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Databases and Business Intelligence
Databases and Business Intelligence – KAP Speciality Chocolates Database Coursewor
This individual coursework for the Databases and Business Intelligence module provides practical experience of database systems through the realistic KAP Speciality Chocolates case study. Students are required to design and implement a relational database solution that supports the management of products, stock, suppliers and purchase orders for KAP Speciality Chocolates Ltd. The case study describes a growing chocolate business with a shop and a storeroom that requires a database system to reduce operational errors and improve stock control. The first task requires students to analyse the case study and identify appropriate entities, attributes, relationships, primary keys, foreign keys and dependencies. Students must produce an extended Entity Relationship Diagram showing how the identified elements should be related. The diagram must include participation and cardinality constraints, and students must clearly document the assumptions made and the notation used. A range of modelling notations and software tools may be used, provided that the required database elements are clearly represented. The second task requires students to translate the ER design into a relational database implementation by writing SQL Data Definition Language statements. The SQL must define suitable tables, attributes, data types, primary and foreign keys and relevant constraints. The chosen data types should be appropriate and foreign-key data types must correspond correctly to their related primary keys. The third task focuses on SQL Data Manipulation Language and requires students to create queries addressing a range of business information needs. These include producing a product price list, identifying purchase orders due for delivery on a specified day, generating shop re-stocking lists, changing product cost and selling prices, identifying products with outstanding purchase-order deliveries, producing a re-order list for products below their minimum warehouse stock level, and identifying suppliers that provide multiple cartons. Students must populate their tables with sufficient suitable data to demonstrate that the SQL works and provide evidence of testing through screen captures of SQL statements and results. The fourth task requires students to demonstrate that the database design is normalised to Third Normal Form (3NF). The normalisation process must begin with unnormalised data and show the stages through First Normal Form (1NF), Second Normal Form (2NF) and Third Normal Form (3NF), explaining the changes and reasons at each stage. The final normalised solution must remain consistent with the ER diagram, assumptions and entity and attribute names used in the database implementation. The final submission must combine all tasks into one report written in English, using Arial 12-point font and single spacing. The report must contain the extended ER diagram and assumptions, SQL table definitions, SQL queries with explanations and testing evidence, and a written explanation of the normalisation process. The completed report must be submitted as a single PDF through SurreyLearn by the specified deadline.
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Databases
1,000 words
Assessment #1 – Advanced Databases: NORTHERNTOURS Database Design and Implementation
This assessment for the Advanced Databases module (KL7011) focuses on the analysis, design and implementation of a database system based on the NORTHERNTOURS scenario, a fictitious transport company operating a fleet of luxury coaches across cities, towns and tourist locations in Northern England. The assessment requires students to demonstrate advanced database knowledge through conceptual modelling, logical database design, SQL implementation, data manipulation and the evaluation of alternative database technologies. The assessment addresses learning outcomes relating to the data life cycle, advanced data modelling and database design, as well as professional, legal, ethical, security, sustainability and risk considerations. The first part requires students to develop a conceptual database design for NORTHERNTOURS using Entity-Relationship (ER) or Enhanced Entity-Relationship (EER) modelling. The design should identify relevant entities, relationships, key attributes, primary keys and structural constraints. Students then convert the conceptual model into a logical relational schema using ER/EER-to-relational mapping, identify primary and foreign keys, ensure the relations satisfy Third Normal Form (3NF), select and justify a consistent naming convention, and produce a textual data dictionary containing relevant names, descriptions and constraints. The logical design is subsequently implemented using Oracle 11g, 12c or higher through appropriate SQL DDL statements and database constraints. The second part involves populating selected database relations with self-generated sample data and demonstrating database retrieval capabilities. Students must provide SQL DML statements, relational algebra expressions and SQL queries for specified NORTHERNTOURS business requirements. The solutions must be executed in a live Oracle environment and supported with appropriate output evidence. The third part extends the database analysis by considering object-relational and NoSQL database technologies. Students evaluate which aspects of the NORTHERNTOURS conceptual design could benefit from object-relational implementation, develop and populate a suitable object-relational subset, and demonstrate it through complex queries. They also analyse where NoSQL concepts could provide benefits and discuss design choices supported by representative NoSQL implementation code. Finally, students prepare a concise report for the NORTHERNTOURS managing director addressing sustainability, professional, legal, ethical and security issues, together with diversity, inclusion, cultural, societal and environmental considerations and commercial risk management. The report should use a critical review of relevant literature, systems, developments and standards and follow Harvard referencing conventions.
