Data Mining and Data Warehousing Syllabus - BIM (TU)

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Course Description

Course Objective

The objective of the course is to make learner understand foundation principles and techniques

of data mining and data warehousing. Students will be able to select and use various data mining language and tools very useful for adding business value of an organization.

Course Description

Introduction, Data Preprocessing- Data Integration and Transformation, Classification, Association Analysis, Cluster Analysis, Information Privacy and Data Mining, Advanced Applications, Search engines, Data Warehouses, Capacity Planning.

 

Unit Contents

Course Details

Unit 1: Introduction                                                                                         LH 2

  • Data Mining Origin
  • Data Mining & Data Warehousing basics

 

Unit 2:     Data Preprocessing                                                                         LH 6

  • Data Types and Attributes
  • Data Pre-processing
  • OLAP
  • Characteristics of OLAP Systems
  • Multidimensional View and Data cube
  • Data Cube Implementation
  • Data Cube Operations
  • Guidelines for OLAP Implementation

 

Unit 3:    Classification                                                                                      LH 7

  • Basics and Algorithms
  • Decision Tree Classifier
  • Rule Based Classifier
  • Nearest Neighbor Classifier
  • Bayesian Classifier
  • Artificial Neural Network Classifier
  • Issues : Overfitting, Validation, Model Comparison

 

Unit 4:      Association Analysis                                                                            LH 7

  • Basics and Algorithms
  • Frequent Itemset Pattern & Apriori Principle
  • FP-Growth, FP-Tree
  • Handling Categorical Attributes

 

Unit 5:

5.1.

Cluster Analysis

Basics and Algorithms

LH 7

5.2.

K-means Clustering

 

5.3.

Hierarchical Clustering

 

5.4.

DBSCAN Clustering

 

Unit 6:

6.1

Information Privacy and Data Mining

Basic principles to Protect Information Privacy

LH 3

6.2

Uses and Misuses of Data Mining

 

6.3

Primary Aims of data Mining

 

6.4

Pitfalls of Data Mining

 

 

Unit 7:       Advanced Applications                                                                         LH 3

  • Web-mining: Web content mining, web usage mining
  • Time-series data mining

 

Unit 8: Search Engines                                                                                            LH 3

  • Characteristics of search engine
  • Search Engine functionality
  • Ranking of Web pages

 

Unit 9: Data Warehousing                                                                                      LH 7

 

9.1

Operational Data sources

 

9.2

ETL (Extract, Transform, Load)

 

9.3

Data Warehouse Processes, Managers and their functions

 

9.4

Data Warehouses and Data Warehouses Design

 

9.5

Guidelines for Data Warehouse Implementation

 

Unit 10

10.1

Capacity Planning

Calculating storage requirement, CPU requirements

LH 3

 

Practical:

Students should practice enough on real-world data intensive problems

Text and Reference Books

References:

  • Pang-NingTan, Michael Steinbach and Vipin Kumar, Introductionto Data Mining, 2005, Addison-
  • Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd Edition, 2006, Morgan
  • K. Gupta, Introduction to Data Mining with Case Studies, Prentice Hall of India
  • IBM, An Introduction to Building the Data Warehouse, Prentice Hall of India
  • IBM, Introduction to Business Intelligence and Data Warehousing, Prentice Hall of India
  • Adriaans Pieter, Zantige, "Data Mining", Pearson Education Asia Pub. Ltd, 2002
Download Syllabus
  • Short Name N/A
  • Course code IT 308
  • Semester Eighth Semester
  • Full Marks 100
  • Pass Marks 45
  • Credit 3 hrs
  • Elective/Compulsary Elective