Complete as required
Requirements: 20 hours
Category: Data Analytics
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data mining hw3
please follow the instructions carefully and dont go more advanced than what the professor expects please.
Requirements: 4 tasks
-
Data Analytics Question
I uploaded the files which contains the Exercise and the other files to solve it.
Please adhere to the following:
1- Do not use artificial intelligence, as the university detects its use and has Turnitin.
2- Do not duplicate assignments from other students.
3- Submit within the specified timeframe,I have chosen four days.
Format: 1500-2000 words (Excluding graphs and charts) based on the guidelines below.
I. Assignment Brief
This assignment requires you to produce an academically grounded business analytics report.
You are required to select one dataset from the pool of datasets provided on the assignment
Loop submission link. All datasets have been sourced from open-access repositories and are
approved for use for educational purposes only.
Choose a dataset that aligns with an industry sector of interest to you (e.g. Healthcare
Management, Human Resources, Marketing, Inventory Management, Transport, Education,
etc.). Your role is to identify a business problem or opportunity that can be addressed
analytically using the variables available in the selected dataset.
Your task is to conduct the appropriate analytics processes to address the identified problem or
opportunity and to present your findings in a business analytics report.
In brief, a business analytics report is a structured document that presents data-driven insights to
inform business decision-making. Using your chosen dataset, you are required to conduct descriptive,
predictive, and prescriptive analytics.
1. 2. II. Analytics Report Framework
1. Organisational Context and Decision Challenge (20%)
This section must demonstrate that the analytics work is grounded in a business need. You should
include:
- Industry Context: Introduce the sector and explain the relevance of the dataset to a real
industry setting.
- Decision Problem or Strategic Opportunity: Clearly define the business problem or
opportunity. Business Value and Strategic Importance: Explain why this issue matters and what
organisational value is sought (e.g., efficiency, growth, risk mitigation, optimisation).
- Analytics Objectives and Key Questions: Frame clear, data-answerable business questions
aligned with the decision challenge.
2. Working with Data and Analytical Design (20%)
This section must demonstrate the use of the dataset to answer the business questions, not just
technical execution. You should include:
- Dataset Overview and Variable Classification: Identify key predictors (independent variables)
and targets (dependent variables).
- Data Exploration and Assumptions: Discuss patterns, outliers, and potential limitations.
- Data Preparation and Transformation: Explain cleaning steps and justification.
- Analytical Approach and Justification: Describe why specific descriptive, predictive, and
prescriptive techniques were selected (you can limit the techniques to those taught in class).
3. Analytical Execution and Evidence (30%)
This section presents the analytic process and techniques in a structured analytical output.
- Descriptive steps and insights
- Predictive modelling results
- Prescriptive analysis and decision scenarios
- Analytics Dashboard: All key charts, tables, and visualisations must be presented together.
Each visual must include a short managerial insight statement.
4. Critical Evaluation and Managerial Insight (20%)
This section discusses your evaluation of the results.
- Interpretation of results
- Discussion of reliability, assumptions, risks, and limitations.
- Managerial implications
- Demonstrate how analytical outputs are combined with your industry understanding to inform
decisions.
5. Recommendations and Decision Communication (5%)
This section translates your analysis into action.
- Actionable recommendations
- Expected organisational impact
- Implementation considerations
6. Housekeeping (5%)
- Harvard or APA referencing (include DOIs where available)
- Logical structure and coherent argumentation
- Table of contents
- Professional presentation of dashboard and appendices
II. Minimum Requirements for Technical Analytics
1. Descriptive Analytics
a) Select four (4) variables from the dataset and formulate four (4) descriptive analytics
questions that are relevant to your stated business problem.
b) Produce data visualisations to support your descriptive analysis and summary statistics.c) Each visualisation must include a brief insight statement explaining what the visual shows
and what decision or action it may inform.
All descriptive analytics visualisations should be compiled and presented together in an
analytics dashboard.
2. Predictive Analytics
Formulate and analyse at least one (1) predictive analytics question. Explain how the results of the
analysis could influence or support the business decision or action.
3. Prescriptive Analytics
Formulate and analyse at least one (1) prescriptive analytics question. Clearly explain how the
resulting recommendation would change or improve the business decision or action.
Notes:
1. 2. 3. Academic work at MSc level is expected to demonstrate independent research and critical
judgement, supported by academic evidence and reputable third-party sources. Please use the
Harvard or APA referencing style throughout your work.
