|
Assessment tasks
|
Learning
Outcome
Mapping
|
|
Assessment ID
|
Assessment Item
|
When due
|
Weighting
|
ULO#
|
|
Assessment 1:
Project Proposal (Group) (1000 words)
|
This is the assessment of the first project milestone. It is to develop and evaluate a project proposal focused on the business context and problem, data and information, data source, data analysis methodology, technical, ethical and other operational factors for the feasibility to deliver the expected analytics product within the given timeframe. You will complete this task in a group of two to three students.
|
Session 3
|
15%
|
1, 2
|
|
Assessment 2:
Exploratory Data Analysis Report (Group) (1000 Words)
|
This is the assessment of the second project milestone. You will work in the same group as Assessment Item 1. It is to assess, review and confirm what has been achieved and what can be achieved in future from working on the artefacts completed, which are primarily the outputs of exploratory data analysis (EDA). Adjustments or refinements to the analytics approach, data collection and analysis, and the related processes are suggested to feed forward the project. Any code and script that has been developed will be reviewed.
|
Session 6
|
15%
|
1,2
|
|
Assessment 3:
Code/Script Listing (Group)
Screenshots of Visual Outputs (Group)
Demonstration of
Solution (Individual)
|
This is an ongoing assessment but can be treated as the third project milestone. A large part of this assessment is focused on code and script development and other technical solutions. Peer reviews and lecturer’s guidance will be given, and emerging issues will be addressed continuously. you will be using popular tools such as Power BI, Tableau, Python or R for this task. Alternative approaches will also be discussed to achieve pragmatic and effective solutions. You will work in the same group as before and will be assessed on: (i) a listing of code/script, (ii) screenshots of visual outputs, and (iii) a 15-minute demonstration of code/script and solution per individual group member.
|
Session 10
|
30%
Code/Script 10%
Screenshots 10%
Demonstration
10%
|
1,2, 3,4, 5
|
|
Assessment 4*:
Final Project Report (Group) (1500 Words) Presentation
(Individual)
|
This is the assessment of the last project milestone. Again, you will continue to work in the same group as before. The focus is on the upcoming deliverables and resolving residual gaps and issues. Assumptions, limitations and potential misunderstandings by the expected users of the data analytics application are well acknowledged. Project deliverables including analytical dashboard, insights together with valid interpretations, business impact valuations and user instructions will be submitted in a group report that addresses to a wide range of stakeholders especially those who are non-IT professionals. Higher achievements such as automation, deep level insights and big potential business impacts are strongly encouraged. Invoking relevant contemporary issues in application such as ethics and potential impact on society will be duly awarded. Each group member will be required to make an individual presentation.
Each group member is also required to complete (i) a peer review report and (ii) a self-reflective report as individual submission.
|
Session 12
|
40%
Report 30%
presentaion10%
|
1, 2,3, 4, 5
|
Note: * denotes ‘Hurdle Assessment Item’ that students must achieve at least 40% in this item to passthe unit and 50% in overall assessments to pass the unit
Referencing guides
You must reference all the sources of information you have used in your assessments. Please use the IEEE referencing style when referencing your assessments in this unit. Refer to the library’s reference guides for more information.
Academic misconduct
VIT ensures that the integrity of its students’ academic studies follows an acceptable level of excellence. VIT will adhere to its VIT Policies, Procedures and Forms where it explains the importance of staff and student honesty in relation to academic work. It outlines the kinds of behaviors that are “academic misconduct”, including plagiarism.
Late submissions
In cases where there are no accepted mitigating circumstances as determined through VIT Policies, Procedures and Forms, late submission of assessments will lead automatically to the imposition of a penalty. Penalties will be applied as soon asthe deadline is reached.
Short extensions and special consideration
Special Consideration is a request for:
Extensions of the due date for an assessment, other than an examination (e.g. assignment extension).
Special Consideration (Special Consideration in relation to a Completed assessment, including an end-of-unit Examination).
Students wishing to request Special Consideration in relation to an assessment the due date of which has not yet passed must engage in written emails to the teaching team to Request for Special Consideration as early as possible and prior to start time of the assessment due date, along with any accompanying documents, such as medical certificates.
For more information, visit VIT Policies, Procedures and Forms.
Inclusive and equitable assessment
Reasonable adjustment in assessment methods will be made to accommodate students with a documented disability or impairment. Contact the unit teaching team for more information.
Contract Cheating
Contract cheating usually involves the purchase of an assignment or piece of research from another party. This may be facilitated by a fellow student, friend or purchased on a website. Other forms of contract cheating include paying another person to sit an exam in the student’s place.
