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You are working in a small company that specialises in providing business data analytics solutions for consultancy. A company approached you and ask you to analyse a dataset.

Your assessment brief:

6.1 Guidance:

  • Summative assessment: Individual essay

This assignment requires you to apply the concepts and practices you have learnt about the big data analytics and visualisation in business with a real dataset. The assignment requires deep thinking and reflection of how the data analytics can be conducted properly and analyse results can be presented in an informative and vivid way. In addition, the data analyse should closely around the business value and solutions to provide innovative insights to business.

Key requirements:

Length: 3000 words (excluding tables, graphs, figures and the reference list).

Tasks: You are working in a small company that specialises in providing business data analytics solutions for consultancy. A company approached you and ask you to analyse a dataset. The company is a financial institute and it want to analyse a dataset of the mortgage in the USA in 2017. The company does not have any specific objective. Instead, the company wants you to carefully examine the dataset and explore any possible relationships amongst the variables in the dataset that have the significant business value.

Suggested structure: Your essay should consist of the sections below. We encourage you to follow it as much as possible. You need to include the following sections in the essay:

  1. Executive summary: provide the summary of the report, what is discussed in the essay.
  2. The idea: describe the business and products or services that have been targeted and outlines the contents of the report.
  3. The description of dataset: describes the dataset in the context why it is important to the organisation
  4. The description of the targeting variables you want to analyse: describe the targeting variables, which accurately modelled by using the correct syntax. The model is explained using a textual description.
  5. The description of method you want to adopt: clearly defined the analytics methods and what it is meant to accomplish.
  6. The analysis procedure: analysis plan is proposed and clearly defined.
  7. The analysis results: show the analysis results with visual evidence.
  8. The Business implications and potential business values: provide the description of business implications.
  9. Conclusion: summarises the report and contains clear and detailed instructions about problems to be avoided, and strategies that produced the most effective results.
  10. Reference: provide the reference with correct Harvard referencing format.

Example Answer

Executive Summary

This report explores the application of big data analytics and visualisation in the financial sector using a dataset of mortgage data from the USA in 2017. The analysis aims to uncover significant relationships among variables without a predefined objective, thereby offering innovative business insights. By leveraging appropriate data analytics methodologies, the study identifies key trends, correlations, and business implications that can support strategic decision-making within the financial industry. The report details the dataset, targeted variables, analytical approach, findings, and their potential business value.

The Idea

The financial industry, particularly mortgage lending institutions, relies heavily on data to assess risks, predict trends, and optimise operations. This study analyses a dataset from 2017 to extract meaningful patterns and relationships within the mortgage market. The focus is on evaluating borrower demographics, loan characteristics, approval patterns, and repayment behaviour. By identifying key insights, the analysis provides financial institutions with data-driven recommendations to enhance lending policies, risk assessment, and customer targeting.

The Description of Dataset

The dataset consists of mortgage-related data collected from various financial institutions across the USA in 2017. It includes variables such as loan amount, borrower income, credit score, interest rate, loan approval status, and property location. Understanding this dataset is crucial for financial institutions as it enables them to refine their risk models, improve lending strategies, and align their products with market demands. The data is comprehensive and structured, allowing for an in-depth exploration of factors influencing mortgage approvals and repayment behaviour.

The Description of the Targeting Variables

The analysis focuses on key variables that are critical in mortgage decision-making. These include borrower income, credit score, loan amount, interest rate, loan tenure, and approval status. The relationships among these variables are examined to determine how financial institutions assess risk and make lending decisions. Additionally, the influence of external factors such as location and economic conditions on mortgage approvals is investigated. The selection of variables ensures a well-rounded understanding of lending patterns and borrower profiles.

Continued...


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