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Time Series Modelling
Time Series Modelling Case Study – Oil Price Forecasting
This individual Time Series Modelling Case Study focuses on analysing and forecasting oil price data using established time-series techniques and an alternative modelling approach. The assessment requires students to work with daily oil price information covering the period from 2024 to 2026 and investigate the underlying patterns, stationarity and forecasting behaviour of the data. The first part of the assignment involves exploratory data analysis and time-series modelling using an ARMA-based approach. Students are required to create appropriate visualisations of the data, perform exploratory analysis and conduct tests for non-stationarity, including relevant stationarity diagnostics such as ADF, ACF and PACF analysis and differencing where required. An ARMA model must then be defined, with suitable model parameters identified using the AIC likelihood approach. The assessment requires the student to examine possible combinations of model parameters, assess model residuals, evaluate model performance using appropriate metrics such as RMSE, and produce forecasts extending 24 months into the future. Confidence intervals must also be included with the forecasts. The second part requires students to investigate an alternative modelling solution for the same oil-price time-series data. Possible approaches discussed in the assignment include models such as LSTM and Prophet. Students are expected to conduct a literature review relating to the selected alternative model, build and apply the model, tune relevant hyperparameters where appropriate, generate 24-month forecasts, create suitable visualisations and calculate appropriate evaluation metrics. The final part of the assessment requires a 6–8 page report describing the modelling process, forecasts, analysis and inferences. The report should explain the reasoning behind the analytical and modelling choices rather than simply presenting numerical results. Students are expected to critically discuss why particular approaches were selected, how the modelling decisions may have influenced the results, how forecasts compare with subsequently collected real data where available, and what improvements could be made in future work. The assessment evaluates both the technical implementation and the quality of the written analysis. The code component assesses completion of the modelling and forecasting tasks, stationarity testing, the alternative solution, code quality and annotation. The report component assesses discussion of the analysis and inferences, comparison of the modelling approaches, clarity of interpretation, report structure, appropriate use of figures and suitable academic references. The submission consists of a report in PDF or Word format, with the code submitted separately or through an appropriate repository.
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Data Science / Time Series Analysis / Machine Learning
Time Series Modelling Case Study: Oil Price Forecasting with ARMA and Alternative Models
This Time Series Modelling Case Study requires students to analyse real-world oil-price time-series data and develop forecasting models capable of predicting future values. The assessment combines traditional statistical time-series techniques with an alternative forecasting approach, requiring students to demonstrate practical modelling skills, critical research engagement and evidence-based interpretation of forecasting results. The coursework is completed individually and contributes 40% of the assessment. assing,,, (1) The assessment is divided into three main parts. Part 1 focuses on developing an ARMA-based forecasting model using daily oil-price data covering approximately 2024 to 2026. Students begin with exploratory data analysis and initial visualisation before testing whether the time series is stationary. Where necessary, appropriate transformations or differencing must be applied to obtain stationarity. Students then define an ARMA model and identify suitable p, d and q parameters using an AIC-based model-selection procedure across the parameter ranges specified in the brief. assing,,, (1) Model adequacy must be assessed using diagnostic analysis. Students inspect residuals, generate additional ACF plots, examine residual distributions and evaluate prediction performance using appropriate metrics such as RMSE. The selected model is then used to forecast oil prices 24 months into the future, with appropriate confidence intervals added to communicate forecast uncertainty. assing,,, (1) Part 2 requires students to research and implement an alternative forecasting approach. Suggested examples include LSTM and Prophet, although another appropriate model may be proposed. Students conduct a literature review supporting the alternative method, build and where relevant hyperparameter-tune the model, generate another 24-month forecast, visualise predictions and confidence intervals, and calculate suitable evaluation metrics. This component is intended to demonstrate independent research and the ability to propose an alternative solution rather than relying only on the conventional ARMA approach. assing,,, (1) Part 3 consists of a 6–8 page technical report explaining the modelling process, forecasting results and resulting inferences. The report should provide a critical analysis rather than simply reproducing numerical outputs. Students