A wide range of relevant peer-reviewed journal articles covering all areas of analytics is
available and should be consulted where appropriate.
Plagiarism will not be tolerated. All sources must be properly acknowledged in accordance
with the chosen referencing style
Requirements: 1 day
-
Data Analytics Question
I uploaded the files which contains the Exercise and the other files to solve it.
Please adhere to the following:
1- Do not use artificial intelligence, as the university detects its use and has Turnitin.
2- Do not duplicate assignments from other students.
3- Submit within the specified timeframe,I have chosen four days.
Format: 1500-2000 words (Excluding graphs and charts) based on the guidelines below.
I. Assignment Brief
This assignment requires you to produce an academically grounded business analytics report.
You are required to select one dataset from the pool of datasets provided on the assignment
Loop submission link. All datasets have been sourced from open-access repositories and are
approved for use for educational purposes only.
Choose a dataset that aligns with an industry sector of interest to you (e.g. Healthcare
Management, Human Resources, Marketing, Inventory Management, Transport, Education,
etc.). Your role is to identify a business problem or opportunity that can be addressed
analytically using the variables available in the selected dataset.
Your task is to conduct the appropriate analytics processes to address the identified problem or
opportunity and to present your findings in a business analytics report.
In brief, a business analytics report is a structured document that presents data-driven insights to
inform business decision-making. Using your chosen dataset, you are required to conduct descriptive,
predictive, and prescriptive analytics.
1. 2. II. Analytics Report Framework
1. Organisational Context and Decision Challenge (20%)
This section must demonstrate that the analytics work is grounded in a business need. You should
include:
- Industry Context: Introduce the sector and explain the relevance of the dataset to a real
industry setting.
- Decision Problem or Strategic Opportunity: Clearly define the business problem or
opportunity. Business Value and Strategic Importance: Explain why this issue matters and what
organisational value is sought (e.g., efficiency, growth, risk mitigation, optimisation).
- Analytics Objectives and Key Questions: Frame clear, data-answerable business questions
aligned with the decision challenge.
2. Working with Data and Analytical Design (20%)
This section must demonstrate the use of the dataset to answer the business questions, not just
technical execution. You should include:
- Dataset Overview and Variable Classification: Identify key predictors (independent variables)
and targets (dependent variables).
- Data Exploration and Assumptions: Discuss patterns, outliers, and potential limitations.
- Data Preparation and Transformation: Explain cleaning steps and justification.
- Analytical Approach and Justification: Describe why specific descriptive, predictive, and
prescriptive techniques were selected (you can limit the techniques to those taught in class).
3. Analytical Execution and Evidence (30%)
This section presents the analytic process and techniques in a structured analytical output.
- Descriptive steps and insights
- Predictive modelling results
- Prescriptive analysis and decision scenarios
- Analytics Dashboard: All key charts, tables, and visualisations must be presented together.
Each visual must include a short managerial insight statement.
4. Critical Evaluation and Managerial Insight (20%)
This section discusses your evaluation of the results.
- Interpretation of results
- Discussion of reliability, assumptions, risks, and limitations.
- Managerial implications
- Demonstrate how analytical outputs are combined with your industry understanding to inform
decisions.
5. Recommendations and Decision Communication (5%)
This section translates your analysis into action.
- Actionable recommendations
- Expected organisational impact
- Implementation considerations
6. Housekeeping (5%)
- Harvard or APA referencing (include DOIs where available)
- Logical structure and coherent argumentation
- Table of contents
- Professional presentation of dashboard and appendices
II. Minimum Requirements for Technical Analytics
1. Descriptive Analytics
a) Select four (4) variables from the dataset and formulate four (4) descriptive analytics
questions that are relevant to your stated business problem.
b) Produce data visualisations to support your descriptive analysis and summary statistics.c) Each visualisation must include a brief insight statement explaining what the visual shows
and what decision or action it may inform.
All descriptive analytics visualisations should be compiled and presented together in an
analytics dashboard.
2. Predictive Analytics
Formulate and analyse at least one (1) predictive analytics question. Explain how the results of the
analysis could influence or support the business decision or action.
3. Prescriptive Analytics
Formulate and analyse at least one (1) prescriptive analytics question. Clearly explain how the
resulting recommendation would change or improve the business decision or action.
