Semester-wise Syllabus for M.Com in Business Analytics

Semester 1: Foundations of Business & Analytics

1. Principles of Management

   • Management theories, organizational behavior, and leadership.

2. Business Statistics & Mathematics

   • Probability, descriptive & inferential statistics, and quantitative techniques.

3. Financial Accounting & Analysis

   • Financial statements, ratio analysis, and accounting standards.

4. Introduction to Business Analytics

   • Basics of analytics, types (descriptive, predictive, prescriptive), and applications.

5. Database Management Systems (DBMS)

   • SQL, data warehousing, and relational database concepts.

Semester 2: Advanced Analytics & Tools

1. Data Visualization & Reporting

   • Tools like Tableau, Power BI, and dashboard creation.

2. Predictive Analytics & Forecasting

   • Regression analysis, time series forecasting, and trend analysis.

3. Marketing Analytics

   • Customer segmentation, campaign analysis, and ROI measurement.

4. Python/R for Business Analytics

   • Basics of Python/R programming, data manipulation (Pandas, NumPy).

5. Business Research Methods

   • Data collection techniques, hypothesis testing, and research design.

Semester 3: Machine Learning & Big Data

1. Machine Learning for Business

   • Supervised & unsupervised learning (classification, clustering).

2. Big Data Analytics

   • Hadoop, Spark, and handling large datasets.

3. Financial Analytics

   • Risk modeling, portfolio analysis, and fraud detection.

4. Supply Chain & Operations Analytics

   • Inventory optimization, logistics analytics, and demand forecasting.

5. Optimization Techniques

   • Linear programming, decision-making models.

Semester 4: Advanced Applications & Capstone Project

1. Artificial Intelligence in Business

   • AI applications in finance, marketing, and HR.

2. Customer & Social Media Analytics

   • Sentiment analysis, churn prediction, and NLP techniques.

3. Ethics & Legal Aspects of Data Analytics

   • Data privacy (GDPR), ethical AI, and compliance.

4. Capstone Project / Industry Internship

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

5. Elective (Choose One)

   • Healthcare Analytics / HR Analytics / Retail Analytics

Key Tools & Technologies Covered

✔ Programming: Python, R, SQL
✔ Visualization: Tableau, Power BI, Excel (Advanced)
✔ Big Data: Hadoop, Spark
✔ Machine Learning: Scikit-learn, TensorFlow (Basics)
✔ Statistical Software: SPSS, SAS