report. All group members are expected to contribute equally to the work, with one designated group member submitting the final assessment on behalf of the group. The main objective of the assignment is to develop a meaningful research question and corresponding hypothesis based on a selected dataset. The research question defines a specific claim or issue that the group intends to investigate and provides the starting point for constructing and testing an evidence-based argument. The hypothesis provides a proposed explanation or prediction that can be examined through analysis of the available data. Together, the research question and hypothesis establish what evidence is required, how that evidence will be evaluated and how effectively the findings can support or challenge a particular position. The assignment permits three types of research questions. The first involves establishing a difference in means between two groups. The second involves establishing a difference in proportion between two groups. The third involves establishing a correlation between two measures. The selected research question should therefore be appropriate for the characteristics of the chosen dataset and should allow the group to perform meaningful statistical analysis. Following the selection of the dataset and formulation of the research question and hypothesis, students are expected to produce appropriate data visualisations and statistical analysis. The analysis should provide evidence relevant to the research question and allow the proposed hypothesis to be examined systematically. The resulting findings should be interpreted in relation to the original research question and hypothesis rather than simply presenting numerical results. The assignment provides a Microsoft Word final report template containing the required table of contents, chapter and subchapter names and explanations of the expected content. Students are instructed to download and use this template when preparing their final report. Work produced by individual students during their first assignment may be incorporated where the student examined the same dataset allocated to the group, provided the material is appropriately incorporated into the group submission. All italicised instructional text in the template must be removed before submission. The completed amended template and the group's dataset file must be submitted through Canvas. Acceptable submission formats include PDF, DOC, DOCX, CSV and XLS. The assignment has a late-submission penalty, and the submission deadline is stated as being available in the assignment specification on Canvas. Assessment is based on the criteria provided in the module rubric. The rubric is used to assess the group's work, with group members initially receiving the same mark, although peer review may be taken into account. The assignment therefore combines collaborative research, statistical reasoning, data visualisation, analytical interpretation and academic report writing. The assignment instructions explicitly state that students should not use AI for this assessment. The module team also reserves the right to arrange a viva if academic misconduct is suspected. The final report should therefore represent the group's own research, analysis, interpretation and contribution.
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Data Science / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assessment requires students to develop a comprehensive analytical solution to a real-world healthcare prediction problem using the WiDS Datathon 2025 Health Outcomes Prediction Dataset. The dataset contains socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents, with the principal objective of developing predictive models for ADHD diagnosis. The assessment is designed to demonstrate the complete data-science lifecycle, from data preparation and exploratory analysis through predictive modelling, interpretation and evidence-based recommendations. Students begin by exploring the dataset's features, data types and distributions before addressing missing values, outliers and other inconsistencies. Appropriate feature engineering should be undertaken where necessary, followed by Exploratory Data Analysis (EDA) using relevant visualisations to identify relationships, patterns and correlations within the data. Students with limited computational resources may use a representative subset, provided that the sampling method preserves the integrity and distribution of the original dataset and is clearly justified. A major component of the assignment involves developing and comparing at least three classification models. Appropriate techniques may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Model performance should be evaluated using measures including accuracy, precision, recall, F1-score and ROC-AUC, allowing students to identify the strongest-performing model through systematic comparison. The assessment also requires model interpretation and explainability. Students should explain the results of the selected model and may apply techniques such as SHAP or LIME to investigate feature importance and individual predictions. A feature-importance visualisation must be produced, and the most influential variables should inform practical recommendations for healthcare professionals regarding the potential use of predictive modelling in supporting earlier ADHD diagnosis and intervention. Overall, the assignment integrates data cleaning, exploratory analytics, predictive modelling, model comparison, explainable AI and research-informed healthcare recommendations. Students must submit a comprehensive report of no more than 2,500 words, alongside a Jupyter Notebook containing the implementation and outputs. The report must use Harvard referencing, with appropriate academic research integrated into the analysis, recommendations and conclusion.