Notes:
1. 2. 3. Academic work at MSc level is expected to demonstrate independent research and critical
judgement, supported by academic evidence and reputable third-party sources. Please use the
Harvard or APA referencing style throughout your work.
A wide range of relevant peer-reviewed journal articles covering all areas of analytics is
available and should be consulted where appropriate.
Plagiarism will not be tolerated. All sources must be properly acknowledged in accordance
with the chosen referencing style
Requirements: 1 day
-
Data Analytics Question
I uploaded the files which contains the Exercise and the other files to solve it.
Please adhere to the following:
1- Do not use artificial intelligence, as the university detects its use and has Turnitin.
2- Do not duplicate assignments from other students.
3- Submit within the specified timeframe,I have chosen four days.
Format: 1500-2000 words (Excluding graphs and charts) based on the guidelines below.
I. Assignment Brief
This assignment requires you to produce an academically grounded business analytics report.
You are required to select one dataset from the pool of datasets provided on the assignment
Loop submission link. All datasets have been sourced from open-access repositories and are
approved for use for educational purposes only.
Choose a dataset that aligns with an industry sector of interest to you (e.g. Healthcare
Management, Human Resources, Marketing, Inventory Management, Transport, Education,
etc.). Your role is to identify a business problem or opportunity that can be addressed
analytically using the variables available in the selected dataset.
Your task is to conduct the appropriate analytics processes to address the identified problem or
opportunity and to present your findings in a business analytics report.
In brief, a business analytics report is a structured document that presents data-driven insights to
inform business decision-making. Using your chosen dataset, you are required to conduct descriptive,
predictive, and prescriptive analytics.
1. 2. II. Analytics Report Framework
1. Organisational Context and Decision Challenge (20%)
This section must demonstrate that the analytics work is grounded in a business need. You should
include:
- Industry Context: Introduce the sector and explain the relevance of the dataset to a real
industry setting.
- Decision Problem or Strategic Opportunity: Clearly define the business problem or
opportunity. Business Value and Strategic Importance: Explain why this issue matters and what
organisational value is sought (e.g., efficiency, growth, risk mitigation, optimisation).
- Analytics Objectives and Key Questions: Frame clear, data-answerable business questions
aligned with the decision challenge.
2. Working with Data and Analytical Design (20%)
This section must demonstrate the use of the dataset to answer the business questions, not just
technical execution. You should include:
- Dataset Overview and Variable Classification: Identify key predictors (independent variables)
and targets (dependent variables).
- Data Exploration and Assumptions: Discuss patterns, outliers, and potential limitations.
- Data Preparation and Transformation: Explain cleaning steps and justification.
- Analytical Approach and Justification: Describe why specific descriptive, predictive, and
prescriptive techniques were selected (you can limit the techniques to those taught in class).
3. Analytical Execution and Evidence (30%)
This section presents the analytic process and techniques in a structured analytical output.
- Descriptive steps and insights
- Predictive modelling results
- Prescriptive analysis and decision scenarios
- Analytics Dashboard: All key charts, tables, and visualisations must be presented together.
Each visual must include a short managerial insight statement.
4. Critical Evaluation and Managerial Insight (20%)
This section discusses your evaluation of the results.
- Interpretation of results
- Discussion of reliability, assumptions, risks, and limitations.
- Managerial implications
- Demonstrate how analytical outputs are combined with your industry understanding to inform
decisions.
5. Recommendations and Decision Communication (5%)
This section translates your analysis into action.
- Actionable recommendations
- Expected organisational impact
- Implementation considerations
6. Housekeeping (5%)
- Harvard or APA referencing (include DOIs where available)
- Logical structure and coherent argumentation
- Table of contents
- Professional presentation of dashboard and appendices
II. Minimum Requirements for Technical Analytics
1. Descriptive Analytics
a) Select four (4) variables from the dataset and formulate four (4) descriptive analytics
questions that are relevant to your stated business problem.
b) Produce data visualisations to support your descriptive analysis and summary statistics.c) Each visualisation must include a brief insight statement explaining what the visual shows
and what decision or action it may inform.
All descriptive analytics visualisations should be compiled and presented together in an
analytics dashboard.
2. Predictive Analytics
Formulate and analyse at least one (1) predictive analytics question. Explain how the results of the
analysis could influence or support the business decision or action.
