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BUSINESS ANALYTICS
Sub Code: 010020
Course Credits: 03
Course Snapshot
Large volumes of data have been collected by organizations using enterprise applications like
ERP, SCM and CRM. Most of the data is being analyzed for operational purposes. Very few are
using the information for Strategic Decision Making. Business Intelligence (or BI) objective is to
derive information from large volume of data and apply this for strategic decision making to gain
a competitive edge. The course will expose the students to tools and techniques used in BI
applications. It will also help the students to appear for SAS Enterprise Certification Program
conducted globally.
Objectives
Upon completion of the course it is expected that students will be able to understand:
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Relevance of Business Intelligence in Industry
Role of Business Intelligence in decision making
Technology to implement Business Intelligence
Importance of Data Warehouse to implement Business Intelligence
Data Mining and Data Visualization
Future Directions
Learning Goals
a. General Learning Goals
Effective Communication
Interpersonal Skills & Teamwork
Social Responsibility
 Problem Solving & Decision Making
 Quantitative Analytical Skills
Global Awareness
(L 1)
(L 2)
(L 3)
(L 4)
(L 5)
(L 6)
b. Specific Learning Goals
Knowledge:
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Understand the process for linking data to critical business outcomes
(K1)
Deployment of Business analytics to arrive at effective and efficient decisions. (K2)
To recognize strengths and identify any needs for improvement in various business functions.
(K3)
Skills:
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How to calculate ROI on various investments
Data Visualization
(S1)
(S2)
Attitude:
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Explorative Mind set
Objectivity
Holistic view
(A1)
(A2)
(A3)
Course Delivery
Classroom Engagement (CE)- contact hours in the class
30 Hours
(lectures, case discussions, presentations, exercises, internal assessment)
Directed Learning (DL)- done under faculty supervision
30 Hours
(lab-work, research, fieldwork, project work)
Total No. of Hours (CE+DL) = 60
Evaluation Components
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Internal Assessment
60%
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Cases: Term Oral and Written Presentation Case
Discussions
Participation: Including responses to
cases/topics
Project
Mid-Term Exam- 2 hours
External Assessment
40%

End-Term Exam- 3 hours
15%
10%
15%
20%
40%
Study Material
Text Book:
 Data Warehousing Fundamentals – Paulraj Ponniah − Reference Book
 Data Mining Techniques – A Berry and Gordon Linoff
Reference Book:
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Cerrito, P. B. 2006. Introduction to Data Mining Using SAS Enterprise Miner, Cary, NC:
SAS Institute Inc.
de Ville Barry, 2006. Decision Trees for Business Intelligence and Data Mining: Using SAS
Enterprise Miner. Cary, NC: SAS Institute Inc.
Biggs, D. B. de Ville, and E. Suen. 1991. “A Method of Choosing Multiway Partitions for
Classification and Decision Trees.” Journal of Applied Statistics 18, no. 1 : 49-62.
Breiman, L. 1996. “Bagging Predictors,” Machine Learning 24, No. 2 : 123-140.
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Breiman, L. 2001. “Random Forests,” Machine Learning 45, No. 1 : 5-32
Breiman, L. 2002. “WALD Lectures.” Paper presented at the sixty-fifth annual meeting of
the Institute of Mathamatical Statistics, Banff, Alberta, Canada. Available at
http://www.stat.barkeley.edu/users/breiman/.
Breiman, L., et al. 1984. Classification and Regression Trees, Belmont, CA: Wadsworth.
Michie, D., C.C. Spiegelhalter, and C. C. Taylor, eds. 1994. Machine Learning, Neural and
Statistical Classification, New York, NY: Ellis Horwood.
Pyle, Dorian, 1999, “Data Preparation for Data Mining. San Francisco, CA: Morgan
Kaufmann Publishers Inc.
Quinlan, J. R. 1993. C4.5: Programs for Machine Learning, San Francisco, CA: Morgan
Kaufmann Publishers Inc.
Data Warehousing Institute Website
Course Material on LMS
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Course Outline
Articles, Cases and Reference material
Session Plan-Classroom Engagement (CE) - each session is for 90 min
Session Topic
No
1-2
3-4
4-5
5-11
Introduction to
Business Intelligence
(BI), Components of
Business Intelligence
Overview and
Concepts of Data
Warehouse
Planning a Data
warehouse
Defining Business
Requirements, Role
of Metadata
Star Schema and
Multi Dimension
Database, OLAP
Lab Sessions on
Building a Data
warehouse
Teaching
Resource
Pedagogy
Assessment
Text Book
PPT and
classroom
discussion
Group Discussion,
Presentation
Text Book
& reference
material
Case
presentation,
analysis &
discussion
Group Case
Discussion
Presentation & Group
CaseAnalysis
Presentations
Text Book
& reference
material
PPT &
discussion
Group Discussion
Text Book
& reference
material
Lab
Sessions
K-S-A
K1-K3
S1-S2
A1-A3
Learning
Goals
L4,L5
K1-K3
S1-S2
L4,L5
A1-A3
K1-K3
S1-S2
A1-A3
Lab Practical and hand K1-K3
on experience on S/W S1-S2
A1-A3
L4,L5
L4,L5
12
Introduction to Data
Mining
Text Book
& reference
material
13
Data Mining
Methodology
Text Book
& reference
material
14
Data Transformation,
identifying variable
of importance, Data
Sampling, Data
Exploration
Text Book
& reference
material
15
Decision Trees
Logistic Regression
Disc. Analysis
Text Book
& reference
material
16-20
Lab Sessions using
SPSS
Text Book
& reference
material
Case
presentation,
analysis &
discussion
Group Case
Discussion
Presentation & Group
Case Analysis
Presentations
K1-K3
S1-S2
A1-A3
L4,L5
Case
Group Case
presentation,
Discussion
analysis &
Presentation
discussion
K1-K3
S1-S2
A1-A3
L4,L5
Case
Group Case
presentation,
Discussion
analysis &
Presentation
discussion
K1-K3
S1-S2
A1-A3
L4,L5
Case
presentation, Group Discussion,
analysis &
Lab practical
discussion
K1-K3
S1-S2
A1-A3
L4,L5
Lab
Sessions
K1-K3
S1-S2
A1-A3
L4,L5
Lab Practical and
problem solving
Total Hours: 30
Work Plan- Directed Learning (DL)
Sl. No.
Description of Component
(Lab-work, research, fieldwork, project work, etc.)
No. of Hours
required
20
2
Output/Deliverable
from students
Presentation
Presentation
1
2
Case
Business Analytics lab
3
Assignment-1
2
Assignment
4
5
Assignment-2
Project
2
4
Total Hours- 30
Assignment
Short report