Comprehensive Course Structure
The Business Analytics program is structured over eight semesters, with a balanced mix of core subjects, departmental electives, science electives, and laboratory sessions designed to build both theoretical knowledge and practical skills.
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MATH101 | Calculus and Analytical Geometry | 3-0-0-3 | - |
| 1 | MATH102 | Linear Algebra and Matrices | 3-0-0-3 | - |
| 1 | CS101 | Introduction to Programming | 2-0-2-3 | - |
| 1 | ECON101 | Principles of Economics | 3-0-0-3 | - |
| 1 | STAT101 | Probability and Statistics I | 3-0-0-3 | - |
| 2 | MATH201 | Differential Equations | 3-0-0-3 | MATH101 |
| 2 | CS201 | Data Structures and Algorithms | 2-0-2-3 | CS101 |
| 2 | ECON201 | Microeconomics | 3-0-0-3 | ECON101 |
| 2 | STAT201 | Probability and Statistics II | 3-0-0-3 | STAT101 |
| 2 | CS202 | Database Systems | 2-0-2-3 | CS101 |
| 3 | MATH301 | Mathematical Modeling | 3-0-0-3 | MATH201 |
| 3 | CS301 | Applied Statistics | 2-0-2-3 | STAT201 |
| 3 | CS302 | Business Intelligence Tools | 2-0-2-3 | CS202 |
| 3 | ECON301 | Macroeconomics | 3-0-0-3 | ECON201 |
| 3 | STAT301 | Linear Programming and Optimization | 3-0-0-3 | MATH201 |
| 4 | CS401 | Machine Learning Fundamentals | 2-0-2-3 | CS301 |
| 4 | CS402 | Data Mining Techniques | 2-0-2-3 | STAT301 |
| 4 | ECON401 | Industrial Organization | 3-0-0-3 | ECON301 |
| 4 | STAT401 | Time Series Analysis | 3-0-0-3 | STAT201 |
| 5 | CS501 | Big Data Technologies | 2-0-2-3 | CS401 |
| 5 | CS502 | Predictive Analytics | 2-0-2-3 | CS402 |
| 5 | ECON501 | Econometrics | 3-0-0-3 | ECON401 |
| 5 | STAT501 | Advanced Statistical Methods | 3-0-0-3 | STAT401 |
| 6 | CS601 | Deep Learning | 2-0-2-3 | CS501 |
| 6 | CS602 | Natural Language Processing | 2-0-2-3 | CS502 |
| 6 | ECON601 | Financial Markets and Institutions | 3-0-0-3 | ECON501 |
| 6 | STAT601 | Bayesian Inference | 3-0-0-3 | STAT501 |
| 7 | CS701 | Capstone Project I | 2-0-4-4 | CS601 |
| 7 | CS702 | Research Methodology | 2-0-2-3 | - |
| 8 | CS801 | Capstone Project II | 2-0-4-4 | CS701 |
| 8 | CS802 | Internship | 0-0-0-6 | - |
Detailed Departmental Elective Courses
Departmental electives provide students with opportunities to specialize in advanced topics aligned with their interests and career goals. Here are some of the key courses offered:
- Machine Learning Applications: This course focuses on applying machine learning algorithms to solve real-world problems in various domains such as healthcare, finance, and marketing.
- Financial Risk Analytics: Students explore techniques for assessing and managing financial risks using statistical models and quantitative methods.
- Supply Chain Optimization: This course examines how analytics can be used to improve efficiency and reduce costs in logistics and distribution networks.
- Consumer Behavior Analysis: Using data science tools, students analyze consumer preferences and behaviors to inform marketing strategies.
- Healthcare Data Analytics: This course covers the application of analytical methods in improving patient outcomes and operational performance in healthcare settings.
- E-Commerce Data Mining: Students learn how to extract valuable insights from e-commerce transactions and user behavior data.
- Marketing Attribution Modeling: This elective teaches students how to measure the effectiveness of marketing channels and optimize budget allocation.
- Behavioral Economics and Analytics: Combines principles of behavioral economics with analytical frameworks to understand decision-making processes in organizations.
- Social Media Analytics: Students analyze social media platforms to derive insights about brand perception, sentiment analysis, and user engagement metrics.
- Geospatial Data Analysis: This course introduces students to spatial data processing and visualization techniques used in urban planning, transportation, and environmental studies.
Project-Based Learning Philosophy
Our approach to project-based learning is centered on fostering innovation, collaboration, and practical problem-solving skills among students. The program incorporates mandatory mini-projects throughout the curriculum, culminating in a comprehensive final-year thesis or capstone project.
The mini-project component begins in the second year and continues through the third year. These projects allow students to apply theoretical concepts learned in class to real-world scenarios under the guidance of faculty mentors. Students work individually or in small teams to complete these projects, which are evaluated based on technical depth, creativity, presentation quality, and impact.
The final-year capstone project is a significant undertaking that spans the entire academic year. Students choose topics aligned with their career aspirations and select faculty advisors who possess expertise in relevant areas. The project involves extensive literature review, data collection, modeling, implementation, and documentation.
Evaluation criteria for projects include:
- Technical soundness of methodology
- Originality and innovation in approach
- Clarity and professionalism of presentation
- Impact and relevance to industry or society
- Effective use of available resources
The selection process for projects involves a proposal phase where students present their ideas to faculty members. Advisors are matched based on subject expertise, availability, and alignment with student interests. Regular progress updates and milestone reviews ensure that projects remain on track toward successful completion.