Comprehensive Course Catalogue
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | MATH101 | Calculus I | 3-0-0-3 | None |
| 1 | PHYS101 | Physics for Engineers | 3-0-0-3 | None |
| 1 | CSE101 | Introduction to Programming | 2-0-2-3 | None |
| 1 | MATH102 | Linear Algebra | 3-0-0-3 | None |
| 1 | STAT101 | Probability and Statistics I | 3-0-0-3 | MATH101 |
| 2 | MATH201 | Calculus II | 3-0-0-3 | MATH101 |
| 2 | CSE201 | Data Structures and Algorithms | 3-0-2-5 | CSE101 |
| 2 | STAT201 | Probability and Statistics II | 3-0-0-3 | STAT101 |
| 2 | CSE202 | Database Systems | 3-0-2-5 | CSE101 |
| 2 | PHYS201 | Modern Physics | 3-0-0-3 | PHYS101 |
| 3 | CSE301 | Machine Learning Fundamentals | 3-0-2-5 | CSE201, STAT201 |
| 3 | STAT301 | Statistical Inference | 3-0-0-3 | STAT201 |
| 3 | CSE302 | Computer Vision | 3-0-2-5 | CSE201, MATH201 |
| 3 | MATH301 | Optimization Techniques | 3-0-0-3 | MATH201 |
| 3 | CSE303 | Natural Language Processing | 3-0-2-5 | CSE201, STAT201 |
| 4 | CSE401 | Deep Learning | 3-0-2-5 | CSE301 |
| 4 | STAT401 | Time Series Analysis | 3-0-0-3 | STAT301 |
| 4 | CSE402 | Reinforcement Learning | 3-0-2-5 | CSE301, MATH301 |
| 4 | MATH401 | Advanced Calculus | 3-0-0-3 | MATH201 |
| 4 | CSE403 | Big Data Technologies | 3-0-2-5 | CSE202 |
| 5 | CSE501 | Special Topics in AI | 3-0-2-5 | CSE401 |
| 5 | STAT501 | Bayesian Statistics | 3-0-0-3 | STAT301 |
| 5 | CSE502 | Cybersecurity in Data Science | 3-0-2-5 | CSE202, CSE301 |
| 5 | MATH501 | Mathematical Modeling | 3-0-0-3 | MATH301 |
| 5 | CSE503 | Data Visualization | 3-0-2-5 | CSE301 |
| 6 | CSE601 | Capstone Project I | 4-0-0-4 | Pre-requisites for 5th semester courses |
| 6 | STAT601 | Advanced Statistical Methods | 3-0-0-3 | STAT501 |
| 6 | CSE602 | Applied Machine Learning | 3-0-2-5 | CSE401, CSE501 |
| 6 | MATH601 | Advanced Optimization | 3-0-0-3 | MATH501 |
| 6 | CSE603 | Industry Projects | 4-0-0-4 | CSE601 |
| 7 | CSE701 | Capstone Project II | 4-0-0-4 | CSE601 |
| 7 | STAT701 | Research Methods in Data Science | 3-0-0-3 | STAT601 |
| 7 | CSE702 | Advanced Cybersecurity | 3-0-2-5 | CSE502 |
| 7 | MATH701 | Stochastic Processes | 3-0-0-3 | MATH601 |
| 7 | CSE703 | Business Analytics | 3-0-2-5 | CSE503 |
| 8 | CSE801 | Thesis/Internship | 4-0-0-4 | CSE701 |
| 8 | STAT801 | Research Ethics and Compliance | 3-0-0-3 | STAT701 |
| 8 | CSE802 | Advanced Topics in Data Science | 3-0-2-5 | CSE702 |
| 8 | MATH801 | Mathematical Foundations for AI | 3-0-0-3 | MATH701 |
| 8 | CSE803 | Capstone Defense | 4-0-0-4 | CSE801 |
Detailed Course Descriptions for Departmental Electives
Machine Learning Fundamentals is an introductory course that covers the core concepts of supervised and unsupervised learning, regression techniques, classification algorithms, and model evaluation methods. Students gain hands-on experience with scikit-learn and TensorFlow, preparing them for more advanced coursework in machine learning.