Contract cheating warning:
By paying someone else to complete your academic work, you don’t learn as much as you could have if you did the work yourself.
You are not prepared for the demands of your future employment.
You could be found guilty of academic misconduct.
Many of for pay contract cheating companies recycle assignments despite guarantees of “original, plagiarism-free work” so similarity is easily detected by TurnitIn. Penalties for academic misconduct include suspension and exclusion.
Students in some disciplines are required to disclose any findings of guilt for academic misconduct before being accepted into certain professions (e.g., law).
You might disclose your personal and financial information in an unsafe way, leaving yourself open to many risks including possible identity theft.
You also leave yourself open to blackmail – if you pay someone else to do an assignment for you, they know you have engaged in fraudulent behaviour and can always blackmail you.
Grades
We determine your gradesto the following Grading Scheme:
|
Grade
|
Percentage
|
|
A
|
80% – 100%
|
|
B
|
70% – 79%
|
|
C
|
60% – 69%
|
|
D
|
50% – 59%
|
|
F
|
0% – 49%
|
Assessment Details for Assessment Item 1:
|
Assessment tasks
|
Learning
Outcome
Mapping
|
|
Assessment ID
|
Assessment Item
|
When due
|
Weighting
|
ULO#
|
|
Assessment 1:
Project Proposal (Group) (1000
words)
|
This is the assessment of the first project milestone. It is to develop and evaluate a project proposal focused on the business context and problem, data and information, data source, data analysis methodology, technical, ethical and other operational factors for the feasibility to deliver the expected analytics product within the given timeframe. You will complete this task in a group of two to three students.
|
Session 3
|
15%
|
1, 2
|
Assessment 1: Project Proposal
Overview
|
Weight
|
Length
|
Due date
|
ULO
|
|
15%
|
(1000 words)
|
Session 3
|
1, 2
|
Introduction
This assessment item relates to the unit learning outcomes as in the unit descriptor. : The objective of this Project Proposal is to assess the ability of students to understand large data sets and apply their knowledge in analyticsto come up with useful insights.
You are provided with historical sales data for 45 stores located in different regions – each store contains a few departments. The company also runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks.
Collect the “Online Retail” dataset from Kaggle Repository . Carefully observe the dataset and apply analytics to find answers for the below queries
Task
1. Define the Business Context and Problem:
o Write an overview of Company, highlighting its market position, product range, and operational structure (100 words). o Describe the specific business problem you aim to address, detailing the impact on sales performance and profit margins (100 words). 2. Identify and Describe Data and Information Requirements:
o List the types of data required for the analysis (e.g., historical sales data, inventory data, customer orders) (100 words). o Explain the sources of these data types and how they will be accessed (100 words).
3. Outline the Data Analysis Methodology:
o Detail the analytical techniques to be used (e.g., outlier detection using statistical methods, profit margin analysis) (100 words). o Discuss the tools and technologies that will be employed (e.g., data visualization tools like Tableau) (100 words). 4. Assess Technical Feasibility:
o Assess the technical requirements such as hardware and software needed for the project (100 words).
o Evaluate the availability of necessary technical skills and resources within the team (100 words).
5. Discuss Ethical and Operational Factors:
o Highlight ethical considerations, including data privacy and consent issues (100 words).
o Discuss operational factors such as resource availability and organizational support for the project (100 words).
Submission Instructions
All submissions are to be submitted through Turnitin. Drop-boxes linked to Turnitin will be set up in Moodle. Assessments not submitted through these drop- boxes will not be considered. Submissions must be made by the end of session 3.
The Turnitin similarity score will be used to determine any plagiarism of your submitted assessment. Turnitin will check conference websites, Journal articles, online resources, and your peer’s submissions for plagiarism. You can see your Turnitin similarity score when you submit your assessments to the appropriate drop-box. If your similarity score is of concern, you can change your assessment and resubmit. However, re-submission is only allowed before the submission due date and time. You cannot make re-submissions after the due date and time have elapsed.
Note: All work is due by the due date and time. Late submissions will be penalized at 20% of the assessment final grade per day, including weekends.