3. Prescriptive Analytics
Formulate and analyse at least one (1) prescriptive analytics question. Clearly explain how the
resulting recommendation would change or improve the business decision or action.
Notes:
1. 2. 3. Academic work at MSc level is expected to demonstrate independent research and critical
judgement, supported by academic evidence and reputable third-party sources. Please use the
Harvard or APA referencing style throughout your work.
A wide range of relevant peer-reviewed journal articles covering all areas of analytics is
available and should be consulted where appropriate.
Plagiarism will not be tolerated. All sources must be properly acknowledged in accordance
with the chosen referencing style
Requirements: 1 day
-
Predictive Analytics
CAI 3801 – Week 5 Lab Assignment
Predictive Analytics (Forecasting) with Tableau Public + GenAI (ChatGPT/Gemini/Copilot)
This lab focuses only on predictive analytics. Prescriptive analytics will be covered later.|
Submission: Tableau TWBX file + filled template (DOCX or PDF) | Note: change both file names
and include your name as: (e.g., Firstname_Lastname_Week5_Lab.twbx and docx or
pdf)
Academic integrity: You may use GenAI tools for drafting and iteration, but you must verify all
numbers in Tableau and disclose your AI use in the template. No sensitive data in prompts.
Learning goals (what you should be able to do after this lab)
- Translate a vague business ask into a measurable predictive question (what to predict, by
when, at what granularity).
- Build a time-series view in Tableau and generate a forecast with uncertainty (prediction
intervals).
- Assess forecast quality using Tableau’s forecast diagnostics (not ‘vibes’).
- Write a short executive-ready narrative that ties forecast results to a business decision and
clearly states assumptions and risks.
- Use GenAI as a productivity tool while keeping ownership of context, evidence, and
decisions.
Tools and data
- Tableau Public (free) – install or use Tableau Public Desktop.
- GenAI tool: ChatGPT/ Gemini / Copilot (choose one).
- Dataset: Sample – Superstore (provided in the Week 5 module on Canvas).
Business scenario (choose ONE)
Pick one executive ask below, or write a similar one that fits Superstore. Your job is to turn it
into a predictive question and build a forecast that supports a decision.
A) Operations: ‘We need a sales forecast for the next 4 weeks to plan staffing and inventory.
Where should we prepare for growth or risk?’
B) Finance: ‘Profit has been volatile. Forecast profit for the next month and explain where the
risk is highest (category/region).’
C) Marketing (proxy): ‘Orders are our demand signal. Forecast order volume for the next 4
weeks and identify which customer segments are likely to drive the change.’
D) Your own: A Superstore-friendly question with a clear decision attached (inventory, staffing,
budget, promotion planning).
What you will submit (deliverables)
1) Tableau Public workbook in TWBX
2) Filled student template (DOCX or PDF) with: problem framing, prompt log, screenshots,
forecast diagnostics, and executive brief.
3) AI use note (inside the template): what you used AI for + what you verified + what you
changed.
Step-by-step instructions
Part 1 – Turn a vague ask into a predictive question
1. Choose a scenario (A-D). Write the decision in one sentence (e.g., ‘allocate inventory across
regions for next month’).
2. Define the predictive question using these fields: Target metric (Sales/Profit/Orders),
Forecast horizon (next 4 weeks or month), Granularity (weekly or monthly), and Segment
(overall or by Region/Category/Segment).
3. Define the success metric/constraint for your decision (example: ‘minimize stockouts’ or
‘prepare for regions with >10% forecasted growth’).
4. Use the RTC-OC-QC prompt template below to ask an AI tool for a draft analysis plan. Save
the prompt and 5-8 lines of the output (you will paste excerpts into the template doc).
5. Refine the plan in your own words. You own the final question and scope.
RTC-OC-QC prompt template (copy/paste):
ROLE: You are a business data analyst helping me use Tableau Public.
TASK: Convert this executive ask into a predictive analytics plan.
CONTEXT: I am using Sample – Superstore (Orders). I can build time-series charts and
forecasts in Tableau.
OBJECTIVE/CONSTRAINTS: My forecast horizon is 4 weeks. Budget/time is limited. I must
include uncertainty and validation.
QUALITY CHECKS: Ask 3-5 clarifying questions. Then produce: (1) a final predictive
question, (2) required charts, (3) how to check forecast quality, (4) how to
communicate assumptions and risks.
