BTech Data Science Syllabus Structure (4 Years)
The BTech Data Science syllabus is designed to combine engineering fundamentals with data analytics, programming, and artificial intelligence.
It focuses on building strong mathematical foundations, computational thinking, and real-world problem-solving skills using data-driven approaches.
Year 1: Foundation & Engineering Basics
The first year builds a strong base in mathematics, programming, and core engineering subjects essential for data science studies.
Semester 1
- Engineering Mathematics I: Linear algebra, calculus, and differential equations.
- Programming for Problem Solving: Basics of programming logic using C/Python.
- Engineering Physics: Fundamentals of mechanics and electronics.
- Communication Skills: Technical writing and professional communication.
Semester 2
- Engineering Mathematics II: Probability, statistics, and numerical methods.
- Object-Oriented Programming: Java/Python with OOP concepts.
- Engineering Chemistry: Materials and chemical processes.
- Environmental Studies: Sustainability and environmental awareness.
Year 2: Core Data Science Concepts
The second year introduces students to core computer science and data handling concepts required for analytics and modeling.
Semester 3
- Data Structures & Algorithms: Arrays, trees, graphs, and algorithm design.
- Database Management Systems: SQL, normalization, and transactions.
- Discrete Mathematics: Logic, sets, relations, and combinatorics.
- Operating Systems: Process management and memory concepts.
Semester 4
- Statistics for Data Science: Hypothesis testing and regression analysis.
- Computer Networks: Network models, protocols, and security basics.
- Data Visualization: Charts, dashboards, and storytelling with data.
- Design & Analysis of Algorithms: Complexity and optimization techniques.
Year 3: Advanced Analytics & Machine Learning
This year focuses on advanced analytics, machine learning models, and big data technologies used in industry.
Semester 5
- Machine Learning: Supervised and unsupervised learning algorithms.
- Big Data Analytics: Hadoop, Spark, and distributed systems.
- Artificial Intelligence: Search algorithms and knowledge representation.
- Elective I: Domain-specific elective subject.
Semester 6
- Deep Learning: Neural networks and deep architectures.
- Natural Language Processing: Text analytics and language models.
- Cloud Computing: Data storage and analytics on cloud platforms.
- Mini Project: Applied data science project.
Year 4: Specialization & Industry Exposure
The final year emphasizes specialization, industry exposure, and practical implementation through projects and internships.
Semester 7
- Data Security & Privacy: Ethical and legal aspects of data usage.
- Elective II & III: Advanced specialization subjects.
- Industry Internship: Real-world exposure to data science roles.
Semester 8
- Major Project: End-to-end data science solution development.
- Project Viva Voce: Evaluation and presentation.
Recommended Books for BTech Data Science
| Subject |
Book Title |
Author |
| Data Science |
Introduction to Data Science |
Joel Grus |
| Machine Learning |
Machine Learning |
Tom M. Mitchell |
| Statistics |
Practical Statistics for Data Scientists |
Peter Bruce |
| AI |
Artificial Intelligence: A Modern Approach |
Stuart Russell & Peter Norvig |
BTech Data Science Syllabus FAQs
Q1: Does the BTech Data Science syllabus require strong mathematics skills?
Yes, mathematics plays a crucial role in data science.
Subjects like statistics, probability, and linear algebra are core components.
These concepts support machine learning and data modeling techniques.
Q2: How much programming is included in the BTech Data Science syllabus?
Programming is a major part of the syllabus.
Students learn Python, Java, and data handling libraries.
Coding skills are developed gradually across semesters.
Q3: Are machine learning and artificial intelligence covered in detail?
Yes, machine learning and AI are core subjects in later semesters.
Students learn algorithms, models, and practical implementation.
Advanced topics like deep learning are also included.
Q4: Does the syllabus include real-world projects and internships?
Yes, project work and internships are integral parts of the curriculum.
Students apply concepts to real-world datasets.
Industry exposure improves practical understanding.
Q5: Is cloud computing part of the BTech Data Science syllabus?
Cloud computing is included in advanced semesters.
Students learn cloud-based data storage and analytics tools.
This prepares them for modern industry environments.
Q6: Are electives available for specialization in the final years?
Yes, students can choose electives based on interest areas.
Options may include AI, NLP, finance analytics, or healthcare analytics.
Electives help in career-focused skill development.
Q7: Does the syllabus prepare students for data science industry roles?
Yes, the syllabus is industry-oriented.
It focuses on tools, technologies, and problem-solving skills.
Graduates are well-prepared for entry-level data roles.
Q8: Can the BTech Data Science syllabus support higher studies and research?
Yes, the syllabus provides a strong academic foundation.
It supports higher studies such as MTech, MS, or research programs.
Research-oriented students benefit from analytical depth.
