Business Analytics - MB25202

MB25202:

Business Analytics

Course Objectives:

This course aims to introduce students to the foundational principles and applications of Business analytics for effective decision-making. It equips learners with the knowledge to identify appropriate tools for descriptive, predictive, and prescriptive analytics. This course also explores real-world applications and Business cases to enhance problem-solving and analytical thinking. The course emphasizes the strategic use of data, resource management, and technology integration for competitive advantage. Upon completion, students will be able to model and analyse Business problems using contemporary analytics techniques.

Foundations of Business Analytics

Business Analytics – Importance – Business Analytics Process – Relationship between Business Analytics and Organizational Decision Making – Business Analytics for Competitive Advantage – Strategic role of analytics.

Data and Resource Management in Analytics

Managing Analytics Infrastructure – Human Resources and Data Requirements – Aligning Organizational Structure with BA – Role of Technology – Information Policy – Data Governance – Data Quality Management – Change Management in BA.

Descriptive Analytics and Visualization

Descriptive Analytics – Visualizing and Exploring Data – Descriptive Statistics – Sampling Techniques – Estimation Methods – Probability Distributions used in Descriptive Analytics – Use of dashboards and visualization tools.

Predictive Analytics and Data Mining

Predictive Analytics – Predictive Models – Data-driven vs. Logic-driven Models – Predictive Modeling Procedures – Supervised vs. Unsupervised Learning – Reinforcement Learning – Data Mining Techniques – Association Analysis and Cluster Analysis – Model Validation – Application in Business cases.

Prescriptive Analytics and Optimization

Prescriptive Analytics – Prescriptive Modeling Techniques – Non-linear Optimization – Simulation – Scenario Planning – Demonstrating Business Performance Improvement – Role of decision support systems.

Applications and Emerging Trends in BA

Industry applications in Marketing, HR, Finance, and Operations – Real-time analytics – AI & ML integration – Cloud-based BA tools – Business Intelligence vs. BA – Ethics in Analytics – Case Studies on contemporary analytics usage.

References:

1. Marc J. Schniederjans, Dara G. Schniederjans, Christopher M. Starkey, Business Analytics: Principles, Concepts, and Applications, Pearson, 1st Ed., 2022

2. Christian S. Albright & Wayne L. Winston, Business Analytics: Data Analysis and Decision Making, Cengage, 6th Ed., 2019

3. James R. Evans, Business Analytics: Methods, Models and Decisions, Pearson, 3rd Ed., 2020

4. U Dinesh Kumar, Business Analytics: The Science of Data-Driven Decision Making, Wiley, 2nd Ed., 2021

5. N. D. Vohra, Quantitative Techniques in Management, McGraw Hill, 6th Ed., 2021

Course Outcomes

At the end of the course, learners will be able to: 

● CO1: Demonstrate conceptual knowledge of Business Analytics fundamentals, data and resource management, descriptive analytics, predictive modeling, prescriptive techniques, and their application in various industries with an ethical perspective.

● CO2: Interpret and relate the strategic role of Business Analytics, data governance, and technology infrastructure to descriptive statistics, predictive models, prescriptive methods, and emerging trends to inform managerial decisions.

● CO3: Apply descriptive, predictive, and prescriptive analytics techniques, including data visualization, statistical analysis, data mining, and optimization, to explore, forecast, and solve business problems using appropriate software and tools.

● CO4: Analyze complex business problems by integrating knowledge of data requirements, analytical methods (descriptive, predictive, and prescriptive), and model validation to derive meaningful insights for competitive advantage.

● CO5: Evaluate the effectiveness of different analytical models and techniques, including descriptive statistics, predictive algorithms, and prescriptive optimization methods, to recommend optimal business solutions and demonstrate performance improvements.

● CO6: Develop a data-driven business analytics strategy by synthesizing knowledge of foundational concepts, data management, analytical techniques, and emerging trends (AI/ML) to solve industry-specific problems and adhere to ethical standards.