Convincing Features
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Date
| Course Title | : | BUSINESS ANALYTICS |
| Course Code | : | GSDM7514 / BAT75014 |
| Semester | : | MARCH 2026 |
| Name of Course Leader | : | MAFAS RAHEEM |
At the end of the course, the students will be able to:
| CLO1 | : | Apply and refine data extraction, transformation, and statistical modeling methodologies, leveraging digital competencies to produce adaptive and impactful business analytics solutions. (P5, PLO6). |
| CLO2 | : | Perform integrated problem-solving frameworks to design sustainable, data-driven models that address complex business challenges, incorporating health and well-being (P4, PLO7. |
| CLO3 | : | Use operational analytics and appropriate software to evaluate and interpret data, support informed decision-making, and effectively communicate analytical insights in professional contexts. (C6, PLO5) |
| Assessment Description | CLO | Individual/
Group |
Assign | Due | Percentage |
| Assignment | CLO1 | Individual | Week 1 | Week 6 | 30% |
| Group Assignment | CLO2 | Group | Week 1 | Week 6 | 30% |
| Refelction | CLO1 | Individual | Week 1 | Week 6 | 10% |
| Assessment Description | CLO | Individual/
Group |
Assign | Due | Percentage |
| Final Assessment | CLO3 | Individual | Week 7 | Week 8 | 30% |
*Items above are based on Table 4
This assignment requires students to apply business analytics techniques by extracting, transforming, and analysing real or simulated business data using appropriate digital tools. Students will develop statistical models, interpret analytical results, and communicate insights to support data-driven business decision-making.
a. Describe the business context and problem addressed using analytics.
(3 marks)
b. Explain the role of business analytics in supporting managerial decision-making in the chosen sector.
(3 marks)
c. Identify the type of analytics applied (descriptive, diagnostic, inferential).
(4 marks)
a. Explain the data source and data structure.
(5 marks)
b. Demonstrate the Extract–Transform–Load (ETL) process:
i. Data extraction method
ii. Data cleaning and transformation (handling missing values, outliers, variable transformation) iii. Data loading into analytical software
(10 marks)
c. Use appropriate digital tools (e.g., Excel, SPSS, Python, R, Power BI, Tableau). Screenshots or output tables must be included.
(10 marks)
For the data attached students_ai_usage.csv complete the following task:
a. Descriptive Analytics
i. Apply descriptive statistics (mean, median, standard deviation).
(5 marks)
ii. Create appropriate data visualisations (charts, graphs, dashboards).
(5 marks)
iii. Summarise and explain the key patterns and trends.
(7 marks)
b. Inferential Analytics
i. Formulate at least one research hypothesis.
(3 marks)
ii. Apply inferential statistical techniques (e.g., confidence intervals, hypothesis testing, correlation or simple regression).
(10 marks)
iii. Interpret the results in a business context.
(5 marks)
a. Translate analytical findings into actionable business insights.
(5 marks)
b. Propose data-driven recommendations to address the identified business problem.
(5 marks)
c. Discuss how analytics supports adaptive decision-making in a dynamic business environment.
(10 marks)
a. Present findings in a clear, professional analytical report format.
(5 marks)
b. Demonstrate effective use of digital analytics tools.
(3 marks)
c. Ensure clarity, logical flow, and appropriate referencing.
(2 marks)
DUE DATE: Week 6 (17th April 2026)
This group assignment requires students to integrate econometric modeling, operational analytics, and data visualisation to solve a complex business problem. Students will design sustainable, data-driven models and explicitly incorporate health and well-being considerations into managerial decisionmaking.
You and your group members are required to complete the following task using the following data provided: employee_stress_dataset_2000.csv
a. Clearly define the complex business problem and organisational context.
( 5 marks)
b. Explain why the problem requires an integrated analytics approach.
( 5 marks)
c. Develop a conceptual problem-solving framework linking sustainability, health, and well-being outcomes
( 5 marks)
Groups must apply at least ONE appropriate econometric approach:
a. OLS regression with CLRM assumptions
b. Hypothesis testing and diagnostic checks
c. Time series or panel data models (if applicable)
d. Binary or limited dependent variable models (if relevant)
Required:
1. Model specification and justification
(5 marks)
2. Interpretation of coefficients in a business and well-being context
(15 marks)
3. Discussion of causality, limitations, and sustainability implications
(10 marks)
Apply at least ONE operational analytics technique:
a. Supply chain analytics
b. Simulation modeling
c. Optimization models
Students must:
1. Explain model assumptions
(5 marks)
2. Demonstrate how the model improves efficiency, resilience, or sustainability
(10 marks)
3. Discuss implications for employee health, workload, or organisational well-being
(10 marks)
a. Design a management dashboard using appropriate visualisation principles.
(10 marks)
b. Develop KPIs that reflect:
i. Operational performance
ii. Sustainability outcomes
iii. Health and well-being indicators
(5 marks)
c. Explain how managers can use the dashboard as a management cockpit for adaptive decisionmaking.
(5 marks)
a. Critically discuss how analytics-driven decisions affect:
i. Employee health and well-being
ii. Sustainable organisational performance
(5 marks)
b. Propose ethically responsible, sustainable recommendations supported by analytics results.
(5 marks)
DUE DATE: Week 6 (17th April 2026)
This reflective assignment aims to evaluate students’ ability to critically reflect on their learning experience in applying analytics tools and frameworks to health and well-being–related business challenges. Students are expected to connect theoretical knowledge, practical analytics applications, and personal learning insights to sustainable and ethical decision-making.
Students must reflect on their learning experience from the group analytics assignment or related coursework activities involving:
a. Econometric modeling
b. Operational analytics
c. Data visualisation and dashboards
d. Health, well-being, and sustainability considerations
Reflection Guidelines (Students MUST address ALL sections)
1. Learning Experience and Skill Development Reflect on:
a. What you learned about applying analytics tools and models
b. How your understanding of data-driven decision-making evolved
c. Skills developed (e.g., problem-solving, analytical thinking, digital skills)
2. Health, Well-Being, and Sustainability Perspective Discuss:
a. How analytics can influence employee health and well-being
b. Trade-offs between efficiency, performance, and well-being
c. Ethical considerations in analytics-driven decisions
3. Challenges, Insights, and Personal Growth Reflect on:
a. Key challenges faced during analysis or group work
b. How these challenges were addressed
c. Insights gained about responsible and sustainable analytics use
4. Future Application Explain:
a. How this learning experience will influence your future academic or professional practice
b. How you would apply analytics responsibly to support sustainable and healthy organisations
Write in first person (reflective writing style).
Use clear, structured paragraphs (subheadings encouraged).
Support reflections with specific examples from your learning experience.
Maintain academic integrity (cite any sources if referenced).
Submissions outside the 500–700 word range may be penalised. Soft copy submission (PDF/Word)
DUE DATE: Week 6 (17th April 2026)
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