Comprehensive Course Listing
This detailed course structure spans eight semesters, outlining core subjects, departmental electives, science electives, and laboratory components. The curriculum is designed to build a strong foundation in analytical thinking, statistical modeling, and business applications.
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | BAN-101 | Introduction to Business Analytics | 3-0-0-3 | - |
| 1 | BAN-102 | Mathematics for Analytics I | 3-0-0-3 | - |
| 1 | BAN-103 | Programming Fundamentals | 2-0-2-4 | - |
| 1 | BAN-104 | Statistics for Data Analysis | 3-0-0-3 | - |
| 1 | BAN-105 | Business Communication Skills | 2-0-0-2 | - |
| 1 | BAN-106 | Database Systems | 3-0-0-3 | - |
| 2 | BAN-201 | Probability and Distributions | 3-0-0-3 | BAN-102 |
| 2 | BAN-202 | Data Structures and Algorithms | 3-0-0-3 | BAN-103 |
| 2 | BAN-203 | Linear Algebra for Analytics | 3-0-0-3 | BAN-102 |
| 2 | BAN-204 | Business Intelligence Concepts | 3-0-0-3 | BAN-106 |
| 2 | BAN-205 | Research Methodology | 2-0-0-2 | - |
| 2 | BAN-206 | Data Mining Techniques | 3-0-0-3 | BAN-104 |
| 3 | BAN-301 | Statistical Inference | 3-0-0-3 | BAN-201 |
| 3 | BAN-302 | Regression Analysis | 3-0-0-3 | BAN-104 |
| 3 | BAN-303 | Time Series Forecasting | 3-0-0-3 | BAN-201 |
| 3 | BAN-304 | Database Management Systems | 3-0-0-3 | BAN-106 |
| 3 | BAN-305 | Optimization Methods | 3-0-0-3 | BAN-203 |
| 3 | BAN-306 | Business Process Modeling | 2-0-0-2 | - |
| 4 | BAN-401 | Machine Learning Fundamentals | 3-0-0-3 | BAN-202 |
| 4 | BAN-402 | Deep Learning and Neural Networks | 3-0-0-3 | BAN-401 |
| 4 | BAN-403 | Text Mining and NLP | 3-0-0-3 | BAN-206 |
| 4 | BAN-404 | Data Visualization Techniques | 2-0-0-2 | BAN-104 |
| 4 | BAN-405 | Financial Risk Modeling | 3-0-0-3 | BAN-301 |
| 4 | BAN-406 | Customer Analytics | 3-0-0-3 | BAN-206 |
| 5 | BAN-501 | Advanced Predictive Modeling | 3-0-0-3 | BAN-401 |
| 5 | BAN-502 | Supply Chain Analytics | 3-0-0-3 | BAN-305 |
| 5 | BAN-503 | Marketing Analytics | 3-0-0-3 | BAN-406 |
| 5 | BAN-504 | Healthcare Data Analysis | 3-0-0-3 | BAN-301 |
| 5 | BAN-505 | Risk Management Systems | 3-0-0-3 | BAN-405 |
| 5 | BAN-506 | Behavioral Analytics | 2-0-0-2 | BAN-301 |
| 6 | BAN-601 | Big Data Technologies | 3-0-0-3 | BAN-402 |
| 6 | BAN-602 | Cloud Computing for Analytics | 3-0-0-3 | BAN-106 |
| 6 | BAN-603 | Enterprise Data Governance | 3-0-0-3 | BAN-505 |
| 6 | BAN-604 | Advanced Visualization Tools | 2-0-0-2 | BAN-404 |
| 6 | BAN-605 | Business Strategy and Analytics | 3-0-0-3 | - |
| 6 | BAN-606 | Data Ethics and Compliance | 2-0-0-2 | - |
| 7 | BAN-701 | Capstone Project I | 4-0-0-4 | BAN-605 |
| 7 | BAN-702 | Advanced Research in Analytics | 3-0-0-3 | BAN-501 |
| 7 | BAN-703 | Industry Internship | 6-0-0-6 | BAN-605 |
| 8 | BAN-801 | Capstone Project II | 6-0-0-6 | BAN-701 |
| 8 | BAN-802 | Professional Practice and Ethics | 2-0-0-2 | - |
| 8 | BAN-803 | Entrepreneurship in Analytics | 2-0-0-2 | - |
Advanced Departmental Electives
The department offers a rich selection of advanced elective courses that enable students to specialize in specific areas of interest. These courses are designed to complement the core curriculum and provide deeper insights into specialized domains within business analytics.
