Artificial Intelligence and Data Science
Encouraging the Upcoming Generation of Astute Innovators
B.Tech. Artificial Intelligence and Data Science
Infotech for AI and Data-Core Domains
KiTE’s B.Tech program in Artificial Intelligence and Data Science is an industry-aligned degree designed to develop future-ready AI engineers and data professionals capable of building intelligent, data-driven systems. Rooted in strong foundations of mathematics, statistics, algorithms, and computer science, the program equips students with the theoretical depth and applied skills required to design, develop, and deploy machine learning models, analytical frameworks, and intelligent software solutions.
Through a rigorous combination of core academics, hands-on laboratories, real-world datasets, and industry-integrated learning experiences, students are prepared for high-impact careers in artificial intelligence, data science, analytics, and automation.
Program Overview
Program Type
Duration
Department
Program Status
Credits Required
Core Courses:
Elective Courses
Innovation in Every Algorithm
By combining real-world experience and innovative projects with data science, machine learning, and cognitive computing, KiTE's AI & DS curriculum transforms education. Students go from developing clever algorithms to evaluating enormous datasets that help make important decisions. They lay hands on technical proficiency as well as the curiosity, self-assurance, and progressive mindset required to spearhead the AI revolution.
Why Study AI & DS at KiTE?
Full-Spectrum Program
Industry Partnership
Premium Infrastructure
Focused Specializations
Global Preparedness
Significant Outcomes
At the end of four years, KiTE students emerge as:
Technical Pioneers
Solution Architects
World-Class Professionals
Curriculum Overview
KGiSL Institute of Technology's B.Tech program in Artificial Intelligence & Data Science uses a Choice-Based Credit System (CBCS) that complies with Anna University requirements. Through laboratories, mini-projects, internships, and a significant capstone project, the program is designed to offer solid foundations in mathematics and computing, progressive specialisation in AI & Data Science, and intensive experiential learning. Graduates from the curriculum are expected to be technically proficient, ethically conscious, and prepared for the workforce.
Program Length & Credits
Duration
Total Credits (typical)
Credit Components
Core Courses (PCC)
Professional Electives (PEC)
Employability Skills Electives (ESE),
Employability Enhancement Courses (EEC),
Labs, Seminars, Internship, and Capstone Project
Semester Highlights
Learning in Phases, Growing Through Intelligence
The fundamentals of computer science, programming, mathematics, and data analytics are presented to the students. The technological foundation for AI learning is created by core courses including data structures, probability, linear algebra, and Python programming. While workshops and technical lectures assist students in exploring the range of AI applications, hands-on activities in coding and data visualisation laboratories help students understand analytical thinking at an early age.
Students proceed to more complex subjects like machine learning, database administration, and AI principles. They delve into case studies, conduct data experiments, and undertake mini-projects to see firsthand how data drives intelligence. The main goal? Build models that aren’t just smart, but also predictive and adaptable..
Students dive deep into Deep Learning, Neural Networks, Big Data Analytics, and Natural Language Processing (NLP). Through industry internships, hackathons, and lab simulations, they gain real-world exposure to AI tools like TensorFlow, Keras, and Scikit-learn. Collaborative projects foster teamwork and applied innovation.
The final stretch integrates learning into Capstone Projects, Research, and Emerging Technologies like Computer Vision, Cloud AI, and Edge Computing. Students undertake a major industry project addressing live problems, mentored by corporate experts. By the end, they graduate as well-rounded professionals ready for data-driven leadership roles.
Course Components & Assessment Approach
The ECE program ensures continuous growth through structured skill-building and technical development:
Lectures + Tutorials
Laboratory Practicals
Mini-Projects & Assignments
Seminars & Journal Clubs
End-of-Semester Exams & Project Evaluation
Continuous Assessment
Electives & Specialization Tracks (sample choices)
Students can tailor their learning through professional electives and minors. Example tracks include:
Skill Certifications & Value-added Components
To increase employability, the program integrates value-added certifications and short courses, such as:
Outcome Mapping & Graduate Attributes
By the end of the program, graduates will be able to:
Download & More Details
For the complete semester-wise syllabus, credit allocations, and course descriptions, please download the detailed curriculum (PDF) or contact the admissions office.
