Section outline

  • Data Mining Online Class (Sections: PC-C, PC-D,PC-F)

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    Join Data Mining Online Class

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    • Students must
      Mark as done

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    • url icon
      Class and Counseling Hour URL
      Students must
      Mark as done
    • Students must
      Mark as done


      Join Telegram Group : https://t.me/joinchat/cHUiAErgieMxNGVl

    • assign icon
      Assignment PC-C
      Not available unless: You belong to PC-C
    • assign icon
      Assignment PC-D
      Not available unless: You belong to PC-D
    • assign icon
      Assignment PC-C F
      Not available unless: You belong to PC-F
    • forum icon
      Announcements Forum

  • Week 1: Ice-Breaking and Course Overview

    Instructor's  Note & Course Overview

    ABOUT COURSE

    GENERAL GUIDELINES ABOUT THE COURSE

    COURSE RATIONALE

    COURSE OBJECTIVES

    COURSE TOPICS (WEEK-WISE)

    GLOBAL DATA REPOSITORY for DATA MINING

    FOR STUDENT SUPPORT

    Student Interest Survey

  • Week 2: Introduction

    LESSON OBJECTIVE

    • To teach the importance of data & data mining in various field of science, technology & business

    LEARNING OUTCOMES

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

    TOPICS

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

    CONTENTS

  • Week 3: Working with data

    LESSON OBJECTIVE

    • To be introduced with data warehousing & data mining tasks in project

    LEARNING OUTCOMES

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

    TOPICS

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

    CONTENTS

  • Week 4: Data Exploration

    LESSON OBJECTIVE

    • To understand data preparation, visulization and data characteristics

    LEARNING OUTCOMES

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

    TOPICS

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

    CONTENTS

  • Week 5: Classification & Prediction

    LESSON OBJECTIVE

    • To understand the classification & prediction in data mining task

    LEARNING OUTCOMES

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

    TOPICS

    • Classification and Prediction
    • Classification: tree-based approaches

    CONTENTS

    • Video Resources
    • book icon
      Introduction to classification (videos) Book

    • Reading Materials

    • book icon
      Introduction to classification (readings) Book

    • Laboratory Materials

    • book icon
      Lab: Working with python & classification Book

    • Weekly Quiz (adaptive)

    • lesson icon
      Weekly_Quiz (deadline: 29 October) Lesson
      Students must
      View
      Go through the activity to the end

    • forum icon
      Week 5 Discussion Forum
      Students must
      View
      Make forum posts: 1

      You are expected to give at least one post under each discussion topic

    • assign icon
      Quiz: 03 PC-C (Click here to get the question) Assignment

      Date/ Time: 17 December, 08:00pm [Ensure your laptop/ phone is fully charged before attempting to the test]
      Syllabus: K means Clustering
      Question Type: Open Book
      Open Book Test Guidelines
      During this open book test, you need to ensure the following items and code of conducts: 
      (1) Books, online repository and other relevant materials 
      (2) Your other supporting materials like clock, pen, pencil, eraser, calculator etc.
      (3) Drink water and have snacks 
      (4) No discussion in group or with your classmates as it will be reflected in your answers 
      (5) No sharing of codes, write-ups or ideas as this will be detected and will lead to negative marking 

      Total Test Time: 
      40 mins ( 30 mins for answering the questions + 10 mins for submitting/ uploading the answers to BLC in the form of pdf). Penalty will be incurred if failed to upload within the given time.
      Note:  **Any kind of email submission is not allowed

      Not available unless: You belong to PC-C
    • assign icon
      Quiz: 03 PC-D (Click here to get the question) Assignment

      Date/ Time: 17 December, 08:00pm [Ensure your laptop/ phone is fully charged before attempting to the test]
      Syllabus: K means Clustering
      Question Type: Open Book
      Open Book Test Guidelines
      During this open book test, you need to ensure the following items and code of conducts: 
      (1) Books, online repository and other relevant materials 
      (2) Your other supporting materials like clock, pen, pencil, eraser, calculator etc.
      (3) Drink water and have snacks 
      (4) No discussion in group or with your classmates as it will be reflected in your answers 
      (5) No sharing of codes, write-ups or ideas as this will be detected and will lead to negative marking 

      Total Test Time: 
      40 mins ( 30 mins for answering the questions + 10 mins for submitting/ uploading the answers to BLC in the form of pdf). Penalty will be incurred if failed to upload within the given time.
      Note:  **Any kind of email submission is not allowed

      Not available unless: You belong to PC-D
    • assign icon
      Quiz: 03 PC-F (Click here to get the question) Assignment

      Date/ Time: 17 December, 08:00pm [Ensure your laptop/ phone is fully charged before attempting to the test]
      Syllabus: K means Clustering
      Question Type: Open Book
      Open Book Test Guidelines
      During this open book test, you need to ensure the following items and code of conducts: 
      (1) Books, online repository and other relevant materials 
      (2) Your other supporting materials like clock, pen, pencil, eraser, calculator etc.
      (3) Drink water and have snacks 
      (4) No discussion in group or with your classmates as it will be reflected in your answers 
      (5) No sharing of codes, write-ups or ideas as this will be detected and will lead to negative marking 

      Total Test Time: 
      40 mins ( 30 mins for answering the questions + 10 mins for submitting/ uploading the answers to BLC in the form of pdf). Penalty will be incurred if failed to upload within the given time.
      Note:  **Any kind of email submission is not allowed

      Not available unless: You belong to PC-F

  • Presentation

    Presentation Mode & Topics:

    • Presentation will be individual and recorded
    • Every student will present the dataset (.csv format/ others if data cannot be represented in .csv) and their project work briefly as discussed in the class. They will focus on the motivation & outcomes of their work

    General Instructions for the Presentation:

    • In presentation, marks will be given on: getup & outfit, body language, English command, eye contact, content knowledge, Q/A
    • Try to prepare the slides with more figures and less text. The font size of text should be larger so that it can be seen from the back bench of your class (if your are presenting through online due to COVID-19 pandemic, you are advised to ignore slides, instead prepare recorded presentation as per guidelines given to the link below)
    • Give slide number (during pandemic, you are advised to ignore slides)
    • More instructions will be discussed in the class

    A few Examples from previous good Presentations

  • Week 8: Associate Rules Mining

    LESSON OBJECTIVE

    • To understand Association rule mining and apriori algorithm

    LEARNING OUTCOMES

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

    TOPICS

    • Association Rule Mining
    • Problem Solving using association rule mining

    CONTENTS

  • Week 9: Working with Clustering

    LESSON OBJECTIVE

    • To understand Clustering algorithm

    LEARNING OUTCOMES

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

    TOPICS

    • Clustering Data
    • Problem Solving using Clustering

    CONTENTS