Topic outline

  • Welcome to Data Mining

    Audio Welcome Note:




    Instructor: Shahana Shultana

    Office : Room # 505, AB-04, Savar
    Cellphone #: 01672860365 
    Email: shahana.cse@diu.edu.bd

    DIU - Daffodil International University



    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 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 possess the basic knowledge of Weka and Python concerning data mining and machine learning
    • CO2 Able to implement different data mining and machine learning algorithms like classification, prediction, clustering and association rule mining to solve real-world problems using Weka and Python
    • CO3 Able to compare and evaluate different data mining and machine learning algorithms like classification, prediction, clustering and association rule mining using Weka and/or Python
    • CO4 Able to apply implementation knowledge of data mining and machine learning in developing research ideas
    Grading Scheme
    Attendance: 10%
    Lab Performance: 25%
    Project / Lab Report: 25%
    Final Exam: 40%

    • Recommended Books
    1. Introduction to Data Mining and Applications
    2. Data Mining Concepts and Techniques
    3. Data Mining Techniques
    4. Data Mining Using Weka
    5. Weka Manual
    6. Data Mining Using Python
    • Global Data Repository for Data Mining and/or Machine Learning
    1. WISDM
    2. UCI ML Repository
    3. KDD Cup
    4. Kaggle
    5. KDnuggets

    • Standard Templates
    1. IEEE Template
    2. ACM Template

  • Week 1: Introduction

    2D Graph #graph #chart #gif #animatedgif #gifanimation #loop #seamless #data #infographic #analytics #… | Bar graph design, Data visualization design, Graph design

    Topics of Discussion

    • Relationship to data mining
    • Overview of data mining


    Expected Learning Outcome

    • Appreciation of the needs of data mining 
    • Visualization of the relationship
    • Visualization of different data mining tasks


    • Lab Performance-1 Assignment
      Restricted Not available unless: You belong to PC-J
    • Lab Performance-Visualization from class record Assignment
      Restricted Not available unless: You belong to PC-G
    • LAB-1 URL
      Restricted Not available unless: You belong to PC-J


    • lab-1 URL
      Restricted Not available unless: You belong to PC-G

    • Feedback Choice

      Today's lab class was:

  • Week 2: Data Visualization Using Weka

    Lightning | Data Visualization Server

    Topics of Discussion

    • Review of data mining task and related application examples
    • Data Visualization with Weka
    • Course Project Team and discussion


    Expected Learning Outcome

    • On-hand acquaintance and practice of data visualization with Weka
    • Team formation for the course project


  • Week 3: Transfer Learning

    How can Transfer Learning be a blessing in deep learning models? | by Karan Bhanot | Towards Data Science

    Topics of Discussion

    • Discussion about Transfer Learning
    • Project Discussion and execution plan

    Expected Learning Outcome

    • On-hand acquaintance and practice of Transfer Learning
    • Selection of project topic by team

    • Lab Practice- group (Transfer learning) Assignment
      Restricted Not available unless: You belong to PC-J
    • Lab Performance- Transfer learning (flowers) Assignment
      Restricted Not available unless: You belong to PC-J
    • Feedback Choice

      Today's lab class was:

    • lab-3 URL
      Restricted Not available unless: You belong to PC-G


    • Lab Practice- Transfer Learning Assignment
      Restricted Not available unless: You belong to PC-G

  • Week 4: Regression

    Machine Learning: How Support Vector Machines can be used in Trading - MQL5 Articles

    Topics of Discussion

    • Regression and prediction 
    • Regression: random forest, ridge, gradient boosting, SVR

    Expected Learning Outcome

    • Problem solving skill in regression and prediction
    • Skill in using python as a data mining technique for prediction


    • Lab-3 (pc-j) URL
      Restricted Not available unless: You belong to PC-J


    • Lab performance (regression) Assignment
      Restricted Not available unless: You belong to PC-J
    • Lab performance (regression) Assignment
      Restricted Not available unless: You belong to PC-G
    • Lab-4 URL
      Restricted Not available unless: You belong to PC-G


  • Week 5: Classification

    Topics of Discussion

    • Classification and prediction
    • Classification: Bayesian, Instance-based

    Expected Learning Outcome

    • Problem solving skill in classification and prediction
    • Skill in using Weka as a data mining tool for classification and prediction

  • Week 6: Cluster Analysis Using Weka

    Topics of Discussion

    • Cluster Analysis with Weka
    • Cluster Analysis: partitional (K-means), hierarchical, density-based


    Expected Learning Outcome

    • Problem solving skill in classification and prediction
    • Skill in using Weka as a data mining tool for cluster analysis

  • Week 7: Mid Exam

    Midterm Examination Week




    • Week 8: Presentation of Project # 1 (Using Weka)

      Project # 1 (with Weka) Presentation



      • Project Proposal Assignment
        Restricted Not available unless: You belong to PC-G
      • Project final Assignment
        Restricted Not available unless: You belong to PC-G
      • Project final Assignment
        Restricted Not available unless: You belong to PC-J

    • Week 9: Introduction to Python

      Topics of Discussion

      • Introduction to Python
      • Relationship to machine learning
      • Overview of machine learning with Python

      Expected Learning Outcome

      • Appreciation of the needs of machine learning with Python
      • Visualization of the relationship of Python to machine learning
      • Visualization of different machine learning tasks with Python

    • Week 10: Classification Using Python


      Topics of Discussion

      • Classification and prediction with Python
      • Classification: decision tree

      Expected Learning Outcome

      • Problem solving skill in classification and prediction
      • Skill in using Weka as a data mining tool for classification and prediction

    • Week 11: Classification Using Python (Continued)


      Topics of Discussion

      • Classification and prediction with Python
      • Classification: decision tree


      Expected Learning Outcome

      • Problem solving skill in classification and prediction
      • Skill in using Weka as a data mining tool for classification and prediction

    • Week 12: Cluster Analysis Using Python


      Topics of Discussion

      • Cluster Analysis with Python
      • Cluster Analysis: partitional (K-means), hierarchical, density-based


      Expected Learning Outcome

      • Problem solving skill in classification and prediction
      • Skill in using Weka as a data mining tool for cluster analysis
      • Ability to apply data mining knowledge in development project

    • Week 13: Presentation of Project # 1 (Using Python)

      Project # 2 (with Python) Presentation




      • Week 14: Final Examination

        Semester Final Examination Week


        Topics to be included in final exam:

        • Classification (with Weka and Python)

        • Lab final Assignment
          Restricted Not available unless: You belong to PC-J
        • Lab final Assignment
          Restricted Not available unless: You belong to PC-G

      • Topic 15