BA5021 Syllabus - Datamining For Business Intelligence - 2017 Regulation Anna University

BA5021 Syllabus - Datamining For Business Intelligence - 2017 Regulation Anna University

BA5021

DATAMINING FOR BUSINESS INTELLIGENCE

 L T P C

3 0 0 3

OBJECTIVES:
• To know how to derive meaning form huge volume of data and information
• To understand how knowledge discovering process is used in business decision making

UNIT I

INTRODUCTION

9

Data mining, Text mining, Web mining, Spatial mining, Process mining, BI process- Private and Public intelligence, Strategic assessment of implementing BI

UNIT II

DATA WAREHOUSING

9

Data ware house – characteristics and view - OLTP and OLAP - Design and development of data warehouse, Meta data models, Extract/ Transform / Load (ETL) design

UNIT III

DATA MINING TOOLS, METHODS AND TECHNIQUES

9

Regression and correlation; Classification- Decision trees; clustering –Neural networks; Market basket analysis- Association rules-Genetic algorithms and link analysis, Support Vector Machine, Ant Colony Optimization

UNIT IV

MODERN INFORMATION TECHNOLOGY AND ITS BUSINESS OPPORTUNITIE

9

Business intelligence software, BI on web, Ethical and legal limits, Industrial espionage, modern techniques of crypto analysis, managing and organizing for an effective BI Team.

UNIT V

BI AND DATA MINING APPLICATIONS

9

Applications in various sectors – Retailing, CRM, Banking, Stock Pricing, Production, Crime, Genetics, Medical, Pharmaceutical.

TOTAL: 45 PERIODS

OUTCOMES:
• Big Data Management
• Appreciate the techniques of knowledge discovery for business applications

REFERENCES:
1. Jaiwei Ham and Micheline Kamber, Data Mining concepts and techniques, Kauffmann Publishers 3 rd edition, 2011
2. Efraim Turban, Ramesh Sharda, Jay E. Aronson and David King, Business Intelligence, 3rd edition,Prentice Hall, 2014.
3. W.H.Inmon, Building the Data Warehouse, fourth edition Wiley India pvt. Ltd. 2005.
4. Ralph Kimball and Richard Merz, The data warehouse toolkit, John Wiley, 2005.
5. Michel Berry and Gordon Linoff, Mastering Data mining, John Wiley and Sons Inc, 3nd Edition, 2011
6. Michel Berry and Gordon Linoff, Data mining techniques for Marketing, Sales and Customer support, John Wiley, 3 rd edition 2011
7. G. K. Gupta, √Źntroduction to Data mining with Case Studies, Prentice hall of India, 2014.
8. Giudici, Applied Data mining – Statistical Methods for Business and Industry, John Wiley. 2009
9. Elizabeth Vitt, Michael Luckevich Stacia Misner, Business Intelligence, Microsoft, 2011
10. Michalewicz Z., Schmidt M. Michalewicz M and Chiriac C, Adaptive Business Intelligence, Springer – Verlag, edition 2016
11. Galit Shmueli, Nitin R. Patel and Peter C. Bruce, Data Mining for Business Intelligence – Concepts, Techniques and Applications Wiley, India ,3rd edition, 2016

Comments

Popular posts from this blog

CS3491 Syllabus - Artificial Intelligence And Machine Learning - 2021 Regulation Anna University

BE3251 - Basic Electrical and Electronics Engineering (Syllabus) 2021-regulation Anna University

CS3401 Syllabus - Algorithms - 2021 Regulation Anna University