EXEC ASK: <paste your scenario here>
OUTPUT FORMAT: Use headings and bullets. Keep it under 250 words.
Part 2 – Build the forecast in Tableau Public
6. Open Tableau Public and connect to the Sample – Superstore dataset
7. Create a time series view: drag Order Date to Columns and change it to WEEK (or MONTH).
Drag Sales (or Profit or Quantity for demand) to Rows. This is your baseline trend line.
8. Create a forecast: Analytics pane -> Forecast -> drag ‘Forecast’ onto the view (or Analysis ->
Forecast -> Show Forecast).
9. Open forecast details: right-click the forecast -> ‘Describe Forecast’. Capture the key metrics
(e.g., MAPE/RMSE) and the model notes. Take a screenshot for your submission.
10. Adjust forecast options (if needed): Forecast -> Forecast Options. Set forecast length to 4
weeks or months (depending on your selection of forecast time) and keep seasonality as
Automatic unless you have a clear reason to change it.
11. Add (Region or Category or Segment) based on your scenario i.e., drag your selected
dimension (one of these 3) to color.
12. Add caption (you can use AI to generate the insights on the view as you did in Week 4 Lab
but make sure to edit to reflect your own observations).
— Write 3 factual observations from Tableau (include numbers): current level, trend
direction, and the forecast range etc.
Part 3 – Interpret results and communicate uncertainty
13. Use an AI tool to draft a short executive brief, but only after you provide the verified
Tableau numbers (do not let the AI invent them).
14. Add a ‘Self-check’ section: list what could make the forecast wrong (data gaps, seasonality,
outliers, promo events) and what you would verify next week.
Suggested ‘draft the brief’ prompt:
ROLE: You are my executive writing assistant.
TASK: Draft a 1-page executive brief based ONLY on the verified facts I provide.
CONTEXT: Superstore forecast for next 4 weeks.
VERIFIED FACTS (from Tableau):
– <paste 5-8 bullet facts with numbers, including forecast range/interval>
OUTPUT: 1) 2-sentence summary, 2) 3 insights, 3) 3 recommended actions, 4)
assumptions/risks (at least 3), 5) what to verify next.
CONSTRAINTS: No new numbers. If unsure, say what to verify.
Submission checklist (quick)
- Tableau Public workbook link works and is viewable.
- Template includes screenshots of: baseline trend, forecast view with intervals, at least one
segmented view, and ‘Describe Forecast’ diagnostics.
- Executive brief includes numbers that match Tableau.
- AI use note completed (what you used AI for + what you verified).
- No sensitive or personal data was used in prompts.
Grading rubric (100 points)
Category What excellent looks like Points
Problem framing Clear predictive question (target, horizon, granularity,
segment) + decision context; success
metric/constraint is stated.
20
Tableau build (forecast) Correct time-series view(s) + forecast shown; at least
one segmented comparison; forecast options are
sensible.
40
Forecast quality +
uncertainty
Uses ‘Describe Forecast’ diagnostics; interprets
prediction intervals; identifies limitations and what to
verify.
20
Executive brief Concise, executive-ready, actionable; all numbers
consistent with Tableau; clear assumptions/risks.
10
Documentation + AI use
note
Prompt excerpts included; transparent AI use; clean
screenshots and labeling.
10
Requirements: all
-
Data Analytics Question
I uploaded the files which contains the Exercise and the other files to solve it.
Please adhere to the following:
1- Do not use artificial intelligence, as the university detects its use and has Turnitin.
2- Do not duplicate assignments from other students.
3- Submit within the specified timeframe,I have chosen four days.
Format: 1500-2000 words (Excluding graphs and charts) based on the guidelines below.
I. Assignment Brief
This assignment requires you to produce an academically grounded business analytics report.
You are required to select one dataset from the pool of datasets provided on the assignment
Loop submission link. All datasets have been sourced from open-access repositories and are
approved for use for educational purposes only.
Choose a dataset that aligns with an industry sector of interest to you (e.g. Healthcare
Management, Human Resources, Marketing, Inventory Management, Transport, Education,
etc.). Your role is to identify a business problem or opportunity that can be addressed
analytically using the variables available in the selected dataset.
Your task is to conduct the appropriate analytics processes to address the identified problem or
opportunity and to present your findings in a business analytics report.
