B.Tech Artificial Intelligence and Data Science
About
The B.Tech program in Artificial Intelligence and Data Science (AI & DS) at J.N.N Institute of Engineering is a forward-looking course that blends core computer science with advanced artificial intelligence technologies. This program is designed to nurture future-ready professionals capable of solving complex problems through intelligent systems and data-driven insights.
Accredited by the AICTE and affiliated with Anna University, the program aims to develop skilled professionals who are ready to tackle the challenges of the evolving tech landscape.
With a curriculum tailored to industry demands, the program covers key areas such as machine learning, deep learning, data visualization, big data analytics, natural language processing, and robotics. Students gain hands-on experience with tools and technologies like Python, R, TensorFlow, Keras, and cloud platforms, ensuring they are well-prepared to meet the challenges of tomorrow’s tech landscape.
Why Choose AI & DS at J.N.N?
- Industry-Aligned Curriculum: Designed in collaboration with AI experts and industry leaders
- State-of-the-Art Labs: Equipped with the latest software and hardware for real-time projects
- Experienced Faculty: A dedicated team of mentors with research and industry exposure
- Skill Development: Focus on coding, algorithmic thinking, and data interpretation
- Career Readiness: Workshops, internships, and industry certifications to enhance employability
- Research and Innovation: Opportunities to work on funded projects and publish papers
Career Prospects:
Graduates of this program are highly sought after in industries such as IT, healthcare, finance, manufacturing, and e-commerce. Potential roles include:
- Data Scientist
- AI/ML Engineer
- Business Intelligence Analyst
- Data Engineer
- AI Researcher
- Automation Consultant
- Big Data Engineer.
“Shape your future with AI. Innovate. Analyze. Transform — at J.N.N Institute of Engineering.”
From the HOD’s Desk
Dr P.G Kuppusamy
Professor & Head
This program is designed to equip students with cutting-edge skills in AI, machine learning, data analytics, and deep learning—key areas shaping the future of technology. Our dedicated faculty and modern lab infrastructure provide a strong foundation for innovation, research, and industry readiness.We foster a learning environment that encourages problem-solving, critical thinking, and real-world application through projects, certifications, internships, and hackathons. Our goal is to prepare students to become AI professionals and data scientists who can lead in an increasingly digital world.
Vision - Mission
Vision:
To impart quality-education, inculcate professionalism and enhance the problem-solving skills of the students in the domain of Artificial Intelligence & Data Science by applying recent technological tools and incorporating collaborative principles with a focus to make them industry ready.
Mission:
To enhance the knowledge of the students with most recent advancements and refresh their insights in the field of Artificial Intelligence and Data Science.
- To equip the students with strong fundamental concepts, analytical capability, programming and problem-solving skills.
- To make the students industry ready and to enhance their employability through training, internships and real-time projects.
- To guide the students to perform research on Artificial Intelligence and Data Science, with the aim to provide solutions to the problems of the industry.
PO/PSO/PEO
Program Outcomes (POs) Engineering Graduates Will Be Able To:
- Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and Artificial Intelligence and Data Science basics to the solution of complex engineering problems.
- Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
- Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
- Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
- Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
- The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
- Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
- Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
- Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
- Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
- Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one‘s own work, as a member and leader in a team, to manage projects and in multidisciplinary environment
- Life–long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological changes.
Programme Specific Outcomes (PSO’s)
- Graduates should be able to evolve AI based efficient domain specific processes for effective decision making in several domains such as business and governance domains.
- Graduates should be able to arrive at actionable Fore sight, Insight , hind sight from data for solving business and engineering problems
- Graduates should be able to create, select and apply the theoretical knowledge of AI and Data Analytics along with practical industrial tools and techniques to manage and solve wicked societal problems.
Program Educational Objectives (PEOs)
- To provide graduates with the proficiency to utilize the fundamental knowledge of basic sciences, mathematics, Artificial Intelligence, data science and statistics to build systems that require management and analysis of large volume of data.
- To enrich graduates with necessary technical skills to pursue pioneering research in the field of AI and Data Science and create disruptive and sustainable solutions for the welfare of ecosystems.
- To enable graduates to think logically, pursue lifelong learning and collaborate with an ethical attitude in a multidisciplinary team.
