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12-24-2022 Demonstrate a critical understanding of data management, manipulation, and modelling methods and techniques applied in an organisational context.
Learning Outcomes Assessed in this assessment
This assignment will assess the following learning outcomes:
Demonstrate a critical understanding of data management, manipulation, and modelling methods and techniques applied in an organisational context.
Critical appraisal of statistical and data science methods, techniques and tools applied for business intelligence.
Systematically identify and critically analyse data-related problems and develop robust solutions using data management, manipulation and data modelling methods, techniques and tools.
Critically demonstrate the use and application of statistical and data science techniques and tools in a problem scenario.
This assignment constitutes 100% towards the final mark for this module. Any queries relating to this assignment should be discussed with the module tutor:
Type of the submission required
This is an INDIVIDUAL piece of work contributing to the module assessment. Deliverables should be assembled into a single report document, which includes (critical appraisal, research, and snapshot evidence of tasks carried out and justification of technologies used). Submission will be in the form of an MS word report (4000 words).
Section 1: Statistical Data Analysis
The aim of the assignment is to apply different statistical methods to the dataset and analyse it through discovering new knowledge and data insight. You are required to analyse a data set “CarType” using SPSS or other statistical software, i.e., R / SAS (The dataset can be found in the Assessment folder). As a result of the analysis, you should answer the following business questions:
What type of car provides the most fuel economy?
What type of car is environmentally friendly?
Individual tasks which are required to be discussed in detail are presented below.
Task one: Domain Understanding and Research Questions (10%)
Describe the domain of research to which the dataset belongs. Evaluate the research area by using peer-reviewed academic articles, journals, and books (no websites). Explain what methods are described in the literature and what results are obtained by other researchers who studied a similar problem.
Formulate an appropriate hypothesis (NULL and alternative) from the research questions that can be tested via the different statistical methods.
Provide references used for this section in Harvard style.
Task two: Dataset and Data Preparation (10%)
Review and describe the dataset.
Appropriately prepare the data for analysis through data transformation and cleaning techniques. Give detailed justifications of what, why and how you have completed each stage of this process. Explain why you have chosen a particular method over others and what you got at the end of data pre-processing.
Support your explanation with screenshots.
Task three: Data Analytical Methods (20%)
Carefully consider which statistical methods are needed to answer your research questions and accept or reject the NULL hypothesis.
Apply appropriately chosen statistical methods which help to analyse your dataset and provide the results for further insight. For example, for descriptive statistics, use frequency, dispersion, central tendency and position measures such as mean, range, variance, standard deviation, percentiles, ranks, etc. You can add methods for correlation and regression analysis.
Present your results clearly and concisely. You might need to provide a correlation matrix to support your ideas. For acceptance or rejection of the NULL hypothesis, you have to calculate the p-value and explain the obtained results.
Design a relational database using either Chen’s notation or Crow Foot notation, capable of supporting the given business scenario. Your design should include Relationships and any participation constraints
Task four: Evaluation and Conclusion (10%)
You have to make a conclusion about the analyzed dataset and formulate the answers to the research questions.
Section 2: Data Modelling
Kangaroo is an online delivery company, which is looking for the development an effective Relational Database Management System (RDBMS) and data warehouse to satisfy the needs of the growing business. As a data analyst, you have been asked to develop a required system for Kangaroo.
Develop the system that satisfies the following business information requirements:
The database should contain information about Customers, Items, Restaurants, Orders, Drivers and their Vehicle. For payroll, the National Insurance (NI) number of the drivers is recorded.
For each customer, the database should store the Customer ID, Last Name, First Name, Email, and the Phone Number.
For each driver, their name, salary, email address and their manager are recorded as well as details of their Driving License such as Driving License Number, Issue Date, country of issue and Expiry Date.
Each driver is assigned a motorbike when they start with the company and they normally keep it during the duration of their contract. Details of the motorbike are registered such as Registration Number, colour, date of purchase, engine size
Each Manager manages at least one driver, and each driver is associated with one restaurant only, but one restaurant employs many drivers.
For each of the Restaurant, the Restaurant ID, Restaurant Name and Address are recorded.
For each Item, the Item ID, the Item Name and Item Price should be recorded. The items are divided into four categories such as Starter, Main Course, Deserts and Drinks. Prices for each item/Product may vary in different branches. For example, the Pizza Hut Croydon Branch sells Garlic Bread at £3.50 but the Oxford Street Branch charges £4.00 for the same item.
For each order, it is required to store the Order ID, Order date and the Products that have been ordered. A customer must order at least one item per order.
A customer can have one or more orders from the same or different restaurants.
A driver can deliver more than one order, but one driver delivers a particular order only.
Design a relational database using either Chen’s notation or Crow Foot notation, capable of supporting the given business scenario. Your design should include Relationships and any participation constraints.
Write MySQL code to implement your database design. You should document your code and use constraints, default values, ON DELETE clauses, etc., as appropriate for the business scenario. The use of wizards is prohibited.
Populate all the tables in the database you created with some data (At least 10 records in each table). The data should be meaningful but does not need to be extensive.
Support your work with screenshots.
Task Three: Explain how database design will support the business scenario. [15 Marks]
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