BA5021

Datamining for Business Intelligence

HBS > Professional Electives > Datamining for Business Intelligence
Course ID
BA5021
Level
Post Graduation
Semester
Semester III

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
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
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
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 OPPORTUNITIES
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
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, 3 rd 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, Data mining techniques for Marketing, Sales and Customer support, John Wiley, 3 rd 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, Ïntroduction 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.