Faculty
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Mr. M Vinoth KumarAssistant Professor Specialization Computer Science and Engineering |
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Mrs M.R ArunaAssistant Professor Specialization Computer Science and Engineering |
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Mr.B RajakumarAssistant Professor Specialization Computer Science and Engineering |
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Dr.Thappeta Praveen kumarAssistant Professor Specialization Computer Science and Engineering |
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Mr.A PraveenaAssistant Professor Specialization Power Electronics and Drives |
Lab Facilities
| SEMESTER | SUB.CODE | LABORATORY NAME |
| I | GE8161 | Problem Solving and Python Programming Lab |
| II | AD8261 | Data Structures Design Lab |
| III | AD8311 | Data Science Laboratory |
| III | CS8383 | Object Oriented Programming Laboratory |
| IV | AD8411 | Database Design and Management Laboratory |
| IV | AD8412 | Data Analytics Laboratory |
| IV | AD8413 | Artificial Intelligence – I Laboratory |
| V | AD8511 | Machine Learning Lab |
| V | AD8512 | Mini Project on Data Sciences |
| V | IT8511 | Web Technology Lab |
| VI | AD8611 | Artificial Intelligence – II Lab |
| VII | AD8711 | Deep Learning Lab |
| VII | AD8712 | Mini Project on Analytics |
| VIII | AD8811 | Project Work |
Curriculum and Syllabus
Regulation 2022 – View/Download
The B.Tech in AI and Data Science covers a range of subjects from basic programming to advanced AI technologies. The curriculum is designed to balance theoretical knowledge with practical applications, ensuring students are job-ready upon graduation.
Core Subjects :
Machine Learning
Deep Learning
Big Data Analytics
Neural Networks
AI Ethics
Elective :
Robotics
Natural Language Processing
Reinforcement Learning
Cloud Computing in AI
Professional Society
The Department of Artificial Intelligence and Data Science (AI & DS) at J.N.N Institute of Engineering actively promotes student engagement in professional societies to foster technical excellence, collaborative learning, and industry exposure. These societies provide platforms for students to stay abreast of emerging technologies, showcase their talents, and build strong professional networks.
Key Associations:
IEEE Student Branch – AI & DS Chapter:
Our IEEE Student Branch provides a platform for students to engage in technical events, workshops, and conferences. Activities include participation in IEEE Xtreme programming competitions and seminars on emerging AI technologies.
Computer Society of India (CSI) Chapter
The CSI chapter organizes coding competitions, technical talks, and seminars, providing students with exposure to the latest trends in computing and data science.
Indian Society for Technical Education (ISTE) Membership
Through ISTE, students participate in faculty development programs, technical workshops, and conferences, fostering a culture of continuous learning and innovation.
Activities and Benefits:
- Workshops and Seminars: Regular sessions on topics like machine learning, data analytics, and AI ethics.
- Competitions: Participation in hackathons, coding contests, and innovation challenges at national and international levels.
- Research and Publications: Opportunities to collaborate on research projects and publish findings in reputed journals and conferences.
- Networking: Interaction with industry experts, alumni, and peers through events and conferences.
- Skill Development: Access to resources and training that enhance technical and soft skills, preparing students for successful careers in AI and Data Science.
Internship
Internships are a vital part of engineering education, acting as a bridge between academic learning and practical industry experience. At J.N.N Institute of Engineering, we view internships as an essential step in preparing students to become competent and confident professionals.
Why Internships Matter:
- Real-World Exposure
Internships provide students with the opportunity to apply theoretical knowledge in real-time industrial environments, helping them understand how classroom concepts translate into practical applications. - Skill Enhancement
Through hands-on projects and team collaboration, students improve essential skills such as communication, teamwork, leadership, problem-solving, and critical thinking. - Career Clarity and Exploration
Internships help students explore various fields and roles within engineering, allowing them to make informed decisions about their career paths and specializations. - Professional Networking
Working alongside industry professionals helps students build valuable connections, increasing their chances of securing future job opportunities or mentorship. - Increased Employability
Students with internship experience are often more attractive to employers, as they come with workplace readiness and an understanding of corporate expectations. - Confidence and Professionalism
Experiencing the workflow, deadlines, and dynamics of real companies builds a sense of professionalism and confidence that cannot be gained through textbooks alone.
At J.N.N Institute of Engineering, we actively encourage and support students to pursue internships during their course of study. These experiences not only enrich their academic journey but also shape them into industry-ready graduates prepared to lead and innovate.
Value Added Course
SPECIALIZATION | SEMESTER | SUB.CODE | PROFESSIONAL ELECTIVES |
Data Science & Analytics | IV | AD8002 | HEALTH CARE ANALYTICS |
VI | AD8006 | ENGINEERING PREDICTIVE ANALYTICS | |
VIII | AD8010 | SPEECH PROCESSING AND ANALYTICS | |
VIII | AD8081 | COGNITIVE SCIENCE AND ANALYTICS | |
VIII | AD8012 | NONLINEAR OPTIMIZATION | |
IOT | VI | CS8081 | INTERNET OF THINGS |
CYBER SECURITY | VIII | AD8011 | CYBER SECURITY |
Cloud | VI | CS8791 | CLOUD COMPUTING |
Software Development | IV | AD8001 | SOFTWARE DEVELOPMENT PROCESS |
VI | CW8591 | SOFTWARE ARCHITECTURE | |
VI | CS8072 | AGILE METHODOLOGIES |