In brief, a business analytics report is a structured document that presents data-driven insights to
inform business decision-making. Using your chosen dataset, you are required to conduct descriptive,
predictive, and prescriptive analytics.
1. 2. II. Analytics Report Framework
1. Organisational Context and Decision Challenge (20%)
This section must demonstrate that the analytics work is grounded in a business need. You should
include:
- Industry Context: Introduce the sector and explain the relevance of the dataset to a real
industry setting.
- Decision Problem or Strategic Opportunity: Clearly define the business problem or
opportunity. Business Value and Strategic Importance: Explain why this issue matters and what
organisational value is sought (e.g., efficiency, growth, risk mitigation, optimisation).
- Analytics Objectives and Key Questions: Frame clear, data-answerable business questions
aligned with the decision challenge.
2. Working with Data and Analytical Design (20%)
This section must demonstrate the use of the dataset to answer the business questions, not just
technical execution. You should include:
- Dataset Overview and Variable Classification: Identify key predictors (independent variables)
and targets (dependent variables).
- Data Exploration and Assumptions: Discuss patterns, outliers, and potential limitations.
- Data Preparation and Transformation: Explain cleaning steps and justification.
- Analytical Approach and Justification: Describe why specific descriptive, predictive, and
prescriptive techniques were selected (you can limit the techniques to those taught in class).
3. Analytical Execution and Evidence (30%)
This section presents the analytic process and techniques in a structured analytical output.
- Descriptive steps and insights
- Predictive modelling results
- Prescriptive analysis and decision scenarios
- Analytics Dashboard: All key charts, tables, and visualisations must be presented together.
Each visual must include a short managerial insight statement.
4. Critical Evaluation and Managerial Insight (20%)
This section discusses your evaluation of the results.
- Interpretation of results
- Discussion of reliability, assumptions, risks, and limitations.
- Managerial implications
- Demonstrate how analytical outputs are combined with your industry understanding to inform
decisions.
5. Recommendations and Decision Communication (5%)
This section translates your analysis into action.
- Actionable recommendations
- Expected organisational impact
- Implementation considerations
6. Housekeeping (5%)
- Harvard or APA referencing (include DOIs where available)
- Logical structure and coherent argumentation
- Table of contents
- Professional presentation of dashboard and appendices
II. Minimum Requirements for Technical Analytics
1. Descriptive Analytics
a) Select four (4) variables from the dataset and formulate four (4) descriptive analytics
questions that are relevant to your stated business problem.
b) Produce data visualisations to support your descriptive analysis and summary statistics.c) Each visualisation must include a brief insight statement explaining what the visual shows
and what decision or action it may inform.
All descriptive analytics visualisations should be compiled and presented together in an
analytics dashboard.
2. Predictive Analytics
Formulate and analyse at least one (1) predictive analytics question. Explain how the results of the
analysis could influence or support the business decision or action.
3. Prescriptive Analytics
Formulate and analyse at least one (1) prescriptive analytics question. Clearly explain how the
resulting recommendation would change or improve the business decision or action.
Notes:
1. 2. 3. Academic work at MSc level is expected to demonstrate independent research and critical
judgement, supported by academic evidence and reputable third-party sources. Please use the
Harvard or APA referencing style throughout your work.
A wide range of relevant peer-reviewed journal articles covering all areas of analytics is
available and should be consulted where appropriate.
Plagiarism will not be tolerated. All sources must be properly acknowledged in accordance
with the chosen referencing style
Requirements: 2 days
-
Data Analytics Question
I uploaded the files which contains the Exercise and the other files to solve it.
Please adhere to the following:
1- Do not use artificial intelligence, as the university detects its use and has Turnitin.
2- Do not duplicate assignments from other students.
3- Submit within the specified timeframe,I have chosen four days.
Format: 1500-2000 words (Excluding graphs and charts) based on the guidelines below.
I. Assignment Brief
This assignment requires you to produce an academically grounded business analytics report.
You are required to select one dataset from the pool of datasets provided on the assignment
Loop submission link. All datasets have been sourced from open-access repositories and are
approved for use for educational purposes only.
Choose a dataset that aligns with an industry sector of interest to you (e.g. Healthcare
Management, Human Resources, Marketing, Inventory Management, Transport, Education,
etc.). Your role is to identify a business problem or opportunity that can be addressed
analytically using the variables available in the selected dataset.
