Business Intelligence and Statistics

Paper Code: 
MCA 325A
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

Course Objectives

This course enables the students to

  1. Define the concepts of business intelligence and statistics.
  2. Understand the concepts of knowledge delivery in business intelligence.
  3. Demonstrate the concept of peer groups and cross efficiency analysis.
  4. Differentiate between business intelligence and business statistics.
  5. Evaluate using different distributions and testing techniques.
  6. Create new ideas where the knowledge of business intelligence and statistics can be implemented.

 

Course Outcomes(COs):

Learning outcomes

(at course level)

Learning and teaching strategies

Assessment

Strategies

CO148. Define the concepts of business intelligence and statistics.

 

CO149. Describe the concepts of knowledge delivery and interactive reports

 

CO150. Demonstrate the concept of pattern matching and cluster analysis

 

CO151. Differentiate using different distributions and testing techniques.

 

CO152. Evaluate using different testing techniques.

 

CO153. Construct new ideas where the knowledge of business intelligence and statistics can be implemented.

Approach in teaching:

Interactive Lectures,

Modeling, Discussions, implementing enquiry based learning.

 

Learning activities for the students:

Experiential Learning, Presentations, Case based learning, Discussions, Quizzes and Assignments

 

Assignments

Written test in classroom

Classroom activity

Continues Assessment

Semester End Examination

 

12.00
Unit I: 

Business intelligence  

Effective and timely decisions, Data, information and knowledge, Role of mathematical models, Business intelligence architectures: Cycle of a business intelligence analysis,  Enabling factors in business intelligence projects – Development of a business intelligence system, Ethics and business intelligence. Data Science Vs. business intelligence

12.00
Unit II: 

Knowledge delivery

The business intelligence user types, Standard reports, Interactive Analysis and Ad Hoc Querying, Parameterized Reports and Self-Service Reporting, dimensional analysis, Alerts/Notifications, Visualization: Charts, Graphs, Widgets, Scorecards and Dashboards, Geographic Visualization, Integrated Analytics, Considerations: Optimizing the Presentation for the Right Message.

12.00
Unit III: 

Efficiency       

Efficiency measures – The CCR model: Definition of target objectives- Peer groups – Identification of good operating practices; cross efficiency analysis – virtual inputs and outputs – Other models. Pattern matching – cluster analysis, outlier analysis, Business Intelligence Applications.

12.00
Unit IV: 

Distributions

Introduction to Population, Sample, Variable, Continuous Distribution, Discrete Distribution, Normal Distribution, Standard Deviation, Skewness, Mean, Mode, Median, Sampling Distribution, Central Limit Theorem, Z-Score.

12.00
Unit V: 

Hypothesis Testing

Steps for Hypothesis Testing, Statistical Significance, Hypothesis Testing Assumptions, Proportion Testing, Null Hypothesis, t-Distribution, t-tests, one-tailed and two-tailed t tests, Significance of p-values. Pareto Principle, Trends in analytics.

ESSENTIAL READINGS: 
  • Larissa T. Moss, S. Atre, “Business Intelligence Roadmap: The Complete Project Lifecycle of Decision Making”, Addison Wesley, 2003.
  • Carlo Vercellis, “Business Intelligence: Data Mining and Optimization for Decision Making”, Wiley Publications, 2009.
  • David Loshin Morgan, Kaufman, “Business Intelligence: The Savvy Manager‟s Guide”, Second Edition, 2012.
  • David Loshin, “Business Intelligence”, Second Edition, Morgan Kaufmann, 2012
REFERENCES: 
  • Cindi Howson, “Successful Business Intelligence: Secrets to Making BI a Killer App”, McGraw-Hill, 2007.
  • Ralph Kimball , Margy Ross , Warren Thornthwaite, Joy Mundy, Bob Becker, “The Data Warehouse Lifecycle Toolkit”, Wiley Publication Inc.,2007.
Academic Year: 
2020-21
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