are expected to explain why results occurred, justify modelling choices, evaluate how those choices influenced performance, compare forecasts with subsequently observed real data where possible, and construct a coherent narrative supported by plots, images, summary statistics and academic literature. Future improvements to the modelling approach should also be critically discussed. assing,,, (1) Submission consists of both the report and working code. The code may be submitted directly or through an accessible Colab or GitHub repository and must reproduce all models, figures and numerical results presented in the report. The assessment allocates 60% of the marks to code and 40% to the report. Within the coding component, modelling and forecasting completion accounts for 40 marks and code quality and annotation for 20 marks. The report is assessed on analysis and inference, methodological justification, comparison of the two modelling approaches, presentation quality, figures and use of appropriate references. assing,,, (1) Key technical expectations include appropriate testing for stationarity, use of methods such as ADF, ACF, PACF and differencing, systematic model selection, forecasting, evaluation and clear comparison between the traditional ARMA model and the chosen alternative approach. Higher-quality work is expected to interpret what the forecasts mean, identify potential improvements and demonstrate sound technical communication rather than merely reporting model outputs. assing,,, (1) Important for the public Reference Library: the brief explicitly states that students must not use generative AI to write the report, and the rubric indicates that AI text-generation use may result in zero marks for the whole assignment. Therefore, the public entry should remain a high-level description of the assessment rather than material intended for direct submission. assing,,, (1)
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Management
5,000 words
Principles of Management – Tesco Management Report
This assessment requires students to produce a 5,000-word management report analysing the current management practices of Tesco. The report is written from the perspective of a business consultant and is intended for Tesco’s senior management team. Students are required to apply management theories, concepts and practices covered in the module, supported by appropriate academic literature, case study materials and independent research. The assessment also requires students to develop a personal Skills Development Plan and a 500-word Reflective Statement focused on their own management competencies and future career objectives. The report addresses four main tasks. Task 1 focuses on key management theories, concepts and practices and requires students to explain how management contributes to Tesco’s success and adds value. Students should discuss the four philosophies of the Competing Values Framework and identify one additional relevant management theory that supports the analysis of Tesco’s management approach. Task 2 evaluates management as a value-adding universal activity by examining the dynamics of the global business environment and how Tesco adapts its strategies and methods at global, national and local levels. Students may apply frameworks such as LoNG-PEST or Porter’s Five Forces and should identify one global opportunity and one global threat. Task 3 critically analyses the key principles and functions of management, including planning, organising, leading and controlling. Students must identify one internal challenge faced by Tesco and evaluate how management functions can be applied to address it, supported by an appropriate academic model such as Value Chain analysis, VRIO or Mendelow’s Matrix. Task 4 focuses on critical reflection and skills development. Students are required to complete a Personal SWOT, prepare a Skills Development Plan addressing their future career aims and objectives, and provide a 500-word Reflective Statement using an appropriate reflection model such as Borton, Kolb’s Cycle, Maslow’s or GROW. The reflection should focus on professional skills including self-management, problem-solving and decision-making. The report should conclude with recommended changes that Tesco should implement to improve its success. The assignment requires academic writing, critical analysis, evidence-based arguments and Harvard referencing. The main body consists of an introduction, four assessment tasks and a conclusion, while the cover page, contents, references and appendices are excluded from the 5,000-word limit. :contentReference[oaicite:2]{index=2} :contentReference[oaicite:3]{index=3}
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Principles of Data Science
2,000 words
Principles of Data Science – Data Analysis Portfolio