Marking Criteria/Rubric
You will be assessed on the following marking criteria/Rubric:
|
Assessment criteria
|
Exceptional >=80%
|
Admirable 70% – 79%
|
Creditable 60% – 69%
|
Acceptable 50% –
59%
|
Unsatisfactory <=49
|
|
1. Define the Business Context and
Problem(3)
|
Clear, concise, and comprehensive
overview;
thoroughly
addresses market
position, product
range, and
operational
structure; specific
business problem
with detailed impact on sales and profit margins.
|
Clear and mostly
comprehensive
overview; addresses market position,
product range, and operational
structure; specific
business problem
with impact on sales and profit margins.
|
Adequate overview; addresses most
aspects of market position, product range, and
operational
structure; business problem with some detail on impact.
|
Basic overview;
addresses some
aspects of market position, product
range, and
operational structure; general business
problem with
minimal detail on impact.
|
Incomplete or
unclear overview; insufficient or vague details on market position, product
range, and
operational
structure; business problem lacks clarity and impact details.
|
|
2. Identify and
Describe Data and
Information
Requirements(3)
|
Comprehensive and relevant list of data types; clear and
detailed explanation of data sources and access methods.
|
Relevant list of data types; clear
explanation of data sources and access methods.
|
Adequate list of data types; mostly clear explanation of data sources and access methods.
|
Basic list of data
types; some
explanation of data sources and access methods.
|
Incomplete or
unclear list of data types; insufficient or vague explanation of data sources and
access methods.
|
|
3. Outline the Data
Analysis
Methodology(3)
|
Detailed and well
justified analytical techniques;
comprehensive
discussion of tools and technologies to be used.
|
Mostly detailed and justified analytical techniques; clear
discussion of tools and technologies to be used.
|
Adequate analytical techniques with
minor gaps in
justification;
discussion of tools and technologies with some detail.
|
Basic analytical
techniques with
several gaps in
justification; minimal discussion of tools and technologies.
|
Incomplete or poorly justified analytical techniques;
insufficient
discussion of tools and technologies.
|
|
4. Assess Technical
Feasibility(3)
|
Thorough
assessment of
technical
requirements; clear evaluation of
technical skills and resources within the team.
|
Clear assessment of technical
requirements; mostly clear evaluation of technical skills and resources within the team.
|
Adequate
assessment of
technical
requirements;
evaluation of
technical skills and resources with some detail.
|
Basic assessment of technical
requirements;
minimal evaluation of technical skills and resources.
|
Incomplete or
unclear assessment of technical
requirements;
insufficient
evaluation of
technical skills and resources.
|
|
5. Discuss Ethical and Operational Factors(3)
|
Comprehensive
discussion of ethical considerations;
thorough discussion of operational
factors including
resource availability and organizational support.
|
Clear discussion of ethical
considerations; clear discussion of
operational factors including resource availability and
organizational
support.
|
Adequate discussion of ethical
considerations;
discussion of
operational factors with some detail.
|
Basic discussion of ethical
considerations;
minimal discussion of operational factors.
|
Incomplete or
unclear discussion of ethical
considerations;
insufficient
discussion of
operational factors.
|
Assessment Details for Assessment Item 2:
Overview
|
Assessment tasks
|
Learning
Outcome
Mapping
|
|
Assessment ID
|
Assessment Item
|
When due
|
Weighting
|
ULO#
|
|
Assessment 2: Exploratory Data Analysis Report (Group) (1000 Words)
|
This is the assessment of the second project milestone. You will work in the same group as Assessment Item 1. It is to assess, review and confirm what has been achieved and what can be achieved in future from working on the artefacts completed, which are primarily the outputs of exploratory data analysis (EDA). Adjustments or refinements to the analytics approach, data collection and analysis, and the related processes are suggested to feed forward the project. Any code and script that has been developed will be reviewed.
|
Session 6
|
15%
|
1,2
|
Introduction
This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment evaluates the progress and achievements of your group’s project since the first milestone. Building on the foundational work done in Assessment Item 1, this milestone focuses on assessing, reviewing, and confirming the outputs from your exploratory data analysis (EDA). Your task is to critically evaluate the artefacts produced, suggest necessary adjustments or refinements, and provide insights into future steps. This milestone aims to ensure that your analytics approach, data collection methods, and analysis processes are on the right track to achieve the project’s goals.
Task
Assessment Item 2: Exploratory Data Analysis Report (Group)
∙ Due: Session 6
∙ Weighting: 15%
∙ Word Limit: 1000 words
Description: Assess, review, and confirm the initial findings from exploratory data analysis (EDA) on the sales and supply chain data collected. The report should cover:
1. Document Data Collection and Preparation Steps:
o Provide a summary of the data collected, including the data sources and types (125 words).
o Describe the data cleaning and preprocessing steps taken to prepare the data for analysis (125 words).
2. Present Key Findings from EDA:
o Highlight the major findings from the EDA, including sales trends, profit margins, and any identified outliers (150 words). o Include visualizations (e.g., charts, graphs) to support the findings (150 words).
3. Propose Adjustments and Refinements:
o Suggest adjustments to the analytics approach based on the EDA findings (125 words).
o Recommend refinements to data collection and analysis processes to improve accuracy and insights (125 words).
