Rationale of Individual Project
This purpose serves to consolidate the learnings from the module and allow you to put the
concepts and principles into action by going through a replica of how to perform predictive
analytics and data mining on real data.
You are expected to apply all the necessary concepts and principles to a business case and
work with real data to analyse the data. You are also expected to apply real-world knowledge to analyse and derive insights into the dataset.
Project Requirement
It will be an open topic project that requires each student to submit a one-page executive
summary (.doc), a presentation deck of no more than 10 slides (.ppt), datasets used (.xlsx)
and a working R program file (.r). These should cover the following five parts of the project.
1. Pitch + Problem Statement
Pitch an idea for a data-driven project. Think of topics you’re passionate about, the knowledge you are familiar with, or problems relevant to your working industries. What questions do you want to answer?
2. Dataset + Data Collection
Source and collect the relevant data for your project. Data acquisition, transformation, and
cleaning are typically the most time-consuming parts of data science projects. Perform
preliminary data munging and cleaning of the data relevant to your project goals. Describe
your data keeping the intended audience of your final report in mind.
3. Data Exploration + Preliminary Analysis
Perform initial descriptive and visual analysis of your data. Identify outliers, summarise risks
and limitations, and describe how your data exploration will inform your modelling decisions.
4. Findings + Technical Report
Explain your goals, describe modelling choices, evaluate model performance, and discuss
results. Summarise your goals and metrics for success, variables of interest, and removal of
any outliers or data imputation. Your process description should be concise and relevant to
your goals. Summarise statistical analysis, including model selection, implementation,
evaluation, and inference. Clearly label the plots and visualisations. Include an Executive
Summary.
5. Presentation + Non-Technical Summary
Take your findings and prepare a 10-minute presentation that delivers the most important
insights from your project to a non-technical audience. Convey your goals,
limits/assumptions, methods and their justification, findings, and conclusions. Define
technical terms. Include graphics and visualisations.
Assessment Marks Allocation
The assignment will be evaluated using the following criteria:
1. Content: 60%
Students need to demonstrate an understanding of course concepts/ theories. They
need to identify connections and show application between textbook concepts/
theories and the case study above.
– A maximum of 60 marks will be awarded.
2. Delivery: 20%
The report should be balanced across topics. The student needs to show evidence of
professional preparation.
– A maximum of 20 marks will be awarded.
3. Organisation: 20%
The presentation must be arranged in a clear and logical order to maintain coherence.
– A maximum of 20 marks will be awarded.
Instructions
Cover Page
The cover page should include the institution name/logo, the programme, the course module, the trimester month & year, and the submission date. All these must be centralised on the page.
On the lower half of the cover page, print the students’ full names and the respective Student Numbers. Students are urged to keep a copy of the submitted assignment (final version) for their record.
Referencing
Use of proper referencing and citation.
Page Limit
One page for the executive summary. Ten slides for presentation.
Font and Spacing
Font size: 12 and 1 ½ or double spacing.
Plagiarism and Collusion
The submitted report must show evidence that this is the students’ own work. No marks will
be awarded if there are no workings or reasonable explanations. Please be reminded that
plagiarism and collusion are serious offences, and all cases will be referred to the
administration. Grades will be withheld if the submission is suspected of plagiarism or
collusion till investigations are completed.
Rationale of Individual Project
This purpose serves to consolidate the learnings from the module and allow you to put the
concepts and principles into action by going through a replica of how to perform predictive
analytics and data mining on
real data.
You are expected to apply all the necessary concepts and principles to a business case and
work with real data to analyse the data. You are also expected to apply real
–
world
knowledge
to analyse and derive insights into the dataset.
Project Req
uirement
It will be an open topic project that requires each student to submit a one
–
page executive
summary (.doc), a presentation deck of no more than 10 slides (.ppt), datasets used (.xlsx)
and a working R program file (.r). These should cover the follow
ing five parts of the project.
1.
Pitch + Problem Statement
Pitch an idea for a data
–
driven project. Think of topics you
’
re passionate about, the
knowledge
you are familiar with, or problems relevant to your working industries. What
questions do you
want t
o answer?
2.
Dataset + Data Collection
Source and collect the relevant data for your project. Data acquisition, transformation, and
cleaning are typically the most time
–
consuming parts of data science projects. Perform
preliminary data munging and cleaning
of the data relevant to your project goals. Describe
your data keeping the intended audience of your final report in mind.
3.
Data Exploration + Preliminary Analysis
Perform initial descriptive and visual analysis of your data. Identify outliers, summaris
e risks
and limitations, and describe how your data explora
tion will inform your modelling
decisions.
4.
Findings + Technical Report
Explain your goals, describe modelling choices, evaluate model performance, and discuss
results. Summarise your goals and m
etrics for success, variables of interest, and removal of
any outliers or data imputation. Your process description should be concise and relevant to
your goals. Summarise statistical analysis, including model selection, implementation,
evaluation, and inf
erence. Clearly label the plots and visualisations. Include an Executive
Summary.
5.
Presentation + Non
–
Technical Summary
Take your findings and prepare a 10
–
minute presentation that delivers the most important
insights from your project to a non
–
technical
audience. Convey your goals,
limits/assumptions, methods and their justification, findings, and conclusions. Define
technical terms. Include graphics and visualisations.
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Rationale of Individual Project
This purpose serves to consolidate the learnings from the module and allow you to put the
concepts and principles into action by going through a replica of how to perform predictive
analytics and data mining on real data.
You are expected to apply all the necessary concepts and principles to a business case and
work with real data to analyse the data. You are also expected to apply real-world
knowledge to analyse and derive insights into the dataset.
Project Requirement
It will be an open topic project that requires each student to submit a one-page executive
summary (.doc), a presentation deck of no more than 10 slides (.ppt), datasets used (.xlsx)
and a working R program file (.r). These should cover the following five parts of the project.
1. Pitch + Problem Statement
Pitch an idea for a data-driven project. Think of topics you’re passionate about, the
knowledge you are familiar with, or problems relevant to your working industries. What
questions do you want to answer?
2. Dataset + Data Collection
Source and collect the relevant data for your project. Data acquisition, transformation, and
cleaning are typically the most time-consuming parts of data science projects. Perform
preliminary data munging and cleaning of the data relevant to your project goals. Describe
your data keeping the intended audience of your final report in mind.
3. Data Exploration + Preliminary Analysis
Perform initial descriptive and visual analysis of your data. Identify outliers, summarise risks
and limitations, and describe how your data exploration will inform your modelling
decisions.
4. Findings + Technical Report
Explain your goals, describe modelling choices, evaluate model performance, and discuss
results. Summarise your goals and metrics for success, variables of interest, and removal of
any outliers or data imputation. Your process description should be concise and relevant to
your goals. Summarise statistical analysis, including model selection, implementation,
evaluation, and inference. Clearly label the plots and visualisations. Include an Executive
Summary.
5. Presentation + Non-Technical Summary
Take your findings and prepare a 10-minute presentation that delivers the most important
insights from your project to a non-technical audience. Convey your goals,
limits/assumptions, methods and their justification, findings, and conclusions. Define
technical terms. Include graphics and visualisations.