ICT702 Business Analytics & Visualization Semester 1, 2025 Assignment Help

 

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

Coverage: Incomplete  or unclear

demonstration.

Presentation:

Unprofessional or

unclear presentation.  Effectiveness:

Demonstrates

insufficient

 

effective use of tools and  methods.

effective use of tools and  methods.

of tools and methods.

of tools and methods.

understanding and  use of tools and

methods.

 

Assessment 4: Final Project Report and Presentation

Overview

Assessment tasks

Learning

Outcome

Mapping

Assessment ID

Assessment Item

When due

Weighting

ULO#

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

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

Criteria

Exceptional (>=80%)

Admirable (70% – 79%)

Creditable (60% – 69%)

Acceptable (50% – 59%)

Unsatisfactory (<=49%)

Group

Report(Summary,

Introduction and

Methodology, Ethical  Discussion)(10)

Comprehensive and  clear executive

summary, introduction,  and methodology.

Insights are deep and  relevant with effective  user instructions.

Thoughtful ethics

discussion and well

organized report.

Well-organized

executive summary,  introduction, and

methodology. Insights  are relevant with good  user instructions. Ethics  discussion is relevant  and report is coherent.

Adequate executive  summary, introduction,  and methodology.

Insights are reasonable  with basic user

instructions. Ethics

discussion is present  but may lack depth.

Basic executive

summary, introduction,  and methodology.

Insights and user

instructions are minimal  or lacking detail. Ethics  discussion is superficial.

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.

Analytical

Dashboard(5)

Highly functional, user friendly, and provides  deep insights with

significant business

impact. Includes

advanced automation  features.

Functional and user friendly dashboard

providing relevant

insights. Automation  features are present  but may have minor  issues.

Usable dashboard with  basic insights.

Automation features  are minimal or not fully  integrated.

Basic dashboard with  limited functionality or  user-friendliness. Few  or no automation

features.

Non-functional or

difficult-to-use

dashboard. No

automation features or  significant issues with  usability.

Business Impact

Valuations(5)

Clear and impactful

business valuations

with strong evidence  and justification.

Demonstrates

significant potential  impact.

Clear business

valuations with good  evidence and

justification. Impact is  relevant but may lack  depth.

Basic business

valuations with some  evidence and

justification. Impact is  evident but limited.

Minimal business

valuations with weak  evidence and

justification. Impact is  unclear or superficial.

Inaccurate or

unsupported business  valuations. No clear  impact demonstrated.

Individual

Highly engaging and

Clear and organized

Adequate presentation

Basic presentation with

Poorly organized or

 

Presentation(10)

well-organized

presentation. Excellent  clarity on role, findings,  dashboard

demonstration, and  reflection on challenges  and future implications.

presentation. Good

coverage of role,

findings, and dashboard  demonstration.

Reflection is solid but  could be more detailed.

with basic organization.  Covers role and findings  but may lack depth in  dashboard

demonstration or

reflection.

limited organization.  Coverage of role and  findings is minimal, and  reflection is superficial.

unclear presentation.  Little coverage of role,  findings, or dashboard  demonstration.

Reflection is inadequate  or missing.

Peer Review Report(5)

Fair, comprehensive  evaluations with

detailed feedback on  collaboration, work

quality, and adherence  to deadlines.

Fair evaluations with  good feedback on

collaboration, work

quality, and deadlines.  Minor details may be  missing.

Basic evaluations with  some feedback on

collaboration, work

quality, and deadlines.  Lacks depth or detail.

Minimal evaluations  with limited feedback  on collaboration, work  quality, and deadlines.

Unfair or incomplete  evaluations with little to  no feedback on

collaboration, work

quality, or deadlines.

Self-Reflective

Report(5)

Deep and insightful  reflection on personal  contributions, learning  experiences, and

performance. Clearly  discusses challenges  and growth.(5)

Thoughtful reflection  on personal

contributions and

learning experiences.  Discusses challenges  and growth but may  lack depth.(4)

Adequate reflection  on personal

contributions and

learning experiences.  Basic discussion of  challenges and

growth.

Minimal reflection on  personal contributions  and learning

experiences.

Superficial discussion  of challenges and

growth.

Poor or superficial  reflection on personal  contributions, learning  experiences, and

challenges. Little

discussion of growth.

 

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.