Your task is to conduct the appropriate analytics processes to address the identified problem or
opportunity and to present your findings in a business analytics report.
In brief, a business analytics report is a structured document that presents data-driven insights to
inform business decision-making. Using your chosen dataset, you are required to conduct descriptive,
predictive, and prescriptive analytics.
1. 2. II. Analytics Report Framework
1. Organisational Context and Decision Challenge (20%)
This section must demonstrate that the analytics work is grounded in a business need. You should
include:
- Industry Context: Introduce the sector and explain the relevance of the dataset to a real
industry setting.
- Decision Problem or Strategic Opportunity: Clearly define the business problem or
opportunity. Business Value and Strategic Importance: Explain why this issue matters and what
organisational value is sought (e.g., efficiency, growth, risk mitigation, optimisation).
- Analytics Objectives and Key Questions: Frame clear, data-answerable business questions
aligned with the decision challenge.
2. Working with Data and Analytical Design (20%)
This section must demonstrate the use of the dataset to answer the business questions, not just
technical execution. You should include:
- Dataset Overview and Variable Classification: Identify key predictors (independent variables)
and targets (dependent variables).
- Data Exploration and Assumptions: Discuss patterns, outliers, and potential limitations.
- Data Preparation and Transformation: Explain cleaning steps and justification.
- Analytical Approach and Justification: Describe why specific descriptive, predictive, and
prescriptive techniques were selected (you can limit the techniques to those taught in class).
3. Analytical Execution and Evidence (30%)
This section presents the analytic process and techniques in a structured analytical output.
- Descriptive steps and insights
- Predictive modelling results
- Prescriptive analysis and decision scenarios
- Analytics Dashboard: All key charts, tables, and visualisations must be presented together.
Each visual must include a short managerial insight statement.
4. Critical Evaluation and Managerial Insight (20%)
This section discusses your evaluation of the results.
- Interpretation of results
- Discussion of reliability, assumptions, risks, and limitations.
- Managerial implications
- Demonstrate how analytical outputs are combined with your industry understanding to inform
decisions.
5. Recommendations and Decision Communication (5%)
This section translates your analysis into action.
- Actionable recommendations
- Expected organisational impact
- Implementation considerations
6. Housekeeping (5%)
- Harvard or APA referencing (include DOIs where available)
- Logical structure and coherent argumentation
- Table of contents
- Professional presentation of dashboard and appendices
II. Minimum Requirements for Technical Analytics
1. Descriptive Analytics
a) Select four (4) variables from the dataset and formulate four (4) descriptive analytics
questions that are relevant to your stated business problem.
b) Produce data visualisations to support your descriptive analysis and summary statistics.c) Each visualisation must include a brief insight statement explaining what the visual shows
and what decision or action it may inform.
All descriptive analytics visualisations should be compiled and presented together in an
analytics dashboard.
2. Predictive Analytics
Formulate and analyse at least one (1) predictive analytics question. Explain how the results of the
analysis could influence or support the business decision or action.
3. Prescriptive Analytics
Formulate and analyse at least one (1) prescriptive analytics question. Clearly explain how the
resulting recommendation would change or improve the business decision or action.
Notes:
1. 2. 3. Academic work at MSc level is expected to demonstrate independent research and critical
judgement, supported by academic evidence and reputable third-party sources. Please use the
Harvard or APA referencing style throughout your work.
A wide range of relevant peer-reviewed journal articles covering all areas of analytics is
available and should be consulted where appropriate.
Plagiarism will not be tolerated. All sources must be properly acknowledged in accordance
with the chosen referencing style
Requirements: 2 days
-
Business Analytics: Delivering Performance Excellence (DPE)
I uploaded the files which contains the Exercise and the other files to solve it.
Please adhere to the following:
1- Do not use artificial intelligence, as the university detects its use and has Turnitin.
2- Do not duplicate assignments from other students.
3- Submit within the specified timeframe,I have chosen two days.
4- I want it in one Excel file
Requirements: 23 hours
-
Business Analytics: Delivering Performance Excellence (DPE)
I uploaded the files which contains the Exercise and the other files to solve it.
Please adhere to the following:
1- Do not use artificial intelligence, as the university detects its use and has Turnitin.
2- Do not duplicate assignments from other students.
3- Submit within the specified timeframe,I have chosen two days.
4- I want it in one Excel file
Requirements: 23 hours