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Machine Learning / Cloud Computing
3,984 words
Machine Learning on Cloud: Fraud Detection Model Design, Training and Evaluation
This postgraduate group project focuses on the design, development and critical evaluation of a cloud-based machine learning solution for financial fraud detection. The scenario involves a financial services organisation seeking to detect fraudulent transactions in order to reduce financial losses and improve customer security. Students are required to analyse an appropriate dataset, develop a machine learning solution, evaluate its effectiveness and critically consider the suitability of cloud technologies for deployment. The project begins with a cloud feasibility study, requiring critical comparison of at least two major machine learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Evaluation criteria include performance, scalability, cost, compliance, integration and vendor lock-in, followed by a justified recommendation. Students then conduct exploratory data analysis to identify patterns, anomalies and correlations using appropriate visualisations such as heatmaps, histograms and boxplots. A substantial component addresses data preprocessing and class imbalance. Students are expected to clean and transform the data, apply scaling and encoding, perform feature engineering and investigate approaches such as SMOTE, undersampling and cost-sensitive learning. Each preprocessing choice must be justified in terms of its potential impact on model performance. Students must select and train at least two machine learning models, with suggested approaches including Logistic Regression, Random Forest, XGBoost and Neural Networks. Model development incorporates cross-validation and hyperparameter tuning. Evaluation uses fraud-relevant measures including Precision, Recall, F1 score, AUC and precision-recall curves, supported by confusion matrices, ROC curves and feature-importance visualisations. The project concludes with critical consideration of professional and ethical issues in cloud-based AI, including bias, fairness, transparency, data privacy and sustainability. The overall assessment therefore integrates cloud-platform evaluation, machine learning development, imbalanced classification, model evaluation and responsible AI practice. Overview word count: approximately 340 words.
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Machine Learning / Data Science
Linear Regression and Stability of the Moore–Penrose Pseudoinverse Using Python
This machine learning practical and assessment activity develops an understanding of linear regression, Ordinary Least Squares and the Moore–Penrose pseudoinverse using Python. The work progresses from generating synthetic regression datasets to implementing regression algorithms manually, applying established machine learning libraries, analysing real datasets and evaluating the stability of estimated regression coefficients. The laboratory component begins with the generation of synthetic linear regression data using NumPy, including explanatory variables, random noise and an outcome variable. Students then implement simple linear regression without relying on machine learning libraries, using the least-squares solution to estimate the intercept and slope. The resulting observations and fitted regression line are visualised using Matplotlib. The work is subsequently extended to multiple linear regression, where several independent variables are used and coefficients are first calculated manually before the same problem is solved using Scikit-learn. The laboratory also introduces application of regression to the Scikit-learn Diabetes dataset, including feature and target standardisation, model fitting, prediction, correlation analysis and interpretation of regression coefficients. It also highlights the importance of residual analysis when assessing whether a linear model is appropriate. The associated weekly challenge focuses on the stability of linear regression solutions estimated using the Moore–Penrose pseudoinverse. Using a house-price dataset containing variables such as property size, number of bedrooms, distance from the city centre and property age, students construct the design matrix, standardise features and the response variable, and calculate regression coefficients using the pseudoinverse. Students then investigate model robustness by repeatedly fitting the regression model to random subsamples of different sizes and analysing the mean and standard deviation of each coefficient. Tables, boxplots or error-bar visualisations can be used to compare coefficient variability. The final discussion considers which variables are most influential, which coefficients are most stable, how sample size affects stability and whether coefficient interpretation remains reliable across different samples. The final work is submitted as a single PDF exported from Jupyter Notebook or Google Colab, combining documented Python code, experimental results, plots and written interpretation in a professionally organised notebook. Overview word count: approximately 360 wor
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Data Science / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assignment focuses on developing a comprehensive analytical solution to a real-world healthcare prediction problem. Using the WiDS Datathon 2025 Health Outcomes Prediction Dataset, students are required to analyse complex and high-dimensional healthcare data containing socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents. The principal predictive objective is to determine ADHD diagnosis from the available features. Students may use a representative subset of the dataset where computational resources are limited, provided that the sampling approach maintains the integrity and distribution of the original data and is appropriately justified. The assessment requires a complete data-science workflow beginning with data understanding and preprocessing. Students investigate the dataset's features, data types and distributions before addressing missing values, outliers and inconsistencies. Appropriate feature engineering should then be undertaken where it can improve the predictive capability of the models. Exploratory Data Analysis is used to identify important patterns, relationships and correlations, supported by relevant visualisations that communicate meaningful insights. A major component of the work involves the development and comparison of at least three classification models for predicting ADHD diagnosis. Suitable approaches may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Models are evaluated using performance measures including accuracy, precision, recall, F1-score and ROC-AUC, after which the most effective model is selected based on the evidence obtained. The assessment also places substantial emphasis on model interpretation and explainability. Students must interpret the selected model and may use approaches such as SHAP or LIME to explain feature importance and individual predictions. A feature-importance visualisation is required, and the most influential variables should inform practical recommendations. The final section translates analytical findings into recommendations for healthcare professionals, considering how predictive modelling could assist early ADHD diagnosis and intervention. Research literature must be integrated into the recommendations and conclusion. The assessment therefore combines preprocessing, exploratory analysis, predictive modelling, explainable AI and evidence-based healthcare decision-making within a single applied data-science project. The required report is a maximum of 2,500 words, with code, supplementary charts and tables permitted in appendices. A Jupyter Notebook containing the implementation and outputs is also required. Harvard referencing must be used throughout.
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