4. Review and Document Code/Scripts:
o Review the code/scripts developed for the EDA (100 words).
o Provide documentation and comments on the code/scripts for clarity and future reference (100 words).
Based on your review you need to submit a report in IEEE format; see the word file in Moodle. Submit your report in a word or pdf format. Your report should be limited to 1000 words.
Submission Instructions
All submissions are to be submitted through Turnitin. Drop-boxes linked to Turnitin will be set up in Moodle. Assessments not submitted through these drop-boxes will not be considered. Submissions must be made by the end of session 6.
The Turnitin similarity score will be used to determine any plagiarism of your submitted assessment. Turnitin will check conference websites, Journal articles, online resources, and your peer’s submissions for plagiarism. You can see your Turnitin similarity score when you submit your assessments to the appropriate drop-box. If your similarity score is of concern, you can change your assessment and resubmit. However, re submission is only allowed before the submission due date and time. You cannot make re-submissions after the due date and time have elapsed.
Note: All work is due by the due date and time. Late submissions will be penalized at 20% of the assessment final grade per day, including weekends.
Marking Criteria/Rubric
You will be assessed on the following marking criteria/Rubric:
|
Assessment criteria
|
Exceptional >=80%
|
Admirable 70% – 79%
|
Creditable 60% – 69%
|
Acceptable 50% – 59%
|
Unsatisfactory <=49
|
|
Document Data
Collection and
Preparation Steps
|
Summary of Data Collected: Clear, concise, and
comprehensive
summary of data
sources and types; thorough
understanding
demonstrated. Data Cleaning and
Preprocessing:
Detailed and well justified description of cleaning and
preprocessing steps; ensures data
readiness for analysis.
|
Summary of Data Collected: Clear and mostly comprehensive summary of data
sources and types; good understanding demonstrated. Data Cleaning and
Preprocessing:
Mostly detailed and justified description of cleaning and
preprocessing steps; ensures data
readiness for analysis.
|
Summary of Data Collected:
Adequate summary of data sources and types; some
understanding
demonstrated. Data Cleaning and
Preprocessing: Adequate
description of
cleaning and
preprocessing
steps; some gaps in justification.
|
Summary of Data Collected: Basic summary of data sources and types; minimal
understanding
demonstrated. Data Cleaning and
Preprocessing:
Basic description of cleaning and
preprocessing
steps; several gaps in justification.
|
Summary of Data Collected:
Incomplete or
unclear summary of data sources and types; insufficient understanding
demonstrated. Data Cleaning and
Preprocessing:
Incomplete or poorly justified description of cleaning and
preprocessing steps.
|
|
Present Key Findings from EDA
|
Major Findings:
Comprehensive
and insightful
presentation of key findings; clear
identification of
sales trends, profit margins, and
outliers.
Visualizations:
Excellent and
relevant
visualizations;
effectively support the findings and
|
Major Findings:
Clear
presentation of
key findings;
good
identification of
sales trends,
profit margins,
and outliers.
Visualizations:
Good and
mostly relevant
visualizations;
support the
findings well.
|
Major Findings: Adequate
presentation of
key findings; some identification of
sales trends, profit margins, and
outliers.
Visualizations:
Adequate
visualizations;
somewhat support the findings.
|
Major Findings: Basic presentation of key findings;
minimal
identification of
sales trends, profit margins, and
outliers.
Visualizations:
Basic visualizations; minimal support for the findings.
|
Major Findings: Incomplete or
unclear
presentation of key findings; insufficient identification of
sales trends, profit margins, and
outliers.
Visualizations:
Incomplete or
irrelevant
visualizations; do not support the
findings effectively.
|
| |
enhance
understanding.
|
|
|
|
|
|
Propose
Adjustments and Refinements
|
Adjustments to
Analytics
Approach: Well
justified and
actionable
suggestions
based on EDA
findings; clear
rationale
provided.
Refinements to
Data Collection
and Analysis:
Comprehensive
and insightful
recommendations ; aim to improve
accuracy and
insights
significantly.
|
Adjustments to
Analytics Approach: Clear and relevant suggestions based on EDA findings; good rationale provided. Refinements to Data Collection and
Analysis: Clear and relevant
recommendations; aim to improve
accuracy and
insights.
|
Adjustments to Analytics
Approach:
Adequate
suggestions based on EDA findings; some rationale
provided.
Refinements to Data Collection and Analysis: Adequate recommendations; some potential for improving accuracy and insights.
|
Adjustments to Analytics
Approach: Basic suggestions based on EDA findings; minimal rationale provided.
