Singapore University of Social Sciences

Business Analytics Applications

Business Analytics Applications (ANL309)

Applications Open: 01 October 2020

Applications Close: 15 December 2020

Next Available Intake: January 2021

Course Types: Modular Undergraduate Course, SkillsFuture Series

Language: English

Duration: 6 months

Fees: $1378 View More Details on Fees

Area of Interest: Business Administration

Schemes: Alumni Continuing Education + (ACE+), Lifelong Learning Credit (L2C), Resilience 2020

Funding: SkillsFuture, Union Training Assistance Programme (UTAP)

School/Department: School of Business


ANL309 Business Analytics Applications aims to equip students with the knowledge of various applications of business analytics in different industries. The course covers a wide range of data mining applications, including defect prediction in manufacturing, cross-selling and up-selling for service providers (for e.g., telecommunication) and employee churn in the retail sector. Issues in deployment and other aspects such as model latency, oversampling, conditions for causality and alternative explanation that are critical to business analytics are discussed.

Level: 3
Credit Units: 5
Presentation Pattern: Every January


  • Data mining applications for different industries
  • Defect detection in the manufacturing industry
  • Human resources data mining applications
  • Different types of association rules
  • Latency issues in predictive modelling
  • Validation methods in prediction
  • Use of different data mining techniques for the same business objective
  • Approaches to understanding the neural network “blackbox”
  • Alternative explanations
  • Association versus causation
  • Prior probabilities, imbalanced data and misclassification costs
  • Ethical issues in data mining

Learning Outcome

  • Compare the different modelling techniques used in different industries
  • Describe the different data mining applications used in different industries
  • Discuss issues related to the deployment of data mining models
  • Evaluate the application of business analytics in different industries
  • Recommend the appropriate analytics techniques to derive useful information to support decision-making for a variety of business problems
  • Critique the application of business analytics in different industries
  • Formulate possible application of business analytics in different industries
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