Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: daixieit

BMAN24621 Business Data Analytics

Coursework Project 2023/24

Context

Carbon dioxide (CO2) is the primary greenhouse gas emitted by human activities, such as burning fossil fuels, industrialisation, and deforestation, and it is viewed as a main driver of climate change. Understanding the data of CO2 emissions both globally and in different countries is crucial in developing climate policies and mitigating the impacts of climate change.

Description of the information and dataset

There are a wide variety of CO2 emissions data sources (including The United Nations Framework Convention on Climate Change, The World Bank Databank, The International Energy Agency and The US Energy Information Administration) used by researchers, policymakers, and the general public to (1) gain insight into the relative contributions of different countries to global CO2 emissions, (2) monitor changes in CO2 emissions over time, (3) investigate the relationship between CO2 emissions and economic growth, (4) evaluate the effectiveness of climate policies, etc.

This coursework project involves analysing a set of datasets collated from different CO2 emissions data sources.

· cumulative_co2_emissions.csv

· annual_co2_emissions_per_country.csv

· annual_co2_emissions_per_capita.csv

· gdp_per_capita_worldbank.csv

· climate_change_data.csv

· energy_consumption_profiles_by_country.csv

This table consists of the consumption data of different energy sources across the whole world and by individual countries.

Tasks of data analytics

In the individual coursework report, you are expected to describe in detail the use of relevant data analytics techniques to address the following analytical tasks.

- Briefly introduce the background of carbon emissions and climate change, and the potential of data analytics in tackling the challenge of climate change.

- Exploratory data analysis

o Briefly describe the given datasets and identify potential data quality issues.

o Identify top emitting countries in terms of the cumulative CO2 emissions and the most recent annual CO2 emissions.

o Explore how the trends of CO2 emissions change over time from the top emitters.

o Describe the trend and status quo of renewable energy development across the whole world or by any country you are interested in (from the dataset of energy_consumption_profiles_by_country.csv), as developing renewable energy is considered as an integral part of energy policy in many countries to reduce CO2 emissions, while maintaining economic growth.

- Clustering analysis

o Characterise the relationship between CO2 emissions per capita and GDP per capita in 2021 across all countries.  

o Cluster countries to an appropriate number of clusters in terms of CO2 emissions per capita and GDP per capita in 2021.

o Describe the key characteristics of the generated clusters.

- Predictive modelling

o Evaluate the correlations between climate change indicators, including temperature, CO2 concentrations, sea level rise, etc., in the dataset of climate_change_data.csv.

o Build at least two predictive models (including a simple linear regression, which can be used as a baseline for performance comparison) to predict the sea level rise from other climate change indicators,

o Evaluate the performance of the predictive models over training and validation data using appropriate error measures.

- Conclusions, assumptions, and limitations of the data analytics project.

Assessment and submissions

· Deadline for individual report submission: 3.00pm Friday 15^th December 2023.

· Complete a 3,500-word coursework report to

o Work out one-page analytical plan (a diagram with brief description), and

o Introduce your solution to tackle the above data analytics tasks and describe the analytical results in detail.

· Submit your report in one single document via Turnitin on Blackboard.

An indicative breakdown of marks is listed in the following table

Please refer to some general guidelines below for preparing your coursework report.

Some general guidelines for writing the coursework report

1. 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.

2. 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.

3. 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

4. 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.

o Title page (including Title, authors (your student ID number), date, etc.

o One page analytical plan

o The main body part of the report

5. 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).

6. You must submit your coursework report for this course to Blackboard no later than the date and time shown above.