*1. Core MBA Courses* 

(Foundational courses with a focus on analytics integration) 

 

*Course Code* | *Course Title* | *Credits* | *Objectives* | *Key Topics* 

---|---|---|---|--- 

MBA 501 | Financial Management | 3 | Use analytics for financial decisions | Financial modelling, risk analytics, ROI optimization 

MBA 502 | Managerial Economics | 3 | Apply econometrics to business strategy | Demand forecasting, pricing analytics, game theory 

MBA 503 | Operations Management | 3 | Optimize processes with data tools | Supply chain analytics, process mining, Six Sigma 

MBA 504 | Organizational Behaviour | 3 | Leverage data for HR strategies | Workforce analytics, employee engagement metrics 

MBA 505 | Strategic Management | 3 | Drive strategy with data insights | Competitive analytics, scenario planning, KPI dashboards 

MBA 506 | Business Ethics & CSR | 3 | Address ethical challenges in AI/analytics | Data privacy, algorithmic bias, AI governance 

MBA 507 | Advanced Business Analytics | 3 | Master analytics frameworks | CRISP-DM, data lifecycle, predictive vs. prescriptive analytics 

MBA 508 | Leadership in Data-Driven Organizations | 3 | Lead analytics teams and projects | Change management, data storytelling, stakeholder buy-in 

 

*2. Business Analytics Specialization Courses* 

(Advanced technical and applied analytics modules) 

 

*Course Code* | *Course Title* | *Credits* | *Objectives* | *Key Topics* 

---|---|---|---|--- 

BA 601 | Data Mining & Predictive Modelling | 3 | Extract insights from large datasets | Clustering, classification, regression (Python/R) 

BA 602 | Big Data Technologies | 3 | Manage and analyse big data | Hadoop, Spark, NoSQL databases, cloud platforms (AWS/Azure) 

BA 603 | Machine Learning for Business | 3 | Implement ML solutions in business contexts | Supervised/unsupervised learning, NLP, recommendation systems 

BA 604 | Business Intelligence & Visualization | 3 | Transform data into actionable reports | Tableau, Power BI, Qlik, dashboard design 

BA 605 | Marketing Analytics | 3 | Optimize marketing strategies with data | Customer segmentation, campaign ROI, attribution modelling 

BA 606 | Supply Chain & Logistics Analytics | 3 | Enhance supply chain efficiency | Inventory optimization, route planning, demand forecasting 

BA 607 | AI for Business Decision-Making | 3 | Apply AI tools to solve business problems | Chatbots, robotic process automation (RPA), AI ethics 

BA 608 | Risk & Fraud Analytics | 3 | Mitigate risks using predictive models | Credit risk modelling, anomaly detection, fraud prevention 

 

*3. Elective Courses* 

(Choose 4–5 based on career interests) 

 

- *BA 701*: Advanced Python/R for Analytics 

- *BA 702*: Customer Analytics & CRM 

- *BA 703*: Healthcare Analytics 

- *BA 704*: Financial Analytics & FinTech 

- *BA 705*: Social Media & Sentiment Analysis 

- *BA 706*: Time Series Forecasting 

- *BA 707*: Ethical AI & Responsible Analytics 

- *BA 708*: IoT and Real-Time Analytics 

 

*4. Capstone Project/Thesis* 

- *Credits*: 6 

- *Objective*: Solve a real-world business problem using analytics (e.g., churn prediction, pricing optimization). 

- *Deliverables*: Data collection, model building, actionable insights, implementation strategy. 

 

*5. Internship (Optional)* 

- *Duration*: 8–12 weeks 

- *Objective*: Gain hands-on experience in analytics roles (e.g., data scientist, business analyst) at tech firms, consultancies, or enterprises. 

 

*6. Tools & Technologies Covered* 

- *Programming*: Python, R, SQL 

- *Visualization*: Tableau, Power BI, D3.js 

- *Big Data*: Hadoop, Spark, AWS 

- *ML/AI*: TensorFlow, scikit-learn, Azure ML 

- *Database*: MySQL, MongoDB, Snowflake 

 

*7. Assessment Methods* 

- *Analytics Projects* (35%) 

- *Exams* (25%) 

- *Case Competitions* (20%) 

- *Presentations* (15%) 

- *Class Participation* (5%) 

 

*8. Recommended Textbooks* 

- *Data Science for Business* by Foster Provost & Tom Fawcett 

- *Python for Data Analysis* by Wes McKinney 

- *Machine Learning Yearning* by Andrew Ng 

- *Big Data: A Revolution* by Viktor Mayer-Schonberger 

 

*Learning Outcomes*: 

Graduates will be able to: 

- Translate business problems into analytics frameworks. 

- Build predictive models using Python/R and ML libraries. 

- Communicate insights visually to non-technical stakeholders. 

- Lead analytics-driven innovation in sectors like finance, retail, or healthcare.