Section outline

  • Collapse Expand

    General

    Collapse all Expand all
    • LIVE CLASS External tool

    • Course Brief

      Following the foundational concepts, the course delves into the realm of Business Intelligence (BI). Students explore the role of BI in organizational strategy, focusing on how data analytics can inform business decisions and improve operational efficiency.

      Hands-on experience is a critical component of the course, as students engage with popular data mining software and BI tools, such as R, Python, Tableau, and SQL databases. Through practical assignments and projects, learners apply theoretical knowledge to real-world scenarios, enhancing their analytical skills and fostering a deeper understanding of data interpretation and visualization.

      By the end of the course, students are expected to demonstrate proficiency in employing data mining techniques to extract actionable insights and utilize BI tools to support strategic business decisions. The course prepares graduates for roles in data analysis, business intelligence, and strategic planning, equipping them with the skills necessary to thrive in an increasingly data-centric business environment.

    • Learning Objectives

      The course is intended to achieve the following learning objective and outcomes

      1.     Understanding Core Concepts

      Level: Understand

      • Learning Objective: Understand fundamental concepts, techniques, and tools of data mining and business intelligence.
      • Learning Outcome: By the end of the course, students will explain key concepts of data mining, knowledge discovery, and business intelligence frameworks, including data warehouses, OLAP, and ETL processes.

      2. Data Preprocessing and Management

      Bloom’s Level: Apply

      • Learning Objective: Develop skills to collect, clean, and prepare data for analysis.
      • Learning Outcome: Students will demonstrate the ability to preprocess raw datasets, handle missing values, normalize data, and transform it for mining and analysis.

      3. Applying Data Mining Techniques

      Level: Apply / Analyze

      • Learning Objective: Apply statistical and machine learning methods for extracting patterns and insights from datasets.
      • Learning Outcome: Students will analyze datasets using classification, clustering, regression, and association rule mining techniques, and interpret the results to make data-driven business decisions.

      4. Business Intelligence and Decision Support

      Level: Analyze / Evaluate

      • Learning Objective: Integrate data mining insights into business intelligence frameworks to support strategic decision-making.
      • Learning Outcome: Students will design dashboards, reports, and predictive models that summarize data insights and support managerial decision-making, demonstrating the value of BI systems.

      5. Critical Evaluation and Innovation 

      Level: Evaluate / Create

      • Learning Objective: Critically evaluate and innovate solutions for complex business problems using BI and data mining.

      Learning Outcome:

      Students will formulate and justify innovative solutions for real-world business scenarios using advanced data mining and BI techniques, including predictive and prescriptive analytics.

  • Collapse Expand

    Detailed Course Outline

  • Collapse Expand

    Introduction to Data Mining & Business Intelligence

  • Collapse Expand

    Data Preparation & Preprocessing

    • Topic 2 SCORM package
      •   Data cleaning, integration, transformation
      •   Handling missing data and noise
      •   Feature selection and dimensionality reduction
  • Collapse Expand

    Classification Fundamentals

  • Collapse Expand

    Advanced Classification Techniques

  • Collapse Expand

    Clustering Methods

  • Collapse Expand

    Association Rule Mining

  • Collapse Expand

    Regression & Predictive Modeling

  • Collapse Expand

    Text Mining & Social Media Analytics

  • Collapse Expand

    Ethical, Security, and Managerial Aspects

    • Case and Articles Presentation submissions Assignment
      Opened: Sunday, 28 September 2025, 12:00 AM
      Due: Sunday, 5 October 2025, 12:00 AM

      Assignment 3: Case/Paper review

      List of Cases and Articles for analysis and Review

      Instructions

      1.     Choose 1 topic

      2.     Download the article/Case

      3.     Provide a detailed analysis based on the questions appended.

      4.     Create Powerpoint presenation

      5.     Submit online and be ready to present on 2nd October, 2025

      Submit Cases and Articles presentations here

    • Casework File
    • Cases and links File
    • Choice of Topic Assignment
      Opened: Monday, 29 September 2025, 12:00 AM
      Due: Saturday, 4 October 2025, 12:00 AM

      Please list your choice of topic here

  • Collapse Expand

    Emerging Trends: Self study topic and class presentations