Semester-wise Syllabus for MSc Business Analytics

Semester 1: Foundations of Analytics

1. Business Statistics & Probability

   • Descriptive/inferential statistics

   • Probability distributions, hypothesis testing

   • Bayesian thinking in business decisions

2. Data Management & SQL

   • Database design (ER diagrams)

   • Advanced SQL (window functions, CTEs)

   • NoSQL basics (MongoDB for unstructured data)

3. Programming for Analytics (Python/R)

   • Python libraries (Pandas, NumPy)

   • Data wrangling with R (dplyr, tidyr)

   • APIs and web scraping

4. Business Intelligence & Visualization

   • Tableau/Power BI dashboarding

   • Storytelling with data (design principles)

5. Managerial Economics

   • Demand forecasting, pricing models

   • Game theory applications

Semester 2: Core Analytics & Machine Learning

1. Predictive Analytics

   • Regression (linear, logistic, polynomial)

   • Time series forecasting (ARIMA, Prophet)

2. Machine Learning for Business

   • Supervised learning (Decision Trees, SVM)

   • Unsupervised learning (k-means, PCA)

   • Model evaluation (ROC, precision-recall)

3. Optimization & Decision Models

   • Linear/non-linear programming

   • Monte Carlo simulations

4. Marketing Analytics

   • Customer segmentation (RFM analysis)

   • Campaign ROI measurement

   • Digital marketing metrics (CTR, CAC)

5. Big Data Technologies

   • Hadoop/Spark ecosystem

   • Cloud analytics (AWS Redshift, Google BigQuery)

Semester 3: Advanced Applications & Electives

1. Deep Learning for Business

   • Neural networks for demand prediction

   • Computer vision (product recognition)

2. Risk & Financial Analytics

   • Credit scoring models

   • Fraud detection (anomaly detection)

   • Portfolio optimization

3. Electives (Choose 2)

   • Supply Chain Analytics (Inventory optimization)

   • HR Analytics (Attrition prediction)

   • Healthcare Analytics (Patient readmission)

   • Social Media Analytics (Sentiment analysis)

4. Business Strategy with Analytics

   • Competitive intelligence

   • A/B testing frameworks

5. Industry Internship (6-8 weeks)

   • Live project with corporate partners

Semester 4: Capstone & Emerging Trends

1. Prescriptive Analytics

   • Recommendation engines

   • Real-time decision systems

2. AI Ethics & Governance

   • Bias mitigation in models

   • GDPR/CCPA compliance

3. Capstone Project

   • End-to-end business problem (e.g., churn reduction)

   • Client presentation with executable solution

4. Blockchain for Business

   • Smart contract analytics

   • Cryptocurrency trend analysis