BMAN24621 Business Data Analytics
Coursework Project
Context
The World Happiness Report (https://worldhappiness.report/) is a landmark survey of the state of global happiness. The first report was published in 2012, and the eighth one was released in March 2020, which ranks 153 countries by their happiness levels. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy decision-making. Leading experts across fields, such as economics, psychology, survey analysis, national statistics, data sciences, health and public policy, also aim to analyse how the measurements of happiness and well-being can be used effectively to assess the progress of nations toward achieving some key aspects of the 17 Sustainable Development Goals launched by the United Nations in 2015.
Description of the information and dataset
The happiness scores and rankings use data from the Gallup World Poll (GWP). The scores are based on the answer to the main life evaluation question asked in the poll. The question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are calculated from nationally representative samples and they are explained by the following factors,
• GDP per capita
• Social support
• Healthy life expectancy
• Freedom to make life choices
• Generosity
• Perceptions of corruption
• Happiness2020.xlsx
This file includes the world happiness evaluation data and it has the following columns:
Column
Description
Country name
Country name
Regional indicator
United Nations regional groups
Ladder score
Happiness score or subjective well-being, which represents the national average response to the main life evaluation question. The English wording of the question is “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”
Logged GDP per capita
The statistics of GDP per capita.
Social support
The national average of the binary responses (either 0 or 1) to the GWP question “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”
Healthy life expectancy
Healthy life expectancies at birth are based on the data extracted from the World Health Organization’s (WHO) Global Health Observatory data repository.
Freedom to make life choices
The national average of the responses to the GWP question “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”
Generosity
The residual of regressing the national average of response to the GWP question “Have you donated money to a charity in the past month?” on GDP per capita.
Perceptions of corruption
The national average of the survey responses to two questions in the GWP: “Is corruption widespread throughout the government or not?” and “Is corruption widespread within businesses or not?” The overall perception is just the average of the two 0-or-1 responses.
• PredictingHappiness2020.xlsx
This file is identical to Happiness2020.xlsx, except that the ladder scores are unknown for a list of imaginary countries.
Tasks of data analytics
In your individual coursework report, you are expected to describe the use of relevant data analytics techniques (introduced in this course unit and beyond) to address the following analytical tasks.
• Introduce the background and scope of the coursework project
• Exploratory data analysis
• Describe the given datasets in terms of its variables and data quality, etc.,
• Explore and compare the distributions of happiness scores by regional groups,
• Explore how each of the above six factors affects the happiness scores statistically and visually.
• Clustering analysis
• Apply clustering analysis to cluster countries to an appropriate number of clusters in terms of the happiness factors,
• Profile the key characteristics of the clusters of the countries.
• Predictive modelling
• Build at least two predictive models (a simple linear regression can be built as a benchmark for performance comparison) to predict the happiness scores for the list of imaginary countries in PredictingHappiness2020.xlsx.
• Use the following Mean Squared Error (MSE) or other appropriate error measures to evaluate the performance of the predictive models.
where N is the total number of countries in the training or validation dataset, is the happiness score for the ith country, while is the predicted happiness score for the country.
• Data analytics to inform public policy decision making
• Reflect on the impact of the Covid-19 pandemic on people’s wellbeing, discuss briefly a few post-Covid-19 public policies a country (you are interested in) can potentially make through referring to the above key factors, which lead to happiness.
• Highlight any key assumptions and limitations of the data analytics project.
Assessment and submissions
• Deadline for individual report submission: 3.00pm Friday 15th January 2021.
• Lay out one-page analytical plan first.
• Complete a 2000-word coursework report to detail the above data analytics tasks.
• Compile the one-page analytical plan and the full coursework report into one single document for submission via Turnitin on Blackboard.
Please refer to some general guidelines below for preparing your coursework report.
Some general guidelines for writing the coursework report
• Your work should be word processed, and visuals, like charts, pictures, etc., should be inserted into the document. A high standard of presentation and English are expected. The document should not contain typing, grammatical, or formatting errors (marks will be deducted for such errors). Avoid gimmicky graphics or overly-informal language and try to write in a scientific style (i.e. in the third person and past tense). The minimum font size allowed is Times Roman 10 and charts should be correctly formatted with appropriate labels, legends, etc.
• Try to avoid using dense paragraphs of text – use bullet points and tables where you can. Your report should be concise and ‘to the point’ and refer to source material where appropriate.
• You should imagine you are a data analyst writing a report to present and justify the development of your analytical solution. You do not need to submit project files generated by SAS and/or other analytical software tools, programming codes, documentation or user instructions
• The report should be well structured, with numbered pages, and include a title page, one page analytical plan and the main body part of the report.
• Title page (including Title, authors (your student ID number), date, etc.
• One page analytical plan
• The main body part of the report
• Any plagiarism from source/reference materials or others’ work will be penalised and may result in a mark of zero (please refer to your programme handbook).
• You must submit your coursework report for this course to Blackboard no later than the date and time shown above.
• An indicative breakdown of marks is listed in the following table
Main report
%
One page analytical plan
10
Introduction to the data analytics project
10
Exploratory data analysis
25
Clustering analysis
15
Predictive modelling
20
Discussions, assumptions and limitations
10
Structure and presentation
10