Semester-wise Syllabus for an MBA in Business Analytics/Data Analytics

Semester 1: Foundation in Business & Analytics

1. Principles of Management

   • Basics of management, organizational behavior, and leadership.

2. Managerial Economics

   • Demand forecasting, cost analysis, and decision-making under uncertainty.

3. Financial Accounting

   • Financial statements, ratio analysis, and accounting for decision-making.

4. Statistics for Business Analytics

   • Descriptive/inferential stats, probability, hypothesis testing, and regression.

5. Introduction to Business Analytics

   • Overview of analytics lifecycle, tools (Excel, Tableau), and applications.

6. IT for Business

   • Databases (SQL), data warehousing, and basics of programming (Python/R).

Semester 2: Core Analytics & Business Functions

1. Predictive Analytics

   • Regression models, time-series forecasting, and machine learning basics.

2. Marketing Analytics

   • Customer segmentation, CLV, campaign ROI, and digital marketing metrics.

3. Operations & Supply Chain Analytics

   • Inventory optimization, logistics modeling, and simulation.

4. Financial Analytics

   • Risk modeling, portfolio optimization, and credit scoring.

5. Data Visualization & Storytelling

   • Tools: Power BI, Tableau; best practices in dashboard design.

6. Big Data Technologies

   • Hadoop, Spark, and NoSQL databases (MongoDB).

Semester 3: Advanced Analytics & Electives

1. Machine Learning for Business

   • Supervised/unsupervised learning (decision trees, clustering, NLP).

2. Prescriptive Analytics

   • Optimization techniques, Monte Carlo simulations.

3. HR & People Analytics

   • Talent analytics, attrition prediction, and workforce planning.

4. AI in Business

   • Deep learning, chatbots, and AI-driven decision systems.

5. Elective 1 (Choose one):

   • Retail Analytics | Healthcare Analytics | Fraud Analytics

6. Elective 2 (Choose one):

   • Social Media Analytics | Risk Analytics | Supply Chain AI

Semester 4: Capstone & Specialization

1. Business Strategy with Analytics

   • Aligning analytics with organizational goals (case studies).

2. Ethics & Data Privacy

   • GDPR, ethical AI, and responsible data usage.

3. Elective 3 (Domain-specific):

   • FinTech Analytics | Marketing Mix Modeling | IoT Analytics

4. Capstone Project

   • Real-world analytics project (e.g., predictive model for a client).

5. Internship (Optional)

   • Industry immersion in analytics roles.

Tools & Technologies Covered

• Programming: Python, R, SQL

• Visualization: Tableau, Power BI, ggplot

• Big Data: Hadoop, Spark, AWS/GCP

• ML/AI: Scikit-learn, TensorFlow, NLP libraries

Elective Specializations

1. Marketing Analytics: Customer journey mapping, churn prediction.

2. Financial Analytics: Algorithmic trading, blockchain analytics.

3. Operations Analytics: Predictive maintenance, route optimization.