Data Mining and Business Intelligence
Information Systems Area
PGP Term V, 2016-17
Instructor
Prof. Srikumar Krishnamoorthy
Wing 4D, Extension 4834
E-mail: srikumark@iimahd.ernet.in
Course Objectives
Business Intelligence offers set of tools, techniques and methodologies for gathering, storing,
analyzing, and presenting information to help decision makers at various levels in the
organization. Today, with significant advancements in databases, web 2.0 and other data
collection technologies, organizations are increasingly relying on BI and/or advanced analytic
techniques for making effective decisions.
This course introduces the participants to the essentials of BI and data mining technologies. It
will enable the participants to learn and apply analytical techniques for solving real-world
business problems. The course will also help participants to understand various issues,
challenges and best practices in implementing BI / analytical solutions in organizations.
Some of the key takeaways for the participants include: (1) Learn the fundamentals of BI,
Data warehousing and On-Line analytical processing, (2) Understand key concepts and
techniques in data mining / advanced analytics, and (3) Apply data mining techniques to
solve business problems in retail, finance and telecom domains.
Session Plan
Session Date Topic Case / Reading
1 09 Sep 2016 Introduction to the Course Case: Diamonds in the Data mine
and Business Intelligence (HBR)
Reading: Competing on Analytics
(HBR)
2-3 09 Sep 2016 Fundamentals of Data Case: Data Warehousing and Multi-
13 Sep 2016 Warehousing dimensional Data Modeling (IIMA)
Reading: Data Warehousing and OLAP
(Text Book, Chapter 4)
4 13 Sep 2016 OLAP Cubes and Hands-on: OLAP Cubes and
Reporting Dashboards
5 14 Sep 2016 Introduction to Data Reading: Getting to know your data
mining (Text Book, Chapters 2 and 3)
6-7 14 Sep 2016 Market Basket Analysis: Reading: Mining Frequent Patterns,
20 Sep 2016 Association Rule Mining Associations and Correlations: Concepts
and Methods (Text Book, Chapter 6)
8-9 20 Sep 2016 Association Rule and Case: Using Association Rules for
21 Sep 2016 Sequential Pattern Mining Product Assortment Decisions: A Case
Study
Reading: Mining Sequential Patterns
Hands-on: Rapid Miner
10-11 21 Sep 2016 Clustering and Outlier Case: Real-time Credit Card Fraud
27 Sep 2016 Analysis Detection using computational
intelligence
Reading: Cluster Analysis: Basic
Concepts and Methods (Text Book,
Chapter 10)
12-13 28 Sep 2016 Classification and Reading: Classification: Basic Concepts
Prediction (Text Book, Chapter 8)
14-15 04 Oct 2016 Building and Evaluating Case: Applying Data mining to Telecom
Classifier Models Churn Management
16-17 05 Oct 2016 Mining Data Streams Reading: Mining Stream, Time-series
and Sequence Data
18-19 18 Oct 2016 Fundamentals of Text Case: Opinion Observer: Analyzing and
Mining Comparing Opinions on the Web
20-21 19 Oct 2016 Sentiment Analysis Reading: The dynamics of online word
of mouth and product salesAn
empirical investigation of the movie
industry
Hands-on: Rapid Miner
22-23 25 Oct 2016 BI Implementation in Case: Managing with Analytics at
Organizations Procter & Gamble (HBR)
24-25 26 Oct 2016 Student Project Reading: Analytics 3.0 (HBR)
Presentations & Course
summary
Pedagogy
This course will have a mix of lectures, cases, and hands-on sessions.
Preparation
Each student needs to spend about 100 hours for class preparation (cases and readings),
quiz/assignment and group project.
Evaluation
The course grade will be based on the following weights:
Class Participation 20%
Quiz / Individual Assignments 40%
Group Project Report and Presentation (max 3 per group) 40%
Text Book
1. Jiawei Han, and Micheline Kamber, Data mining: Concepts and Techniques, Morgan
Kaufmann (Harcourt India Private Ltd), 3rd Edition, 2011
Further Readings
1. Efraim Turban, Ramesh Sharda, Dursun Delen, Decision Support and Business
Intelligence Systems, Pearson, 2011
2. D. Loshin, Business Intelligence: The Savvy Managers Guide, Morgan Kaufmann,
2003
3. E. Siegel, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or
Die, Wiley 2013
4. M.J.A. Berry, and G. Linoff, Data Mining Techniques: For Marketing, Sales and
Customer Support, Wiley, 1997
5. David J. Hand, Heikki Mannila, Padhraic Smyth, Principles of Data Mining, Smyth
Publisher: The MIT Press, 2001
