Comprehensive Course Structure Across 8 Semesters
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MATH101 | Calculus I | 3-0-0-3 | - |
| 1 | MATH102 | Linear Algebra | 3-0-0-3 | - |
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | BUS101 | Business Fundamentals | 3-0-0-3 | - |
| 2 | MATH201 | Probability and Statistics | 3-0-0-3 | MATH101, MATH102 |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | DBMS101 | Database Management Systems | 3-0-0-3 | CS101 |
| 2 | BUS201 | Managerial Economics | 3-0-0-3 | - |
| 3 | STAT301 | Statistical Inference | 3-0-0-3 | MATH201 |
| 3 | ML301 | Introduction to Machine Learning | 3-0-0-3 | CS201, MATH201 |
| 3 | BIA301 | Business Intelligence Fundamentals | 3-0-0-3 | DBMS101 |
| 3 | CS301 | Web Technologies | 3-0-0-3 | CS101 |
| 4 | TIME401 | Time Series Analysis | 3-0-0-3 | MATH201 |
| 4 | DEEP401 | Deep Learning | 3-0-0-3 | ML301 |
| 4 | ADV401 | Advanced Statistical Modeling | 3-0-0-3 | STAT301 |
| 4 | BIA401 | Data Visualization | 3-0-0-3 | BIA301 |
| 5 | PRED501 | Predictive Analytics | 3-0-0-3 | ML301, TIME401 |
| 5 | BUS501 | Strategic Decision Making | 3-0-0-3 | BUS201 |
| 5 | OPT501 | Optimization Techniques | 3-0-0-3 | MATH201 |
| 5 | CS501 | Cloud Computing | 3-0-0-3 | CS201 |
| 6 | CAP601 | Capstone Project | 4-0-0-4 | All previous courses |
| 6 | BUS601 | Industry Internship | 2-0-0-2 | All previous courses |
| 7 | ADV701 | Advanced Topics in Analytics | 3-0-0-3 | PRED501 |
| 7 | BIA701 | Enterprise Analytics Platforms | 3-0-0-3 | BIA401 |
| 7 | CS701 | Blockchain and Cryptocurrency | 3-0-0-3 | CS201 |
| 8 | MINI801 | Mini Project | 4-0-0-4 | All previous courses |
| 8 | THESIS801 | Final Year Thesis | 6-0-0-6 | All previous courses |
Detailed Departmental Elective Courses
The department offers a range of advanced electives tailored to specific areas within business analytics:
- Advanced Statistical Modeling: This course explores complex statistical methods used in modern data science, including Bayesian inference, mixed-effects models, and non-parametric techniques.
- Machine Learning for Business Applications: Students learn how to implement ML algorithms in real-world business contexts such as recommendation systems, fraud detection, and customer segmentation.
- Data Visualization & Communication: This course focuses on effective visualization techniques using tools like Tableau, Power BI, and D3.js to communicate findings clearly to stakeholders.
- Text Mining and NLP: Students study natural language processing techniques for extracting insights from unstructured text data in social media, news articles, and customer reviews.
- Geospatial Data Analysis: This course introduces students to geographic information systems (GIS) and spatial statistics for analyzing location-based data in urban planning, logistics, and marketing.
- Financial Time Series Forecasting: Students learn advanced forecasting methods for financial markets using ARIMA, GARCH, and state-space models.
- Big Data Analytics with Hadoop & Spark: This course covers distributed computing frameworks for processing large-scale datasets efficiently.
- Marketing Analytics: Students explore how to use data to understand consumer behavior, optimize marketing campaigns, and measure ROI.
- Healthcare Informatics: This course applies analytics techniques to improve patient outcomes through predictive modeling and electronic health records analysis.
- Ethics in Data Science: A critical examination of ethical considerations in data collection, analysis, and decision-making processes within business environments.
Project-Based Learning Philosophy
The department's philosophy on project-based learning emphasizes the development of practical skills through hands-on experience. Students engage in both mini-projects and capstone projects that mirror real-world challenges faced by industry partners.
Mini-projects are conducted during the second and third years, focusing on specific analytical problems within chosen specializations. These projects are supervised by faculty members who guide students through the entire process from problem definition to solution implementation.
The final-year thesis or capstone project is a comprehensive endeavor that integrates all knowledge gained throughout the program. Students select their topic in consultation with faculty mentors, often collaborating with external organizations or government agencies to address actual business needs.
Evaluation criteria for these projects include technical depth, creativity, clarity of communication, impact on stakeholders, and adherence to ethical standards. The project components are assessed by both internal faculty panels and industry experts, ensuring relevance and rigor.