Refinements to Data Collection and Analysis: Basic recommendations; minimal potential for improving accuracy and insights.
|
Adjustments
to Analytics
Approach:
Incomplete or
unclear
suggestions;
insufficient
rationale
provided.
Refinements
to Data
Collection
and Analysis:
Incomplete or
irrelevant
recommendati
ons;
insufficient
potential for
improving
accuracy and
insights.
|
|
Review and
Document
Code/Scripts
|
Review of
Code/Scripts:
Thorough and
insightful review;
clear identification of strengths and
areas for
improvement.
Documentation
|
Review of
Code/Scripts: Clear review; good
identification of
strengths and areas for improvement. Documentation and Comments: Clear documentation; well annotated
|
Review of
Code/Scripts:
Adequate review; some identification of strengths and areas for improvement. Documentation and Comments:
Adequate
|
Review of
Code/Scripts: Basic review; minimal
identification of
strengths and areas for improvement. Documentation and
Comments: Basic documentation;
|
Review of
Code/Scripts:
Incomplete or
unclear review;
insufficient
identification of
strengths and
areas for
improvement.
|
| |
and Comments:
Comprehensive
and clear
documentation;
well-annotated
code/scripts for
future reference.
|
code/scripts for future reference.
|
documentation;
some annotations in code/scripts for
future reference.
|
minimal annotations in code/scripts for future reference.
|
Documentatio
n and
Comments:
Incomplete or
unclear
documentation
; insufficient
annotations in
code/script.
|
Assessment Details for Assessment Item 3: Retail Sales Data Analysis
Overview
|
Assessment tasks
|
Learning
Outcome
Mapping
|
|
Assessment
ID
|
Assessment Item
|
When due
|
Weighting
|
ULO#
|
|
Assessment3: Code/Script
Listing (Group) Screenshots of Visual Outputs (Group)
Demonstration of Solution
(Individual)
|
This is an ongoing assessment but can be treated as the third project milestone. A large part of this assessment is focused on code and script development and other technical solutions. Peer reviews and lecturer’s guidance will be given, and emerging issues will be addressed continuously. you will be using popular tools such as Power BI, Tableau, Python or R for this task. Alternative approaches will also be discussed to achieve pragmatic and effective solutions. You will work in the same group as before and will be assessed on: (i) a listing of code/script, (ii) screenshots of visual outputs, and (iii) a 15-minute demonstration of code/script and solution per individual group member.
|
Session 10
|
30%
Code/Script
10%
Screenshots 10%
Demonstration 10%
|
1,2, 3,4, 5
|
Introduction
The third project milestone builds upon your group’s progress and focuses on the development of technical solutions through code and script creation. This ongoing assessment emphasizes practical application and technical proficiency using popular tools such as Power BI, Tableau, Python, or R. Alternative approaches will also be explored to ensure pragmatic and effective solutions. Continuous peer reviews and lecturer guidance will help address emerging issues and refine your approach.
Task 1: Exploratory Data Analysis (EDA)
Objective: Analyse historical sales data for 45 stores to identify trends, patterns, and factors influencing sales performance.
Overview of Data:
∙ Explore the structure and contents of the provided dataset.
∙ Identify variables in each tab (Stores, Features, Sales) and their significance for analysis.
Historical Sales Analysis:
∙ Analyze sales trends and patterns over time (2010-02-05 to 2012-11-01).
∙ Identify seasonal variations, sales peaks, and dips.
Store-wise Analysis:
∙ Identify stores with the highest and lowest sales revenue.
Task 2: Predictive Modeling
Objective: Develop predictive models to forecast future sales, predict the impact of markdown events, and predict holiday sales performance.
Sales Forecasting:
∙ Develop time-series forecasting models to predict future sales for each store and department.
∙ Evaluate model performance using appropriate metrics (e.g., Accuracy).
Holiday Sales Prediction:
∙ Develop models to predictsales performance during prominent holidays (Super Bowl, Labor Day, Thanksgiving, Christmas). Key Components for Submission:
1. Code/Script Listing (Group):
o Provide a comprehensive listing of all code and scripts developed by the group.
o Ensure that the code is well-organized, follows best practices, and includes comments for clarity and future reference. o This component will account for 10% of the total assessment grade.
2. Screenshots of Visual Outputs (Group):
o Include clear and relevant screenshots of visual outputs generated using the tools.
o Ensure that the visualizations effectively communicate key findings and insights.
o This component will also account for 10% of the total assessment grade.
3. Demonstration of Solution (Individual):
o Each group member will individually demonstrate their contribution to the project.
o The demonstration should last 15 minutes and cover the code/scripts developed, the visual outputs, and the overall solution. o This component will account for another 10% of the total assessment grade.
