Data Mining (Fall 2021)

Section outline

• Select section Welcome to Data Mining

  Welcome to Data Mining

  Collapse all Expand all

  Course Code: CSE 450	Course Title: Data Mining
  Program: BSC in CSE	Faculty: Faculty of Science and Information Technology (FSIT)
  Semester: FALL 2021	Year: 2021
  Credit: 3     Course Hours: 3 hrs./week	Course Level: Level 4, Term 2
  Course Category: Core

  
  

  Instructor : Dr. Fizar Ahmed Office : Room 411, CSE Building, Daffodil Tower  Office Hour   :  Saturday to Wednesday (9:00AM to 5 PM) Telephone : 01775695814 Email              : fizar.cse@diu.edu.bd Appointment in Google Calendar: Click Here	Welcome Information on Data Mining Course Welcome Audio Listen to Course Objectives Listen to Expected Outcomes Listen to Course Delivery Plan Some Successful Projects Student Interests Survey Online Python Compiler for Data Mining Lab

  
  

  
  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 Learning Outcome: (at the end of the course, student will be able to do:)
  

  CLO1	Able to conceptualize basic applications, concepts, and techniques of data mining
  CLO2	Able to identify appropriate data mining algorithms to solve real world problems
  CLO3	Able to compare and evaluate different data mining techniques like classification, prediction, clustering and association rule mining
  CLO4	Able to apply knowledge of data mining in developing research ideas

  
  

  Grading Scheme

  Attendance: 7% Class Tests/Quizes:  15% Assignment: 5% Presentation (using video/ppt): 8% Midterm Exam: 25% Final Exam: 40%

  
  

  • Text Book

  1. Introduction to Data Mining by Tan

  • Reference Reading Materials

  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

  1. WISDM
     
  2. UCI ML Repository
     
  3. KDD Cup
     
  4. Kaggle
     
  5. KDnuggets
     

  • Standard Templates

  1. IEEE Template
  2. ACM Template

  
  

  • Select activity Announcements

    

    Announcements Forum
  • Select activity Course Outline

    Completion

    Students must

    

    Mark as done

    Course Outline

  • Select activity Class Video Link

    Completion

    Students must

    

    Mark as done

    Class Video Link

  • Select activity CSE450(B) (2021-09-22 at 22:40 GMT-7)

    

    CSE450(B) (2021-09-22 at 22:40 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(F) (2021-09-22 at 21:16 GMT-7)

    

    CSE450(F) (2021-09-22 at 21:16 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(A) (2021-09-22 at 03:21 GMT-7)

    

    CSE450(A) (2021-09-22 at 03:21 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(E) (2021-09-22 at 00:16 GMT-7)

    

    CSE450(E) (2021-09-22 at 00:16 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(B) (2021-09-26 at 00:08 GMT-7)

    

    CSE450(B) (2021-09-26 at 00:08 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE412(PC-B) (2021-09-27 at 01:58 GMT-7)

    

    CSE412(PC-B) (2021-09-27 at 01:58 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(F) (2021-09-27 at 20:37 GMT-7)

    

    CSE450(F) (2021-09-27 at 20:37 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(E) (2021-09-29 at 00:44 GMT-7)

    

    CSE450(E) (2021-09-29 at 00:44 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(A) (2021-10-04 at 22:43 GMT-7)

    

    CSE450(A) (2021-10-04 at 22:43 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(F) (2021-10-04 at 19:51 GMT-7)

    

    CSE450(F) (2021-10-04 at 19:51 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(F) (2021-10-18 at 19:57 GMT-7)

    

    CSE450(F) (2021-10-18 at 19:57 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(A) (2021-10-18 at 22:50 GMT-7)

    

    CSE450(A) (2021-10-18 at 22:50 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(F) (2021-10-20 at 21:16 GMT-7)

    

    CSE450(F) (2021-10-20 at 21:16 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(F) (2021-10-20 at 21:16 GMT-7)

    

    CSE450(F) (2021-10-20 at 21:16 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(B) (2021-10-20 at 22:50 GMT-7)

    

    CSE450(B) (2021-10-20 at 22:50 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(A) (2021-10-25 at 22:45 GMT-7)

    

    CSE450(A) (2021-10-25 at 22:45 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(F) (2021-10-25 at 19:53 GMT-7)

    

    CSE450(F) (2021-10-25 at 19:53 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(E) (2021-10-26 at 00:12 GMT-7)

    

    CSE450(E) (2021-10-26 at 00:12 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(F) (2021-10-27 at 21:14 GMT-7)

