Curriculum Overview
The Data Analysis program at Birla Institute Of Applied Sciences is structured over eight semesters, with a balanced mix of core foundational courses, departmental electives, science electives, and laboratory sessions. The curriculum is designed to ensure students acquire both theoretical knowledge and practical skills essential for modern data analysis roles.
Course Structure by Semester
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MATH101 | Calculus I | 3-0-0-3 | - |
| 1 | MATH102 | Linear Algebra | 3-0-0-3 | - |
| 1 | STAT101 | Probability Theory | 3-0-0-3 | - |
| 1 | CS101 | Introduction to Programming | 2-0-2-3 | - |
| 1 | ENGG101 | Engineering Fundamentals | 2-0-0-2 | - |
| 2 | MATH201 | Calculus II | 3-0-0-3 | MATH101 |
| 2 | STAT201 | Statistical Inference | 3-0-0-3 | STAT101 |
| 2 | CS201 | Data Structures and Algorithms | 2-0-2-3 | CS101 |
| 2 | DBMS101 | Database Systems | 2-0-2-3 | - |
| 2 | ENG102 | Communication Skills | 2-0-0-2 | - |
| 3 | MATH301 | Advanced Calculus | 3-0-0-3 | MATH201 |
| 3 | STAT301 | Time Series Analysis | 3-0-0-3 | STAT201 |
| 3 | ML101 | Machine Learning Fundamentals | 3-0-0-3 | STAT201, CS201 |
| 3 | CS301 | Web Technologies | 2-0-2-3 | CS101 |
| 3 | DS101 | Data Science Lab | 0-0-4-2 | - |
| 4 | MATH401 | Differential Equations | 3-0-0-3 | MATH301 |
| 4 | STAT401 | Bayesian Statistics | 3-0-0-3 | STAT201 |
| 4 | ML201 | Deep Learning | 3-0-0-3 | ML101 |
| 4 | CS401 | Software Engineering | 2-0-2-3 | CS201 |
| 4 | DS201 | Advanced Data Science Lab | 0-0-4-2 | DS101 |
| 5 | STAT501 | Natural Language Processing | 3-0-0-3 | ML101 |
| 5 | ML301 | Computer Vision | 3-0-0-3 | ML201 |
| 5 | CS501 | Big Data Technologies | 2-0-2-3 | DBMS101 |
| 5 | DS301 | Specialized Analytics Lab | 0-0-4-2 | DS201 |
| 6 | STAT601 | Financial Modeling | 3-0-0-3 | STAT401 |
| 6 | ML401 | Reinforcement Learning | 3-0-0-3 | ML201 |
| 6 | CS601 | Cybersecurity | 2-0-2-3 | CS401 |
| 6 | DS401 | Capstone Project Lab | 0-0-4-2 | DS301 |
| 7 | STAT701 | Healthcare Analytics | 3-0-0-3 | STAT501 |
| 7 | ML501 | Advanced Deep Learning | 3-0-0-3 | ML401 |
| 7 | CS701 | Cloud Computing | 2-0-2-3 | CS601 |
| 7 | DS501 | Industry Collaboration Project | 0-0-4-2 | DS401 |
| 8 | STAT801 | Research Methodology | 3-0-0-3 | - |
| 8 | ML601 | Capstone Thesis | 3-0-0-3 | ML501 |
| 8 | DS601 | Final Project Presentation | 0-0-4-2 | DS501 |
Advanced Departmental Electives
The department offers a wide range of advanced departmental elective courses designed to provide specialized knowledge in various domains of data analysis. These courses are intended to deepen students' expertise and prepare them for advanced roles in specific industries or research areas.
Natural Language Processing
This course explores the intersection of linguistics, computer science, and artificial intelligence. Students learn to build systems that can understand, interpret, and generate human language. Topics include sentiment analysis, named entity recognition, machine translation, and dialogue systems. The course uses frameworks like spaCy, NLTK, and Hugging Face Transformers.
Computer Vision
Focused on teaching students how computers can interpret and understand visual information from the world, this course covers image classification, object detection, segmentation, and tracking. It delves into convolutional neural networks (CNNs), transfer learning, and applications in robotics, medical imaging, and autonomous vehicles.
Time Series Analysis
This course focuses on analyzing temporal data, including forecasting, anomaly detection, and modeling seasonal patterns. Students work with datasets from finance, climate science, and economics to develop models that predict future trends using historical observations.
Financial Modeling
Designed for students interested in quantitative finance, this course introduces mathematical models used to evaluate financial assets and markets. It covers derivatives pricing, portfolio optimization, risk management, and algorithmic trading strategies.
Big Data Technologies
This course provides hands-on experience with distributed computing frameworks like Apache Hadoop, Spark, Kafka, and Flink. Students learn how to process large volumes of data efficiently using cluster computing and implement scalable analytics pipelines.
Reinforcement Learning
Exploring the theory and practice of reinforcement learning algorithms, this course teaches students to build agents that learn optimal behaviors through trial and error. Applications include robotics, game AI, autonomous navigation, and recommendation systems.
Geospatial Analytics
This elective focuses on analyzing spatial data using GIS tools and geospatial databases. Students explore mapping techniques, spatial statistics, location-based services, and urban planning applications in smart cities.
Cybersecurity Analytics
Combining cybersecurity principles with data analysis techniques, this course teaches students to detect and respond to threats using network logs, user behavior analytics, and intrusion detection systems. It includes real-time incident response simulations and forensic investigations.
Deep Learning
This advanced course covers modern architectures in deep learning including recurrent networks, transformers, attention mechanisms, and generative adversarial networks (GANs). Students implement models using TensorFlow and PyTorch for image recognition, text generation, and speech synthesis tasks.
Data Visualization & Communication
Teaching students how to effectively present complex data insights through charts, dashboards, and interactive visualizations, this course emphasizes storytelling with data. Tools like Tableau, Power BI, D3.js, and Plotly are introduced for creating compelling narratives from datasets.
Ethics in Data Science
This interdisciplinary course explores ethical considerations in data science, including bias in algorithms, privacy concerns, transparency, fairness, and governance. Students examine case studies involving real-world dilemmas such as facial recognition technology, social media manipulation, and predictive policing.
Project-Based Learning Philosophy
The Data Analysis program at Birla Institute Of Applied Sciences places significant emphasis on project-based learning to ensure students gain practical experience while applying theoretical concepts. The philosophy centers around fostering innovation, collaboration, and problem-solving abilities through hands-on engagement with real-world datasets.
Mini-projects are introduced in the third year, where students work individually or in small teams to solve specific analytical challenges. These projects involve defining research questions, gathering and cleaning data, applying appropriate models, interpreting results, and presenting findings to peers and faculty members.
The final-year capstone project is a comprehensive endeavor that requires students to tackle a complex, open-ended problem in their chosen specialization track. Projects often originate from industry partnerships or faculty research initiatives and may result in publishable papers, patent applications, or startup ventures.
Faculty mentors play a crucial role in guiding students throughout the project lifecycle. Each student is assigned a mentor based on their interests, background, and career goals. Regular meetings, feedback sessions, and progress reviews ensure that projects stay aligned with academic standards and industry expectations.