Submission Instructions
Submission Instructions All submissions are to be submitted through turn-it-in. Drop-boxes linked to turn-it-in will be set up in the Unit of Study Moodleaccount. Assignments not submitted through these drop-boxes will not be considered. Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made after the due date and time will be penalized at the rate of 20% per day (including weekend days). The turn-it-in similarity score will be used in determining the level if any of plagiarism. Turn-it-in will check conference websites, Journal articles, the Web and your own class member submissions for plagiarism. You can see your turn-it-in similarity score when you submit your assignment to the appropriate drop-box. If this is a concern you will have a chance to change your assignment and re-submit. However, re-submission is only allowed prior to the submission due date and time. After the due date and time have elapsed you cannot make re-submissions and you will have to live with the similarity score as there will be no chance for changing. Thus, plan early and submit early to take advantage of this feature. You can make multiple submissions, but please remember we only see the last submission, and the date and time you submitted will be taken from that submission. Instruction: You are required to submit 2500± 10% words report (word/pdf file) on the below tasks. Use appropriate headings and subheading in your report. Please note that only group leaders will submit the file.
Note: All work is due by the due date and time. Late submissions will be penalized at 20% of the assessment final grade per day, including weekends.
Marking Criteria/Rubric:
You will be assessed on the following marking criteria/Rubric:
|
Assessment Criteria
|
Exceptional (>=80%)
|
Admirable (70% – 79%)
|
Creditable (60% – 69%)
|
Acceptable (50% – 59%)
|
Unsatisfactory
(<=49%)
|
|
Code/Script Listing
(10%)
|
Comprehensiveness: Thorough and detailed code/scripts covering all aspects of the task.
Organization: Well
structured and follows best practices.
Comments: Extensive and clear comments for readability and future reference.
|
Comprehensiveness: Detailed code/scripts covering most aspects of the task. Organization: Mostly well-structured and follows best
practices. Comments: Clear comments for
readability and future reference.
|
Comprehensiveness: Adequate code/scripts covering some aspects of the task.
Organization:
Somewhat structured and follows some best practices. Comments:
Sufficient comments for readability.
|
Comprehensiveness: Basic code/scripts
covering limited
aspects of the task. Organization:
Minimally structured with few best
practices. Comments: Minimal comments for readability.
|
Comprehensivenes s: Incomplete or
unclear code/scripts covering few aspects of the task.
Organization: Poorly structured with little adherence to best practices.
Comments:
Insufficient or no
comments.
|
|
Screensh
ots of
Visual
Outputs
(10%)
|
Relevance: Highly
relevant and insightful visual outputs. Clarity: Clear, well-labeled, and professional-quality
visuals. Support:
Effectively support the key findings and insights.
|
Relevance: Mostly
relevant and insightful visual outputs. Clarity: Clear and mostly well
labeled visuals. Support: Support the key findings and insights well.
|
Relevance: Somewhat relevant visual outputs. Clarity: Adequately clear and labeled
visuals. Support:
Support the key
findings and insights adequately.
|
Relevance: Basic
visual outputs with
limited relevance.
Clarity: Minimally
clear and labeled
visuals. Support:
Minimal support for the key findings and insights.
|
Relevance: Irrelevant or unclear visual
outputs. Clarity: Poorly labeled or unclear visuals. Support: Do not support the key findings and insights effectively.
|
|
Demonstr
ation of
Solution
(10%)
|
Coverage: Thorough and clear demonstration
covering all aspects of the solution. Presentation: Highly professional and engaging presentation. Effectiveness:
Demonstrates a deep understanding and
|
Coverage: Clear
demonstration covering most aspects of the
solution. Presentation: Professional and
engaging presentation. Effectiveness:
Demonstrates good
understanding and
|
Coverage: Adequate demonstration covering some aspects of the solution. Presentation: Sufficiently professional presentation.
Effectiveness:
Demonstrates basic understanding and use
|
Coverage: Basic
demonstration covering limited aspects of the solution. Presentation: Minimally professional presentation.
Effectiveness:
Demonstrates minimal understanding and use
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Coverage: Incomplete or unclear
demonstration.
Presentation:
Unprofessional or
unclear presentation. Effectiveness:
Demonstrates
insufficient
|
| |
effective use of tools and methods.
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effective use of tools and methods.
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of tools and methods.
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of tools and methods.
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understanding and use of tools and
methods.
