Course Objectives
This course enables the students to
Course Outcomes(COs):
Learning Outcome (at course level)
| Learning and teaching strategies | Assessment Strategies |
| 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
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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
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.
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.
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.
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.
·Ramesh Sharda, Dursun Delen and Efraim Turban, “Business Intelligence, Analytics, and Data Science: A Managerial Perspective”, 4th Edition, Pearson, 2019
Suggested Readings:
E-Resources
Journals