Deep Learning introduces students to neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. The course emphasizes practical implementation using Python libraries like Keras and PyTorch, with projects involving image recognition, natural language understanding, and time series forecasting.
Computer Vision explores the principles and applications of computer vision systems, including edge detection, feature extraction, object recognition, and segmentation techniques. Students work on real-world datasets and implement advanced architectures such as YOLO and ResNet for various visual tasks.
Natural Language Processing delves into text preprocessing, tokenization, sentiment analysis, named entity recognition, and machine translation. The course includes practical sessions with tools like spaCy, NLTK, and Hugging Face Transformers to build language models and analyze textual data.
Reinforcement Learning focuses on decision-making processes in uncertain environments using Markov Decision Processes (MDPs). Students learn about Q-learning, policy gradients, actor-critic methods, and deep reinforcement learning algorithms. Projects involve training agents to play games or navigate environments.
Cybersecurity for Data Science addresses the challenges of protecting sensitive data in analytics workflows. Topics include encryption, access control, privacy-preserving techniques, and compliance frameworks. Students evaluate real-world cybersecurity incidents and propose mitigation strategies using cryptographic protocols and security tools.
Big Data Technologies covers distributed computing frameworks like Apache Spark and Hadoop for processing large-scale datasets. The course includes hands-on labs on cluster configuration, data pipelines, and optimization techniques for handling streaming data and batch processing tasks.
Data Visualization teaches students how to present complex data in intuitive and compelling ways using tools such as Tableau, Power BI, and D3.js. Emphasis is placed on storytelling through charts, maps, dashboards, and interactive visualizations that enhance understanding of analytical findings.
Quantitative Finance applies mathematical models to financial markets, covering topics like derivatives pricing, portfolio optimization, risk management, and algorithmic trading strategies. Students use Python for simulations and backtesting, gaining insights into market behavior and financial decision-making.
Business Intelligence and Analytics explores the intersection of data science with business operations, focusing on reporting, dashboards, predictive analytics, and performance metrics. Students learn to align analytical outputs with strategic business goals using tools like SQL, Excel, and BI platforms.
Data Engineering and Big Data Analytics provides a comprehensive overview of data pipeline design, ETL processes, data warehousing, and cloud-based storage solutions. The course emphasizes scalability, reliability, and performance optimization in large-scale data environments.
Healthcare Informatics integrates data science with clinical workflows to improve patient outcomes and healthcare delivery systems. Students analyze medical datasets, develop diagnostic tools, and evaluate public health interventions using statistical modeling and machine learning approaches.
Applied Machine Learning bridges the gap between theory and practice by applying machine learning techniques to real-world problems across various domains. Students work on capstone projects involving data collection, preprocessing, model selection, training, and deployment in production environments.
Research Methods in Data Science equips students with the skills necessary for conducting independent research, including hypothesis formulation, experimental design, statistical inference, and literature review techniques. The course prepares students for thesis writing and academic publication in peer-reviewed journals.
Mathematical Modeling introduces students to the art of translating real-world phenomena into mathematical representations using differential equations, linear algebra, optimization theory, and stochastic processes. Projects include modeling population dynamics, economic trends, and physical systems through computational methods.
Project-Based Learning Philosophy
BIMT's departmental philosophy centers on project-based learning as a core pedagogical approach that bridges the gap between classroom theory and real-world application. This methodology ensures that students develop not only technical proficiency but also critical thinking, problem-solving, and collaborative skills essential in today’s data-driven landscape.
The curriculum includes mandatory mini-projects starting from the second semester, where students work on small-scale problems under faculty supervision. These projects help students grasp fundamental concepts through hands-on experimentation and iterative development cycles.
As students advance to the final year, they are required to complete a comprehensive capstone project that addresses a significant challenge in data science. The project typically spans two semesters and involves collaboration with industry partners or research groups, ensuring relevance and impact.
Students select their projects based on personal interests and career aspirations, guided by faculty mentors who provide expert advice and feedback throughout the process. This mentorship system ensures that students receive personalized support and guidance tailored to their individual goals.
Evaluation criteria for all projects are clearly defined and include documentation quality, innovation level, technical execution, presentation skills, and overall impact on the field. Students must demonstrate proficiency in both written reports and oral presentations, preparing them for professional settings where clear communication is paramount.