    

    CSE450(F) (2021-10-27 at 21:14 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(A) (2021-10-27 at 02:08 GMT-7)

    

    CSE450(A) (2021-10-27 at 02:08 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(E) (2021-10-27 at 00:27 GMT-7)

    

    CSE450(E) (2021-10-27 at 00:27 GMT-7) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(F) (2021-11-22 at 19:05 GMT-8)

    

    CSE450(F) (2021-11-22 at 19:05 GMT-8) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(E) (2021-11-23 at 23:16 GMT-8)

    

    CSE450(E) (2021-11-23 at 23:16 GMT-8) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(F) (2021-11-29 at 19:18 GMT-8)

    

    CSE450(F) (2021-11-29 at 19:18 GMT-8) URL

    Completion

    Students must

    

    Mark as done
  • Select activity CSE450(A) (2021-12-06 at 22:06 GMT-8)

    

    CSE450(A) (2021-12-06 at 22:06 GMT-8) URL

    Completion

    Students must

    

    Mark as done
• Select section Week 1: Introduction

  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

  • Select activity Class Discussion Slide

    Completion

    Students must

    

    Mark as done

    Class Discussion Slide

  • Select activity Working with Python Basics

    Completion

    Students must

    

    Mark as done

    Working with Python Basics

  • Select activity Working with Python and NumPy

    Completion

    Students must

    

    Mark as done

    Working with Python and NumPy
    

  • Select activity Working with NumPy Part 2Download Data File

    Completion

    Students must

    

    Mark as done

    • Working with NumPy Part 2
    • Download Data File

    
    

  • Select activity Working with Data File

    Completion

    Students must

    

    Mark as done

    Working with Data File

• Select section Week 2: Working with Data

  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

  
  

  • Project Guideline

  • Select activity Class Discussion Slide

    Completion

    Students must

    

    Mark as done

    Class Discussion Slide

  • Select activity Lab Session 2: Working with Data Preprocessing in ...

    Completion

    Students must

    

    Mark as done

    Lab Session 2: Working with Data Preprocessing in Weka or Jupyter Notebook

  • Select activity Working with Python and PandaDownload CSV File

    Completion

    Students must

    

    Mark as done

    • Working with Python and Panda
    • Download CSV File

• Select section Week 3: Data Exploration

  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

  • Select activity Class Discussion Slide

    Completion

    Students must

    

    Mark as done

    Class Discussion Slide

  • Select activity Additional Reading

    Completion

    Students must

    

    Mark as done

    Additional Reading

  • Select activity Working with Python and Panda-Part 1Download Data ...

    Completion

    Students must

    

    Mark as done

    • Working with Python and Panda-Part 1
    • Download Data Fle-Part 1
    • Working with Python and Panda-Part 2
    • Download Data File-Part 2

• Select section Week 4: Classification and Prediction

  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

  • Select activity Class Discussion Slide

    Completion

    Students must

    

    Mark as done

    Class Discussion Slide

  • Select activity Discussion on Decision Tree

    Completion

    Students must

    

    Mark as done

    Discussion on Decision Tree

  • Select activity Working with Python and ClassificationDownload Dat...

    Completion

    Students must

    

    Mark as done

    • Working with Python and Classification
    • Download Data File
    • Working with Python and Classification-Part 2
    • Download Data File for Part 2

  • Select activity Tutorials Point Lab Practice: scikit-learn

    

    Tutorials Point Lab Practice: scikit-learn File

    Completion

    Students must

    

    Mark as done
  • Select activity Another PPT of Classification

    

    Another PPT of Classification File

    Completion

    Students must

    

    Mark as done
  • Select activity Another Resource of Entropy and Gain

    

    Another Resource of Entropy and Gain File

    Completion

    Students must

    

    Mark as done
• Select section Week 5: Classification Tuning

  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

  
  

  • Select activity Class Discussion Slide

    Completion

    Students must

    

    Mark as done

    Class Discussion Slide

  • Select activity Working with Python and Regression

    Completion

    Students must

    

    Mark as done

    Working with Python and Regression
    
    

  • Select activity Decision Tree pdf

    

    Decision Tree pdf File

    Completion

    Students must

    

    Mark as done
  • Select activity Decision Tree Entopy Gain

    

    Decision Tree Entopy Gain File

    Completion

    Students must

    

    Mark as done
  • Select activity Gini Index

    

    Gini Index File

    Completion

    Students must

    

    Mark as done
• Select section Week 6: Nearest Neighbor and Bayesian Classification