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Assessment 4: Final Project Report and Presentation
Overview
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Assessment tasks
|
Learning
Outcome
Mapping
|
|
Assessment ID
|
Assessment Item
|
When due
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Weighting
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ULO#
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Assessment 4*:
Final Project Report (Group) (1500 Words) Presentation
(Individual)
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This is the assessment of the last project milestone. Again, you will continue to work in the same group as before. The focus is on the upcoming deliverables and resolving residual gaps and issues. Assumptions, limitations and potential misunderstandings by the expected users of the data analytics application are well acknowledged. Project deliverables including analytical dashboard, insights together with valid interpretations, business impact valuations and user instructions will be submitted in a group report that addresses to a wide range of stakeholders especially those who are non-IT professionals. Higher achievements such as automation, deep level insights and big potential business impacts are strongly encouraged. Invoking relevant contemporary issues in application such as ethics and potential impact on society will be duly awarded. Each group member will be required to make an individual presentation.
Each group member is also required to complete (i) a peer review report and (ii) a self-reflective report as individual submission.
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Session 12
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40%
Report 30%
presentaion10%
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1, 2,3, 4, 5
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Introduction
This final project milestone focuses on completing and refining your group’s work, addressing any residual gaps and issues, and preparing your project deliverables for submission. Each group will produce a comprehensive report and each member will deliver an individual presentation. Additionally, individual peer review and self-reflective reports will be submitted.
Group Tasks:
1. Final Project Report (1500 Words)
o Executive Summary:
Provide a concise overview of the project, including objectives, methods, and key findings.
o Introduction:
Describe the background and context of the project.
State the problem or opportunity addressed by the project.
o Methodology:
Detail the data collection, analysis methods, and tools used.
Explain any assumptions, limitations, and potential misunderstandings of the data analytics application.
o Analytical Dashboard and Insights:
Present the final analytical dashboard with key insights and interpretations.
Discuss the business impact valuations derived from the analysis.
o User Instructions:
Provide clear instructions for users, especially non-IT professionals, on how to use the analytical dashboard and interpret the insights. o Discussion on Ethics and Contemporary Issues:
Discuss relevant ethical considerations and potential societal impacts of the data analytics application.
o Conclusion and Recommendations:
Summarize the key findings and insights.
Provide recommendations for future work or application of the project results.
2. Project Deliverables:
o Analytical Dashboard:
Ensure the dashboard is user-friendly and provides deep-level insights with significant business impact.
o Business Impact Valuations:
Clearly articulate the potential business impacts derived from the analytics.
o Automation: Where possible, include automation features in the data analytics application.
Individual Tasks:
1. Presentation:
o Each group member will deliver a 10-minute presentation covering their contribution to the project.
o The presentation should include:
An overview of the individual’s role and tasks within the project.
Key findings and insights from the analysis.
Demonstration of how the analytical dashboard works.
Discussion of any challenges faced and how they were overcome.
Reflection on the project’s impact and future implications.
2. Peer Review Report:
o Complete a report evaluating the contributions and performance of each group member. o Include criteria such as collaboration, quality of work, and adherence to deadlines.
3. Self-Reflective Report:
o Reflect on your own contributions, learning experiences, and overall performance throughout the project. o Discuss personal challenges, growth, and areas for improvement.
Submission Components:
1. Group Submission:
o Final Project Report (1500 words)
o Analytical Dashboard
o Business Impact Valuations
o Automation Features (if applicable)
2. Individual Submission:
o Presentation (10 minutes per member)
o Peer Review Report
o Self-Reflective Report
Evaluation Criteria:
The assessment will be evaluated based on the following criteria:
1. Group Report:
o Clarity and completeness of the executive summary, introduction, and methodology.
o Depth and relevance of insights presented in the analytical dashboard.
o Quality and clarity of user instructions.
o Thoughtfulness and relevance of the discussion on ethics and contemporary issues.
o Overall organization, coherence, and quality of writing.
2. Project Deliverables:
o Functionality and user-friendliness of the analytical dashboard.
o Depth of insights and business impact valuations.
o Presence and effectiveness of automation features (if applicable).
3. Individual Presentation:
o Clarity, organization, and engagement of the presentation.
o Depth and relevance of individual contributions to the project.
o Effectiveness in demonstrating the analytical dashboard.
o Reflection on challenges and future implications.
4. Peer Review Report:
o Fairness and comprehensiveness of the peer evaluations.
5. Self-Reflective Report:
o Depth of reflection on personal contributions, learning experiences, and performance
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Criteria
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Exceptional (>=80%)
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Admirable (70% – 79%)
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Creditable (60% – 69%)
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Acceptable (50% – 59%)
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Unsatisfactory (<=49%)
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Group
Report(Summary,
Introduction and
Methodology, Ethical Discussion)(10)
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Comprehensive and clear executive
summary, introduction, and methodology.
