BUSI1125代做、代写Java/python程序语言

日期：2023-12-23 08:07

BUSI1125 Softwares and Tools for Data Analytics

INDIVIDUAL ASSIGNMENT

Autumn 2023/24

This individual assignment carries 100% of the total marks of this module.

Students are required to download 2 different datasets, and analyse each dataset using a

randomly assigned data analytics software.

Dataset 1 (poverty): Eradicating extreme poverty for all people everywhere by 2030 is a

pivotal goal of the 2030 Agenda for Sustainable Development. It has been recognised that

ending poverty must go hand-in-hand with strategies that build economic growth and address

a range of social needs including education, health, social protection, and job opportunities,

while tackling climate change and environmental protection. As a data analyst your objective

is to conduct an exploratory analysis to better understand the relationships/associations

between the level of income (outcome) and the selected socio-economic factors (features).

Dataset 1, extracted from the World Bank Development Indicators, includes the following

variables for 151 countries.

Variable Name Description

country Name of the country

region Region of the country

comp_edu Compulsory education, duration (years)

female_labour Ratio of female to male labour force participation rate (%)

agri_value_added Agriculture, forestry, and fishing, value added (% of GDP)

political_stability Political Stability and Absence of Violence/Terrorism: Estimated index

income_group Income group classification by the World Bank based on gross national

income (GNI) per capita (High income, Upper-middle income, Lower-

middle income, Low income)

Dataset 1 is available on the module Moodle page or download directly from:

https://raw.githubusercontent.com/mmchit/poverty/main/poverty.csv

Dataset 2 (wage): One of the other UN Sustainable Development Goals is about promoting

inclusive and sustainable economic growth, employment and decent work for all (Decent work

and Economic Growth). Decent work means opportunities for everyone to get work that is

productive and delivers a fair income, security in the workplace and social protection for

families, better prospects for personal development and social integration. As a data analyst

your objective is to conduct an exploratory analysis to better understand the

relationships/associations between the individual’s wage (outcome) and the selected

demographic factors (features).

Dataset 2, extracted from The United States National Longitudinal Surveys, includes the

following variables for 935 individuals.

Variable Name Description

wage Average weekly earnings (in US$)

hours Average weekly working hours

exper Years of working experience

age Age in years

marital Marital status (Married, Single)

gender Gender (Male, Female)

education Level of education (High School, College, Graduate, Post-Graduate)

Dataset 2 is available on the module Moodle page or download directly from:

https://raw.githubusercontent.com/mmchit/wage/main/wage.csv

Assignment requirements

Students are required to import the dataset and analyse with the assigned software (R or

Python). For descriptive and exploratory analytics and interpretations, students are required

to:

1. check data quality issues (missing values, data entry errors, inconsistencies, etc.),

perform necessary data cleansing, and briefly explain your data cleaning strategy.

2. identify the type of variables, provide appropriate summary statistics (all measures of

location and dispersion and frequencies) of each variables with appropriate

visualisations and interpretations.

3. identify the objectives of analytics based on the given dataset and scenario and identify

the relevant/appropriate relationships/associations between the outcome and feature

variables, conduct exploratory analysis with appropriate visualisations, and present

and interpret the analyses (based on DIKW pyramid).

4. write up a data analytics report with clear and effective communication.

The 1500-word assignment should include the following two sub-sections.

Section 1: Report of descriptive and exploratory analytics of Dataset 1 using the

assigned software with appropriate visualisations, and interpretations (around 750

words)

Section 2: Report of descriptive and exploratory analytics of Dataset 2 using the

assigned software with appropriate visualisations, and interpretations (around 750

words)

Students are also required to submit R-scripts and Jupyter Notebook files via Moodle

submission box.

Deadline Date for Submission of Coursework

Your coursework needs to be submitted electronically via the Module Moodle page. See the

Student Services website and the programme handbook for further details of this process.

The deadline for coursework submission is 3:30pm on Wednesday, 27th of December

2023. Late submission will attract marks deduction penalty unless an extension has been

approved by Student Services. Please familiarise yourself with the extenuating circumstances

policy and process for submitting a claim.

Five marks will be deducted for each working day (or part thereof) if coursework is submitted

after the official deadline without an extension having been obtained. Except in exceptional

circumstances, late submission penalties will apply automatically unless a claim for

extenuating circumstances is made before the assessment deadline.

Coursework Submission Requirements:

A maximum word count of the assignment is 1500 words and must be adhered to.

The penalty for exceeding this limit is a five mark deduction for exceeding up to 300

words, 10 marks deduction for exceeding between 301 and 500 words, and 15

marks reduction for exceeding over 501 words.

The actual word count of the assignment must be stated by the student on the first

page (cover sheet) of the assignment.

The overall word count does include citations and quotations.

The overall word count does not include the references or bibliography at the

end of the coursework.

The word count does not include figures and tables with numeric values and the titles

of figure and table. Any statement, interpretation, and explanation presented in

a figure or a tabular form will be included in the overall wordcount,

Appendices (mostly supporting materials that are not directly related to the assignment

and will not be considered in marking) are not included in the overall word count.

Students should prepare and submit their coursework assessments via Moodle in

the following format:

Font: Verdana 11 point

Spacing: 1.5 spaced

Margins: Normal (2.5 cm)

Referencing: Harvard citation style

Plagiarism will not be tolerated. Please consult the Business School Undergraduate Student

Handbook for more guidelines on how to present and submit your essays. It is the strong

advice of the Business School that you should avoid plagiarism by engaging in ethical and

professional academic practice.

In accordance with the University’s Quality Manual, in normal circumstances, marked

coursework and associated feedback will be returned to you within 15 working days of the

published submission deadline. Therefore, students submitting work before the published

deadline should not have an expectation that early submission will result in earlier return of

work. Where coursework will not be returned within 15 working days for good reason (for

example in circumstances where a student has been granted an extension, illness of module

convenor, or lengthy pieces of coursework), students will be informed of the timescale for the

return of the coursework and associated feedback.

Additional circumstances where coursework may not be returned within 15 working days for

good reason can include the University closure dates. Therefore, where this applies, you will

be informed in advance of the date coursework feedback will be provided to you.