BAT75014 / GSDM7514 Business Analytics Assignment Brief March Semester 2026 | UNITAR

BAT75014 / GSDM7514 Assignment Brief

Learning Outcomes

At the end of the course, the students will be able to:

Course Assessment Components

Formative Assessment

Summative Assessment

 *Items above are based on Table 4

Assessment Details

INDIVIDUAL ASSIGNMENT (100%) 

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.

Task 1 Business Analytics Context (10%)

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)

Task 2 Data Collection And ETL Process (25%)

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)

Task 3 Descriptive And Inferential Analytics (35%)

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)

Task 4 Managerial Insights And Recommendations (20%)

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)

Task 5 Professional Communication And Digital Competency (10%)

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)

Deliverables

1. Individual Report (2,500–3,000 words, excluding references and appendices)
2. Appendices: data tables, screenshots of analysis, statistical outputs
3. Soft copy submission (PDF/Word)
4. Turnitin Report_attach formt page only

DUE DATE: Week 6 (17^th April 2026)

GROUP ASSIGNMENT (100 MARKS)

 Analytical Report + Dashboard / Model Output

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

Task 1: Problem Definition & Integrated Framework (15%) 

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)

Task 2: Econometric Modeling & Analysis (30%)

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: