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).

Task

1. Define the Business Context and Problem:

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

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.

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