Section outline

  • Welcome to the Data Mining course!

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    Course Teacher Information:

    Rashidul Hasan Hridoy (RHH)
    Lecturer, Department of Computer Science and Engineering
    Daffodil International University
    Mobile: 01714969317
    Email: rashidul.cse0394.c@diu.edu.bd
    ResearchGate: https://www.researchgate.net/profile/Rashidul-Hasan-Hridoy
    Google Scholar: https://scholar.google.com/citations?user=9o4uHPsAAAAJ&hl
    Website: https://rashidulhasanhridoy.com


    Dear Students,

    You are the future leaders of our world, and with great power comes great responsibility. It is important that you utilize your time wisely, as it is a precious resource that cannot be regained once lost. Make sure to prioritize your studies and work hard towards your goals, as success requires dedication and effort.

    But remember, success is not just about personal achievement. It is also about making a positive impact on others and giving back to your community. So, take the time to do good for others, whether it be volunteering, mentoring, or simply being kind.

    In short, utilize your time wisely, study hard, and do good for others. With these principles in mind, you can achieve great things and make a meaningful difference in the world. So go out there and make us proud!

  • Course Details


    Course Rationale
    An introduction to data mining; Data preparation, model building, and data mining techniques such as clustering, decisions trees and neural networks; Induction of predictive models from data: classification, regression, and probability estimation; Application case studies; Data-mining software tools review and comparison.

    Course Objectives

    • To appreciate the necessity of data mining in everyday life
    • To apply the concept of data mining in solving problems
    • To demonstrate applications of data mining using tools
    • To apply knowledge of data mining in project work

    Course Outcomes (CO’s)

    • CO1 Able to grasp the basic Data Mining Principles
    • CO2 Able to identify appropriate data mining algorithms to solve real world problems
    • CO3 Able to compare and evaluate different data mining techniques like classification, prediction, clustering and association rule mining
    • CO4 Able to apply data mining knowledge in problem solving



  • Week 1: Introduction

    Topics of Discussion

    • Introduction to data mining
    • Relationship to data warehousing
    • Why data mining is a discipline?
    • Overview of data mining tasks: Clustering, Classifications, Rules learning etc


    Expected Learning Outcome

    • Appreciation of the needs of data mining
    • Visualization of data warehouse and relationship to data mining
    • Visualization of different data mining tasks

    • resource icon
      Lecture Content : Week 1 File
      Students must
      Mark as done
    • assign icon
      Week 1 Assignment
      Opened: Friday, 3 March 2023, 12:00 AM
      Due: Friday, 31 March 2023, 12:00 AM
      Students must
      Mark as done
    • folder icon
      Supporting study materials Folder
      Students must
      Mark as done
    • assign icon
      Week 2 Assignment
      Opened: Sunday, 2 April 2023, 12:00 AM
      Due: Friday, 28 April 2023, 12:00 AM
      Students must
      Mark as done

  • Week 2: Working with Data

    Topics of Discussion

    • Review of data mining task and related application examples
    • Data Warehousing Introduction
    • Course Project Team and discussion


    Expected Learning Outcome

    • Identification of data mining task
    • Team formation for the course project

    Quiz 1
    Syllabus: Week 1 and 2

    • resource icon
      Lecture Content : Week 2 File
      Students must
      Mark as done
    • assign icon
      Week 2 Assignment
      Opened: Friday, 3 March 2023, 12:00 AM
      Due: Friday, 31 March 2023, 12:00 AM
      Students must
      Mark as done
    • folder icon
      Supporting study materials Folder
      Students must
      Mark as done
    • assign icon
      Week 3 Assignment
      Opened: Sunday, 2 April 2023, 12:00 AM
      Due: Friday, 28 April 2023, 12:00 AM
      Students must
      Mark as done

  • Week 3: Data Exploration

    Topics of Discussion

    • Discussion on data mining process: Data preparation and cleansing and task identification
    • Project Discussion and execution plan

    Expected Learning Outcome

    • Visualization data mining processes
    • Selection of project topic by team

    • resource icon
      Lecture Content : Week 3 File
      Students must
      Mark as done
    • assign icon
      Week 3 Assignment
      Opened: Friday, 3 March 2023, 12:00 AM
      Due: Friday, 31 March 2023, 12:00 AM
      Students must
      Mark as done
    • folder icon
      Supporting study materials Folder
      Students must
      Mark as done
    • assign icon
      Week 4 Assignment
      Opened: Sunday, 2 April 2023, 12:00 AM
      Due: Wednesday, 3 May 2023, 12:00 AM
      Students must
      Mark as done

