Download Course Title: DATA MINING AND BUSINESS INTELLIGENCE Credit

Course Title: DATA MINING AND BUSINESS INTELLIGENCE
Credit Units:
Course Level: UG
Course Code: IT402
L
T
P/S
SW/F
W
3
1
-
-
TOTAL
CREDIT
UNITS
4
Course Objectives:
• Approach business problems data-analytically by identifying opportunities to derive business value from data.
• Know the basics of data mining techniques and how they can be applied to extract relevant business intelligence
Pre-requisites: Understanding of basic concepts of Database Management System and Algorithms and Data Structures
Course Contents/Syllabus:
Module I Data Mining
Data mining approaches and methods: concept description, classification, association rules, clustering, Mining complex types of
data, Research trends in data warehousing and data mining. Objectives of Data Mining the Technical context for Data Mining,
machine learning, decision support and computer technology.
Weightage (%)
15%
Module II Data Mining Techniques and Algorithms
Process of data mining, Algorithms, Data base segmentation or clustering, predictive Modeling, Link Analysis, Data Mining
Techniques, Automatic Cluster Detection, Decision trees and Neural Networks.
15%
Module III Classification and Predictions
What is Classification & Prediction, Issues regarding Classification and prediction, Decision tree, Bayesian Classification,
Classification by Back propagation, Multilayer feed-forward Neural Network, Back propagation Algorithm, Classification
methods K-nearest neighbor classifiers, Genetic Algorithm.
Module IV Business Intelligence and Analytics
Definition of Problem (Corporate problems & Issues), BI Architecture spread sheets, concept of dashboard, OLAP, decision
engineering, LIS, BI Tools concept of dashboard, BI Analytics discriminate analysis and logistic regression, cluster analysis,
principle component analysis.
30%
20%
Module V Data Mining for Business Intelligence Applications
Data mining for business Applications like Balanced Scorecard, Fraud Detection, Click stream Mining, Market Segmentation,
retail industry, telecommunications industry, banking & finance and CRM etc.
20%
Student Learning Outcomes:
•
•
•
•
Learn to apply various data mining techniques into various areas of different domains.
Examine the types of the data to be mined and present a general classification of tasks and primitives to integrate a data mining system.
Discover interesting patterns from large amounts of data to analyze and extract patterns to solve problems, make predictions of outcomes
Apply and analyze data mining for Business Intelligence Application.
Pedagogy for Course Delivery:
Subject will be taught on the basis of lectures, concepts learn in the classroom using various real life situations and discussing case study covering different module
with special reference to Classification, clustering, predictions and neural network techniques
Assessment/ Examination Scheme:
Theory L/T (%)
Lab/Practical/Studio (%)
100%
NA
Total
100%
Theory Assessment (L&T):
Continuous Assessment/Internal Assessment
Components (Drop
down)
Weightage (%)
Mid Term Exam
10%
Home Assignment
8%
End Term Examination
Presentation/ Viva
Attendance
7%
End Term Examination
5%
70%
Text Book:
1.
2.
3.
4.
Data Warehousing Fundamentals for IT Professionals, Paulraj Ponniah, Willey 2nd Edition.
Data Warehousing, Reema Thareja, Oxford University Press, 2009 Edition
Data Mining: Concepts and Techniques‖, J.Han, M.Kamber, Academic Press, Morgan Kanf man Publishers, 2001.
Business Intelligence: Practices, Technologies, and Management- Rajiv Sabherwal , Irma Becerra-Fernandez
Reference books:
1. Data Warehousing, Data Mining & OLAP, Alex Berson and Stephen J. Smith, Tata McGraw-Hill Edition, 2004.
2. Data Mining, Vikram Pudi and P. Radha Krishna, Oxford University Press, 2009 Edition
3. G. Shmueli, N.R. Patel, P.C. Bruce, “Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner”,
Wiley India