Business Analytics (CCW331) model question paper

Model Question Paper

Course Title: Business Analytics (CCW331)

Part A: (2 x 10 = 20 Marks)

1. Describe the different types of analytics and their applications in business.
2. Explain the key stages in the analytics life cycle.

3. Discuss the role of data warehouses and data marts in business intelligence.
4. Explain the decision-making process and the role of decision support systems.

5. Define business forecasting and describe the importance of predictive analytics in business.
6. Explain the difference between data-driven and logic-driven models in predictive analytics.

7. Describe the applications of analytics in human resources planning and recruitment.
8. Explain the role of analytics in supply chain logistics and inventory management.

9. Discuss the key components of a marketing strategy and how analytics can enhance it.
10. Explain the application of predictive analytics in understanding customer behavior in marketing.


Part B: (5 x 13 = 65 Marks)

11. (a) Explain the process of data preparation and its significance in the analytics life cycle.
    
    (b) Discuss the steps involved in hypothesis generation and modeling in business analytics.

12. (a) Describe the functions of OLAP in business intelligence and provide examples of its use.
    
    (b) Discuss knowledge management and its importance in business intelligence.

13. (a) Explain the role of machine learning in predictive analytics and its advantages over traditional methods.
    
    (b) Discuss the process of data mining and its application in predictive analysis modeling.

14. (a) Describe how HR analytics can be used to predict the demand for hourly employees for a year. Provide a hypothetical example.
    
    (b) Discuss the role of analytics in supply chain network planning and demand forecasting.

15. (a) Explain how predictive analytics can be used to analyze customer behavior in marketing and sales. Provide an example.
    
    (b) Discuss the importance of sales planning and the role of analytics in optimizing the selling process.

Part C:  (1 x 15 = 15 Marks)

16. (a) A retail company wants to forecast its sales for the next quarter using historical sales data. Develop a simple predictive model using hypothetical data and explain the steps involved in building and validating the model. Discuss how machine learning techniques can improve the accuracy of the forecast.
    
    (b) An organization is facing challenges in managing its workforce due to fluctuating demand. As an HR analyst, design an analytics-driven strategy to predict the demand for hourly employees for the next year. Outline the steps involved, data required, and potential challenges.

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