  • Week 4: Classification and Prediction

    Topics of Discussion

    • Classification and Prediction
    • Classification: tree-based approaches

    Expected Learning Outcome

    • Problem solving for classification and prediction
    • Using Weka and other DM tools


    Quiz 2

    Syllabus: Week 3 and 4


    • resource icon
      Lecture Content : Week 4 File
      Students must
      Mark as done
    • assign icon
      Week 4 Assignment
      Opened: Friday, 3 March 2023, 12:00 AM
      Due: Friday, 31 March 2023, 12:00 AM
      Students must
      Mark as done
    • quiz icon
      Quiz - 1
      Opened: Tuesday, 28 March 2023, 12:00 PM
      Closed: Tuesday, 28 March 2023, 12:45 PM
      Students must
      Mark as done
    • assign icon
      Week 4 - Assignment
      Opened: Tuesday, 28 March 2023, 12:00 AM
      Due: Tuesday, 4 April 2023, 12:00 AM
      Students must
      Mark as done

  • Week 5: Classification Tuning

    Topics of Discussion

    • Classification and Prediction with tuning
    • Classification: tree-based approaches
    • Course Project Presentation 1

    Expected Learning Outcome

    • Problem solving for classification and prediction
    • Using Weka and other DM tools

    • resource icon
      Lecture Content : Week 5 File
      Students must
      Mark as done
    • assign icon
      Week 5 Assignment
      Opened: Friday, 3 March 2023, 12:00 AM
      Due: Friday, 31 March 2023, 12:00 AM
      Students must
      Mark as done
    • folder icon
      Supporting study materials Folder
      Students must
      Mark as done
    • assign icon
      Week 6 - Assignment
      Opened: Sunday, 2 April 2023, 12:00 AM
      Due: Friday, 28 April 2023, 12:00 AM
      Students must
      Mark as done

  • Week 6: Review Week

    Topics For Discussion:

    1. Review for the MID Term Examinations
    2. Problem solving Session

    Ref. Contents of Week 1 – Week 5

    • folder icon
      Supporting study materials Folder
      Students must
      Mark as done
    • assign icon
      Week 7 Assignment
      Opened: Sunday, 2 April 2023, 12:00 AM
      Due: Friday, 28 April 2023, 12:00 AM
      Students must
      Mark as done

  • Week 7: Mid Term Examination

    Mid Term Examination Syllabus:


    1. Week 1: Introduction

    2. Week 2: Working with Data

    3. Week 3: Data Exploration

    4. Week 4: Classification and Prediction

    5. Week 5: Classification Tuning 


  • Week 8: Nearest Neighbor and Bayesian Classification

    Topics of Discussion

    • Nearest Neighbor Classifier
    • Bayesian Classification


    Expected Learning Outcome

    • Understand nearest neighbor classification
    • Problem solving using Weka

  • Week 9: Association Rule Mining

    Topics of Discussion

    • Association Rule Mining
    • Problem Solving using association rule mining

    Expected Learning Outcome

    • Apply the knowledge of association rule mining
    • Problem solving using Weka


    Quiz 3

    Syllabus: Week 8 and 9


  • Week 10: Working with Clustering

    Topics of Discussion

    • Clustering Data
    • Problem Solving using Clustering


    Expected Learning Outcome

    • Understanding of clustering in data mining
    • Problem solving using clustering

  • Week 11: Neural Network

    Topics of Discussion

    • Neural Network
    • Application of neural network


    Expected Learning Outcome

    • Apply knowledge of neural network
    • Problem solving

  • Week 12

    • Assignment and Presentation
    • Course Project Discussion

    • assign icon
      Assignment Submission
      Opened: Monday, 19 June 2023, 12:00 AM
      Due: Saturday, 8 July 2023, 12:00 AM
      Students must
      Mark as done

  • Week 13: Review and Discussion

    Topics For Discussion:

    1. Review for the Final Examinations
    2. Problem-solving Session

    Ref. Contents of Week 8 – Week 12

  • Week 14: Final Examination

    Final Examination Syllabus:

    1. Week 8: Nearest Neighbor and Bayesian Classification

    2. Week 9: Association Rule Mining

    3. Week 10: Working with Clustering

    4. Week 11: Neural Network


    Congratulations on successfully completing this course! You have proven your dedication and hard work, and I encourage you to continue your pursuit of knowledge in this field. There is always more to learn and discover, and I hope you will take the opportunity to expand your understanding of this subject. Best of luck in your future endeavors!