Business Intelligence and Analytics

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  About the Course

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  • Select activity Information about the Course

    Course Name: Data Mining and Business Intelligence

    Course Code: PGDBD 5103

    Course Level: 1

    Credit Units: 5

    Credit Hours: 75

    Facilitator: Dr. John Paul Kasse

    Description

    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.

  • Select activity Learning Objectives and Outcomes

    

    Learning Objectives and Outcomes Forum
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    a.     Learning Objectives and Outcomes

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

    1.     1. Understanding Core Concepts

    • 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

    • 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

    • 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

    • 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 

    • 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.

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    LIVE CLASS External tool
• Select section Introduction to Data Mining & Business Intelligence

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  Introduction to Data Mining & Business Intelligence

  • Business value of data mining
  • BI frameworks and architectures
  • Role of data warehouses (ETL, OLAP, data marts)

  Reading

  1.Han, Kamber & Pei, Data Mining: Concepts and Techniques (Chapter. 1)

  2. Moss, Business Intelligence Roadmap (Chapter. 1–2)

  ·       -Lab: Introduction to BI tools (Power BI/Tableau)  creating first dashboards

  -Case Study: Netflix data-driven recommendation systems
• Select section Data Preparation & Preprocessing

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  Data Preparation & Preprocessing

  •   Data cleaning, integration, transformation
  •   Handling missing data and noise
  •   Feature selection and dimensionality reduction

  Reading

  ·       Han, Kamber & Pei (Ch. 2–3)

  • Lab: Data preprocessing using Python (Pandas/Scikit-learn)
  • Case Study: Retail dataset cleaning for sales forecasting
• Select section Classification Fundamentals

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  Classification Fundamentals

  •      Decision trees, Naïve Bayes, Logistic regression
  •      Supervised vs unsupervised learning

  RReading

  1.       Han, Kamber & Pei (Ch. 6)
  2.       Shmueli et al., Data Mining for Business Analytics (Ch. 4)

  • Lab: Implementing decision trees & Naïve Bayes in Python/R
  •       Case Study: Credit scoring with classification
• Select section Advanced Classification Techniques

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  Advanced Classification Techniques

  k-NN, Support Vector Machines (SVM), Ensemble methods (Random Forest, Boosting)

  Reading

  Han, Kamber & Pei (Ch. 6.7–6.8)

  • Lab: Building ensemble classifiers for customer churn prediction
  • Case Study: Telecom customer churn analysis
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