Insights are deep and relevant with effective user instructions.
Thoughtful ethics
discussion and well
organized report.
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Well-organized
executive summary, introduction, and
methodology. Insights are relevant with good user instructions. Ethics discussion is relevant and report is coherent.
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Adequate executive summary, introduction, and methodology.
Insights are reasonable with basic user
instructions. Ethics
discussion is present but may lack depth.
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Basic executive
summary, introduction, and methodology.
Insights and user
instructions are minimal or lacking detail. Ethics discussion is superficial.
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Poorly structured or incomplete executive summary, introduction, and methodology.
Insights are vague or irrelevant, and user
instructions are unclear. Ethics discussion is
missing or inadequate.
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Analytical
Dashboard(5)
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Highly functional, user friendly, and provides deep insights with
significant business
impact. Includes
advanced automation features.
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Functional and user friendly dashboard
providing relevant
insights. Automation features are present but may have minor issues.
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Usable dashboard with basic insights.
Automation features are minimal or not fully integrated.
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Basic dashboard with limited functionality or user-friendliness. Few or no automation
features.
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Non-functional or
difficult-to-use
dashboard. No
automation features or significant issues with usability.
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Business Impact
Valuations(5)
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Clear and impactful
business valuations
with strong evidence and justification.
Demonstrates
significant potential impact.
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Clear business
valuations with good evidence and
justification. Impact is relevant but may lack depth.
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Basic business
valuations with some evidence and
justification. Impact is evident but limited.
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Minimal business
valuations with weak evidence and
justification. Impact is unclear or superficial.
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Inaccurate or
unsupported business valuations. No clear impact demonstrated.
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Individual
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Highly engaging and
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Clear and organized
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Adequate presentation
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Basic presentation with
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Poorly organized or
|
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Presentation(10)
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well-organized
presentation. Excellent clarity on role, findings, dashboard
demonstration, and reflection on challenges and future implications.
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presentation. Good
coverage of role,
findings, and dashboard demonstration.
Reflection is solid but could be more detailed.
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with basic organization. Covers role and findings but may lack depth in dashboard
demonstration or
reflection.
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limited organization. Coverage of role and findings is minimal, and reflection is superficial.
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unclear presentation. Little coverage of role, findings, or dashboard demonstration.
Reflection is inadequate or missing.
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Peer Review Report(5)
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Fair, comprehensive evaluations with
detailed feedback on collaboration, work
quality, and adherence to deadlines.
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Fair evaluations with good feedback on
collaboration, work
quality, and deadlines. Minor details may be missing.
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Basic evaluations with some feedback on
collaboration, work
quality, and deadlines. Lacks depth or detail.
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Minimal evaluations with limited feedback on collaboration, work quality, and deadlines.
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Unfair or incomplete evaluations with little to no feedback on
collaboration, work
quality, or deadlines.
|
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Self-Reflective
Report(5)
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Deep and insightful reflection on personal contributions, learning experiences, and
performance. Clearly discusses challenges and growth.(5)
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Thoughtful reflection on personal
contributions and
learning experiences. Discusses challenges and growth but may lack depth.(4)
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Adequate reflection on personal
contributions and
learning experiences. Basic discussion of challenges and
growth.
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Minimal reflection on personal contributions and learning
experiences.
Superficial discussion of challenges and
growth.
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Poor or superficial reflection on personal contributions, learning experiences, and
challenges. Little
discussion of growth.
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Submission Instructions
Submission Instructions All submissions are to be submitted through turn-it-in. Drop-boxes linked to turn-it-in will be set up in the Unit of Study Moodleaccount. Assignments not submitted through these drop-boxes will not be considered. Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made after the due date and time will be penalized at the rate of 20% per day (including weekend days). The turn-it-in similarity score will be used in determining the level if any of plagiarism. Turn-it-in will check conference websites, Journal articles, the Web and your own class member submissions for plagiarism. You can see your turn-it-in similarity score when you submit your assignment to the appropriate drop-box. If this is a concern you will have a chance to change your assignment and re-submit. However, re-submission is only allowed prior to the submission due date and time. After the due date and time have elapsed you cannot make re-submissions and you will have to live with the similarity score as there will be no chance for changing. Thus, plan early and submit early to take advantage of this feature. You can make multiple submissions, but please remember we only see the last submission, and the date and time you submitted will be taken from that submission. Instruction: You are required to submit 2500± 10% words report (word/pdf file) on the below tasks. Use appropriate headings and subheading in your report. Please note that only group leaders will submit the file. 
Note: All work is due by the due date and time. Late submissions will be penalized at 20% of the assessment final grade per day, including weekends.
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