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Operations and Supply Chain Management
1,987 words
Supply Chain Analytics and Quantitative Data Analysis for Organisational Decision-Making
This postgraduate individual report focuses on the application of quantitative data analysis to Operations, Logistics and Supply Chain Management decision-making. Students are required to select an organisation from the private, public or third sector and investigate a relevant operational or supply-chain issue using quantitative data. The purpose is to demonstrate how data can be collected, prepared, analysed and interpreted to generate evidence-based insights that may support managerial decision-making. The selected dataset must relate to the organisation's operations or supply chain and may include variables such as revenues, product orders, sales, transportation costs, procurement expenditure, inventory levels or other appropriate quantitative measures. The dataset must contain at least 60 observations, and the analysis must involve at least two variables. Data may be obtained directly from organisations or from recognised secondary-data platforms and databases. The assessment consists of two equally weighted components. Part A – Motivation and Justification for the Analysis requires students to formulate relevant analytical questions and explain their practical importance by linking them to Operations and Supply Chain Management theory and business practice. Appropriate academic, industry and practitioner evidence should be used to justify the selected issue. Research questions may also be translated into testable hypotheses where appropriate. Part B – Execution of the Analysis requires students to answer the identified questions through appropriate statistical techniques. Potential methods include tables, charts, summary statistics, t-tests and regression analysis. Data may first need to be cleaned, transformed and structured before analysis. The results must then be interpreted clearly for a managerial audience such as the organisation's board, owner or CEO. The statistical analysis is expected to be conducted using Stata, with all data-cleaning, manipulation and analytical commands recorded in a reproducible do-file. The report must also demonstrate explicit links between theory and practice and contain a suitable mixture of academic and professional evidence, including at least five academic journal articles. Harvard referencing is required throughout. The resulting work demonstrates practical competence in business analytics, statistical interpretation, supply-chain decision support, reproducible analysis and evidence-based managerial communication. Overview word count: approximately 370 words.
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Cyber Security for Business and Cloud Management
This activity aims to assess your comprehension of the diverse concepts discussed in this module. You must use the frameworks and concepts covered in this module's delivery to respond to all the tasks below. Scenario ShieldSafe Analytics Ltd. is a fast-growing health analytics company specialising in AI-driven patient diagnostics and telehealth platforms. Operating across multiple countries, the company processes high volumes of real-time patient data, including biometric and genomic records. Due to the increased reliance on remote healthcare and IoT-enabled medical devices, their infrastructure has expanded into hybrid cloud environments. Recently, ShieldSafe experienced a suspected data exfiltration incident involving anomalous traffic from one of its diagnostic platforms integrated with third-party cloud APIs. As a result, executive leadership has raised concerns about the company’s vulnerability to adversarial information operations, particularly in relation to data manipulation, misinformation, and insider threats. As a Junior Cybersecurity Strategist, you’ve been recruited to support the lead cyber intelligence consultant in reviewing vulnerabilities within their information environment, exploring offensive and defensive Information Operations (IO) concepts, and crafting robust cyber defence mechanisms. The leadership also wants to migrate a legacy electronic health record (EHR) system used across its African operations to a more scalable and secure cloud infrastructure. However, concerns exist regarding cross-border data protection laws, insider threats, and the strategic use of information in potential cyber warfare scenarios.
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L7 Cyber Security for Business and Cloud Management
This activity aims to assess your comprehension of the diverse concepts discussed in this module. You must use the frameworks and concepts covered in this module's delivery to respond to all the tasks below. Scenario ShieldSafe Analytics Ltd. is a fast-growing health analytics company specialising in AI-driven patient diagnostics and telehealth platforms. Operating across multiple countries, the company processes high volumes of real-time patient data, including biometric and genomic records. Due to the increased reliance on remote healthcare and IoT-enabled medical devices, their infrastructure has expanded into hybrid cloud environments. Recently, ShieldSafe experienced a suspected data exfiltration incident involving anomalous traffic from one of its diagnostic platforms integrated with third-party cloud APIs. As a result, executive leadership has raised concerns about the company’s vulnerability to adversarial information operations, particularly in relation to data manipulation, misinformation, and insider threats. As a Junior Cybersecurity Strategist, you’ve been recruited to support the lead cyber intelligence consultant in reviewing vulnerabilities within their information environment, exploring offensive and defensive Information Operations (IO) concepts, and crafting robust cyber defence mechanisms. The leadership also wants to migrate a legacy electronic health record (EHR) system used across its African operations to a more scalable and secure cloud infrastructure. However, concerns exist regarding cross-border data protection laws, insider threats, and the strategic use of information in potential cyber warfare scenarios.
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