Career Opportunities
Beyond the Classroom
AI Hackathons, Ideation Challenges & Seminars by Industry Experts
Value-Added Courses: Power BI, TensorFlow, AWS Machine Learning
Research & Publication Support for Global Conferences
Admissions Open Now
Gallery Section
Why B.Tech. Artificial Intelligence and Data Science at KGiSL Institute of Technology?
Industry-aligned autonomous curriculum
Flipped classrooms with integrated labs
Active industry internships and projects
KGiSL campus placement program
Placements and Achievements
The Department of Artificial Intelligence and Data Science maintains a strong track record of placements. Graduates are consistently recruited by multinational corporations and leading Indian IT services firms.
Top Placements - Campus Recruitment
Vision and Mission
Vision
To develop technically competent and socially responsible computing professionals powered with the required skills in the field of Artificial Intelligence to contribute globally for the benefit of industry and society.
Mission
Objectives and Outcomes
Program Educational Objectives
PEO1
To enable graduates to pursue higher education and research, or have a successful career in industries associated with Computer Science and Engineering, or as entrepreneurs.
PEO2
To ensure that graduates will have the ability and attitude to adapt to emerging technological changes.
PEO3
To help graduates attain professional skills by ensuring life-long learning with a sense of social values.
Program Outcomes
PO1
Engineering knowledge
Apply knowledge of mathematics, natural science, computing, engineering fundamentals, and an engineering specialization, as specified in WK1 to WK4 respectively, to develop solutions for complex engineering problems.
PO2
Problem analysis
Identify, formulate, review research literature, and analyze complex engineering problems, reaching substantiated conclusions with consideration for sustainable development (WK1 to WK4).
PO3
Design / development of solutions
Design creative solutions for complex engineering problems and design/develop systems, components, or processes to meet identified needs, considering public health and safety, whole-life cost, net zero carbon, culture, society, and environment as required (WK5).
PO4
Conduct investigations of complex problems
Conduct investigations of complex engineering problems using research-based knowledge, including design of experiments, modelling, analysis, and interpretation of data, to provide valid conclusions (WK8).
PO5
Engineering tool usage
Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools, including prediction and modelling, recognizing their limitations to solve complex engineering problems (WK2 and WK6).
PO6
The engineer and the world
Analyze and evaluate societal and environmental aspects while solving complex engineering problems, considering their impact on sustainability, economy, health, safety, legal framework, culture, and environment (WK1, WK5, and WK7).
PO7
Ethics
Apply ethical principles and commit to professional ethics, human values, diversity, and inclusion; adhere to national and international laws (WK9).
PO8
Individual and collaborative teamwork
Function effectively as an individual, and as a member or leader in diverse, multidisciplinary teams.
PO9
Communication
Communicate effectively and inclusively within the engineering community and society at large, including comprehending and writing effective reports and design documentation, and making presentations while considering cultural, language, and learning differences.
PO10
Project management and finance
Apply knowledge and understanding of engineering management principles and economic decision-making to one’s own work, as a member and leader in a team, and to manage projects in multidisciplinary environments.
PO11
Life-long learning
Life-long learning: Recognize the need for, and have the preparation and ability for:
Program Specific Outcomes
PSO1
To analyze, design and develop computing solutions by applying foundational concepts of Computer Science and Engineering.
PSO2
To apply software engineering principles and practices for developing quality software for scientific and business applications.
PSO3
To adapt to emerging Information and Communication Technologies (ICT) to innovate ideas and solutions to existing/novel problems.
Curriculum Overview
The B.Tech. Artificial Intelligence and Data Science curriculum at KGISL Institute of Technology is guided by the Anna University regulations and uses a Choice-Based Credit System (CBCS). Spanning four years, the curriculum is designed to provide comprehensive technical depth as well as interdisciplinary flexibility.
Year I
Year II
Year III
How to Apply?
For Registration, submit your
Candidates seeking admission to the B.Tech. AI&DS degree course are required to submit the following certificates: