Course Code: ANL303
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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 learn to apply data analytics using Python. Students with no prior knowledge of Python are strongly encouraged to complete Python for Data Analytics (ANL252) before taking this course.
Level: 3
Credit Units: 5
Presentation Pattern: EVERY REGULAR SEMESTER

Topics

  • Introduction to data mining
  • Types of data mining
  • Data mining in Google Colab
  • Data exploration
  • Data preparation – Data cleaning
  • Data preparation – Data transformation
  • Introduction to association rule mining
  • Introduction to cluster analysis
  • Introduction to predictive modelling
  • Model construction
  • Model evaluation and result interpretation
  • Cross-industry standard process for data mining

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
Full-time Programmes Part-time Programmes / CET Courses