Semester-wise syllabus for a BBA in Business Analytics

 

Semester 1:

Foundations of Business & Analytics

1. Principles of Management 

   - Basics of management, organizational structures, and decision-making. 

2. Financial Accounting 

   - Fundamentals of accounting, financial statements, and ledgers. 

3. Business Mathematics & Statistics 

   - Algebra, probability, descriptive statistics, and data interpretation. 

4. Introduction to Business Analytics 

   - Overview of analytics in business, types of analytics (descriptive, predictive, prescriptive). 

5. IT Fundamentals 

   - Basics of Excel, data entry, and introduction to analytics tools (e.g., Tableau). 

6. Microeconomics 

   - Supply-demand, market equilibrium, and business decision frameworks. 

 

Semester 2:

 Core Business & Data Skills

1. Macroeconomics 

   - GDP, inflation, fiscal policy, and economic indicators. 

2. Marketing Management

   - Consumer behavior, segmentation, and analytics in marketing. 

3. Database Management Systems (DBMS) 

   - SQL, data modeling, and relational databases. 

4. Business Communication 

   - Data storytelling, visualization basics, and report writing. 

5. Programming Fundamentals 

   - Introduction to Python/R for data manipulation (Pandas, NumPy). 

6. Operations Research 

   - Linear programming, optimization, and decision analysis. 

 

Semester 3:

Intermediate Analytics & Tools

1. Predictive Analytics 

   - Regression analysis, time-series forecasting, and model evaluation. 

2. Data Visualization 

   - Tools like Tableau, Power BI, and design principles for dashboards. 

3. Financial Management 

   - Capital budgeting, financial ratios, and analytics in finance. 

4. Business Law & Ethics 

   - Legal frameworks, data privacy (GDPR, HIPAA), and ethical AI. 

5. Machine Learning Basics 

   - Supervised vs. unsupervised learning, clustering, and classification (using Python/R). 

6. Supply Chain Analytics 

   - Inventory optimization, logistics analytics, and demand forecasting. 

 

Semester 4:

Advanced Analytics & Applications

1. Big Data Analytics 

   - Hardtop, Spark, and handling unstructured data. 

2. Marketing Analytics 

   - Customer segmentation, campaign analysis, and ROI metrics. 

3. Prescriptive Analytics 

   - Decision optimization, simulation, and scenario modeling. 

4. HR Analytics 

   - Talent analytics, employee retention, and workforce planning. 

5. Web & Social Media Analytics

   - Sentiment analysis, SEO, and social network analysis. 

6. Elective 1 (e.g., Retail Analytics or Healthcare Analytics). 

 

Semester 5:

Specializations & Industry Integration

1. Advanced Machine Learning

   - Neural networks, NLP, and deep learning basics. 

2. Business Intelligence (BI) Systems 

   - ERP integration, SAP Analytics, and real-time dashboards. 

3. Risk Management & Analytics

   - Financial risk modeling, Monte Carlo simulations. 

4. Elective 2 (e.g., Financial Analytics or Sports Analytics). 

5. Elective 3 (e.g., AI in Business or Block chain for Analytics). 

6. Internship/Industry Project 

   - Hands-on experience in analytics roles (e.g., data analyst, BI intern). 

 

Semester 6:

Capstone & Professional Development

1. Capstone Project

   - End-to-end analytics project solving real business problems (e.g., churn prediction, market basket analysis). 

2. Data Governance & Quality 

   - Master data management, data cleaning, and governance frameworks. 

3. Strategic Management

   - Analytics-driven strategy formulation and case studies. 

4. Elective 4 (e.g., IoT Analytics or Fraud Detection). 

5. Career Readiness

   - Resume building, analytics certifications (e.g., Google Analytics, Power BI), and interview prep. 

6. Dissertation/Research Paper 

   - Independent research on emerging trends (e.g., AI ethics, predictive policing).