This portfolio assignment for the Principles of Data Science module at Coventry University requires students to analyse the Global Life-Work Balance Index 2025 dataset using statistical and data science techniques in R. The dataset ranks 60 countries according to life-work balance using factors including statutory annual leave, paid maternity leave, sick leave, healthcare, public safety, public happiness, LGBTQ inclusivity and average working hours per employee. The assignment has a 2,000-word equivalent limit, excluding the reference list and output. The portfolio consists of two main tasks. Task 1 is a group task involving multivariate data analysis. Students must use R to perform Principal Component Analysis (PCA) and Cluster Analysis on the dataset. For PCA, students analyse quantitative variables, produce and interpret relevant visualisations such as screeplots, biplots and loadings plots, and investigate the effects of Region and Healthcare System. The PCA analysis also requires comparison of the overall dataset with countries from Europe. The cluster analysis component requires students to cluster both countries and variables using different distance metrics and hierarchical clustering methods. Students compare methods such as Manhattan and Euclidean distances and single linkage and Ward’s method, present comparisons in compact tables, and interpret relevant dendrograms. They must then compare the conclusions obtained from PCA and Cluster Analysis, identifying common insights and apparent conflicts and discussing the extent to which the results are explainable rather than simply interpretable. Task 2 is an individual task focusing on Exploratory Data Analysis and Linear Models. Students create a scatter matrix using ggpairs(), investigate strongly correlated variables, and identify quantitative variables that may help predict Region for European and Asian countries. They then develop and critically assess linear regression models for predicting Score, including models based on employment variables and broader quantitative predictors. Model comparison and selection use concepts including AIC, while diagnostic plots are used to identify countries requiring further investigation. The individual task also requires students to use European Life-Work Balance Index 2023 data to make predictions for European countries not included in the 2025 dataset and to construct a Residuals versus Fitted Values plot. Finally, students must combine the conclusions from their individual linear modelling work with the PCA and Cluster Analysis findings to identify specific discoveries about the variables and countries in the dataset. R code, output and relevant plots must be included directly within the reports. The assignment encourages use of the R tidyverse and requires appropriate referencing of sources. The brief specifies APA-style referencing for the individual and group work. It also states that generative AI may be used for inspiration but not for generating answers or analysing the datasets, and any permitted AI use must be acknowledged and documented.
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Sustainable Development / Resource Management
6,000 words
Resource-Related Challenges in a Selected Country (STREAM) — Term Paper: India's Energy Sector, Coal Dependence and the Transition to Solar and Storage
This first-semester term paper for the STREAM programme takes a single country and a single resource sector and examines the challenges that arise where the two meet. India was selected for its energy sector — a case where scale, growth rate and an entrenched coal base make the tension between development need and environmental limit unusually sharp. The paper is written as an academic scientific text of roughly twenty pages, structured across the four sections the brief specifies and supported throughout by peer-reviewed literature, institutional reports and official statistical sources. The opening section establishes the country context: geographical position and climatic variation, population size and distribution, the shape and growth trajectory of the economy, development status against recognised indicators, and why the energy sector in particular is decisive for the country's near-term development path. The second section analyses the current status of the resource itself. Domestic coal reserves and their geographical concentration are set against renewable potential, particularly solar irradiance across the western and southern states. Production and consumption figures are traced over recent years, the import dependency for crude oil, gas and coking coal is quantified, and the major pressures are identified — demand growth outpacing capacity addition, the financial condition of distribution utilities, grid integration limits for variable generation, and the storage gap that constrains how far solar can displace baseload. The third section covers infrastructure and value chains. It describes the generation fleet, transmission and distribution network, and the logistics moving coal from pithead to plant, then follows the value chains attached to the resource — mining, power generation, equipment manufacturing and the growing domestic solar module and cell industry. Key stakeholders are mapped across central and state government, regulators, public and private generators, distribution companies, industrial consumers and the mining workforce whose livelihoods a transition directly affects. The final section addresses environmental problems and their connections to resource use. Ambient air quality and its public health burden, the water demand of thermal generation in already water-stressed basins, land degradation and displacement around mining regions, ash management and greenhouse gas emissions are each examined as consequences of the existing energy system rather than as separate issues. The section closes on future developments and risks: the plausible trajectories for renewable capacity and storage deployment, the stranded asset question for recently built thermal plants, and what a socially just transition would require of policy.
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