Singapore University of Social Sciences

Fundamentals of Data Mining

Fundamentals of Data Mining (ANL303)

Applications Open: 01 October 2020

Applications Close: 30 November 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


Synopsis

Fundamentals of Data Mining (ANL303) introduces students to the process and applications of data mining. Students will learn to appraise possible data mining solutions to address different types of business problems. Apart from learning to explore and prepare data for mining, they will be equipped with the basic skills and knowledge in constructing, interpreting, and evaluating data mining results or models. They will also be exposed to applying data analytics using a suite of tools including cloud services.

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

Topics

  • Introduction to data mining
  • Types of data mining
  • Applications of data mining
  • Cross-industry standard process for data mining
  • General data quality issues and treatments
  • Preparing data for different types of mining tasks
  • Introduction to association rule mining
  • Introduction to cluster analysis
  • Introduction to predictive modelling
  • Model evaluation and result interpretation
  • Introduction to clouds analytics
  • Applying clouds analytics

Learning Outcome

  • Discuss various aspects of formulating data analytics solutions
  • Appraise the application of data analytics in a given context
  • Recommend appropriate analytics solutions in a given context
  • Construct analytics models/results as part of solutions to address business problems
  • Evaluate the performance of analytics models
  • Analyse the results or outputs of analytics models
Back to top
Back to top