  Week 6: Nearest Neighbor and Bayesian Classification

  Topics of Discussion

  • Nearest Neighbor Classifier
  • Bayesian Classification

  
  

  Expected Learning Outcome

  • Understand nearest neighbor classification
  • Problem solving using Weka

  • Select activity Class Discussion on Bayes Theorem Slide

    Completion

    Students must

    

    Mark as done

    Class Discussion on Bayes Theorem Slide

  • Select activity Class Discussion on Nearest Neighbor Slide

    Completion

    Students must

    

    Mark as done

    Class Discussion on Nearest Neighbor Slide

  • Select activity Class Discussion on Nearest Neighbor Classificatio...

    Completion

    Students must

    

    Mark as done

    Class Discussion on Nearest Neighbor Classification Slide

  • Select activity Working with Python and Nearest NeighborDownload D...

    Completion

    Students must

    

    Mark as done

    • Working with Python and Nearest Neighbor
    • Download Data File

  • Select activity Bayes rule

    

    Bayes rule File

    Completion

    Students must

    

    Mark as done
  • Select activity Class Activities Resource Link on Bayes Classifier

    

    Class Activities Resource Link on Bayes Classifier URL

    Completion

    Students must

    

    Mark as done
  • Select activity Class Test

    

    Class Test Quiz

    Completion

    Students must

    

    Mark as done
• Select section Week 7: Mid Exam

  Week 7: Mid Exam

  
  

  Topics to be covered in midterm exam:

  • Data Pre-processing
  • Classification and Prediction

  • Select activity Answer Script Template File

    

    Answer Script Template File

    Completion

    Students must

    

    Mark as done

    Please follow the name convention of the answer script file name: Section_StudentID_Name_SubjectCode_FALL2021_mid.pdf

  • Select activity Mid Term Question Paper Fall 2021

    

    Mid Term Question Paper Fall 2021 File

    Completion

    Students must

    

    Mark as done
  • Select activity Fall 2021: Mid Term answer script submission link

    

    Fall 2021: Mid Term answer script submission link Assignment

    Completion

    Students must

    

    Mark as done
• Select section Week 8: Association Rule Mining

  Week 8: 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

  • Select activity Class Discussion on Association Rule Mining Slide

    Completion

    Students must

    

    Mark as done

    Class Discussion on Association Rule Mining Slide

  • Select activity Working with Python and Association Rule MiningDow...

    Completion

    Students must

    

    Mark as done

    • Working with Python and Association Rule Mining
    • Download Data File

  • Select activity Apriori pdf resource

    

    Apriori pdf resource File

    Completion

    Students must

    

    Mark as done
  • Select activity Apriori Report Submission

    

    Apriori Report Submission Assignment

    Completion

    Students must

    

    Mark as done
• Select section Week 9: Working with Clustering

  Week 9: Working with Clustering

  Topics of Discussion

  • Clustering Data
  • Problem Solving using Clustering
  • Course Project Presentation 2

  
  

  Expected Learning Outcome

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

  • Select activity Class Discussion on Clustering Slide

    Completion

    Students must

    

    Mark as done

    Class Discussion on Clustering Slide

  • Select activity Working with Python and Clustering

    Completion

    Students must

    

    Mark as done

    Working with Python and Clustering
    

  • Select activity K-MEAN Clustering

    

    K-MEAN Clustering File

    Completion

    Students must

    

    Mark as done
  • Select activity K-MEAN Clustering Practice

    

    K-MEAN Clustering Practice Assignment

    Completion

    Students must

    

    Mark as done
• Select section Week 10: Neural Network

  Week 10: Neural Network

  Topics of Discussion

  • Neural Network
  • Application of neural network

  
  

  Expected Learning Outcome

  • Apply knowledge of neural network
  • Problem solving

  • Select activity Class Discussion on Neural Network Slide

    Completion

    Students must

    

    Mark as done

    Class Discussion on Neural Network Slide

  • Select activity Basic Neural Network

    

    Basic Neural Network File

    Completion

    Students must

    

    Mark as done
• Select section Week 11: Course Project Discussion

  Week 11: Course Project Discussion

  Topics of Discussion

  • Working with Data Mining Projects
  • Class Test # 3

  
  

  Expected Learning Outcome

  • Ability to apply data mining knowledge in development project

  
  

  • Select activity Project Presentation Guideline

    

    Project Presentation Guideline File

    Completion

    Students must

    

    Mark as done
• Select section Final Examination Summer 2021

  Final Examination Summer 2021