Deep Learning and Neural Networks (BAN-402)
This course delves into the architecture and application of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students will implement neural networks using TensorFlow and PyTorch frameworks while analyzing real-world datasets. The course emphasizes practical implementation and performance optimization techniques.
Text Mining and NLP (BAN-403)
Exploring natural language processing techniques for extracting meaningful insights from textual data, this course covers tokenization, sentiment analysis, named entity recognition, and topic modeling. Students will work with large-scale text datasets and develop applications using tools like spaCy and NLTK.
Supply Chain Analytics (BAN-502)
This course focuses on applying analytical techniques to optimize supply chain operations, including demand forecasting, inventory management, and logistics planning. Students will analyze real-world case studies from major retailers and manufacturing companies using optimization models and simulation techniques.
Marketing Analytics (BAN-503)
Students explore how data analytics can drive marketing strategies through customer segmentation, campaign performance measurement, and personalized recommendation systems. The course includes hands-on projects with marketing platforms like Google Analytics, Adobe Analytics, and Salesforce CRM.
Healthcare Data Analysis (BAN-504)
Designed for students interested in healthcare applications, this course covers data analysis methods used in clinical research, epidemiology, and patient outcomes evaluation. Students will work with real medical datasets and learn about regulatory compliance issues in healthcare analytics.
Risk Management Systems (BAN-505)
This course examines various risk assessment methodologies and their applications in financial institutions, insurance companies, and regulatory bodies. Topics include credit risk modeling, operational risk analysis, and catastrophe modeling using statistical and machine learning approaches.
Behavioral Analytics (BAN-506)
Exploring human behavior through data-driven approaches, this course combines psychological theories with analytical methods to understand decision-making processes and consumer preferences. Students will design experiments and interpret behavioral datasets using advanced statistical techniques.
Big Data Technologies (BAN-601)
This course introduces students to distributed computing frameworks such as Apache Spark, Hadoop, and Kafka for processing large-scale datasets. Practical labs involve working with real-world big data challenges in various industries including finance, e-commerce, and telecommunications.
Cloud Computing for Analytics (BAN-602)
Students learn how to deploy and manage analytics workloads on cloud platforms such as AWS, Google Cloud Platform, and Microsoft Azure. The course covers topics like data lakes, serverless computing, and containerization using Docker and Kubernetes.
Enterprise Data Governance (BAN-603)
This course explores the principles and practices of managing enterprise data assets effectively, including data quality management, metadata governance, and compliance frameworks. Students will develop skills in designing data governance strategies and implementing regulatory requirements like GDPR and HIPAA.
Advanced Visualization Tools (BAN-604)
Building upon foundational visualization concepts, this course teaches advanced techniques for creating interactive dashboards, storytelling with data, and visual analytics using tools such as Tableau, Power BI, and D3.js. Students will design custom visualizations for complex business problems.
Business Strategy and Analytics (BAN-605)
This course integrates strategic thinking with analytical capabilities to help students understand how data-driven insights can inform business decisions at executive levels. Case studies from global companies provide practical context for applying analytics in strategic planning and competitive positioning.
Project-Based Learning Philosophy
The department strongly believes that project-based learning is essential for developing practical skills and preparing students for real-world challenges. Projects are integrated throughout the curriculum to reinforce theoretical concepts with hands-on experience.
Mini-Projects
Throughout the program, students undertake mini-projects in groups of 3-5 members, focusing on specific business problems or analytical techniques. These projects are supervised by faculty mentors and typically span two months. Mini-projects cover areas such as exploratory data analysis, hypothesis testing, regression modeling, and visualization.
Final-Year Thesis/Capstone Project
The capstone project is the culmination of the student's learning journey, requiring them to tackle a significant business challenge using advanced analytics techniques. Students select projects in consultation with faculty mentors, often inspired by industry needs or personal interests. The project involves extensive research, data collection, model development, and presentation to industry experts.
Evaluation Criteria
Projects are evaluated based on technical depth, business relevance, innovation, teamwork, and presentation quality. Each project component is assessed individually, with final grades determined by a combination of peer evaluations, mentor feedback, and public presentations.
Project Selection Process
Students begin selecting projects in their third year, working closely with faculty advisors to identify feasible topics aligned with their interests and program outcomes. The selection process includes proposal submissions, initial reviews, and ongoing guidance throughout the project lifecycle.