B.Sc. CS (Artificial Intelligence & Data Science)
B.Sc. Computer Science (Artificial Intelligence & Data Science)
Empowering Future Innovators through Data Intelligence
Artificial Intelligence and Data Science represent the cornerstone of the fourth industrial revolution, merging computational intelligence with statistical analysis to transform complex data into actionable insights. This multidisciplinary field focuses on the development of autonomous systems, predictive modeling, and intelligent algorithms that drive innovation across global industries.
The B.Sc. Computer Science (AI & DS) programme at KG College of Arts and Science (KGCAS) is designed to equip students with a robust foundation in machine learning, big data analytics, and cognitive reasoning. Operating under an autonomous framework, the curriculum integrates theoretical depth with intensive practical applications, ensuring graduates are prepared to address real-world challenges in sectors such as healthcare, finance, e-commerce, and beyond.
Programme Overview
Programme Type
Total Duration
Department
Programme Status
Credits Required
Core Courses
Elective Courses
Ability Enhancement Courses
Campus Placements
No placements found.
B.Sc. Computer Science (AI&DS) at KGCAS
Industry-Inspired Curriculum
Data-Driven Problem Solving
Advance Laboratory Infrastructure
Expert Academic Guidance
Research and Innovation Culture
Career Readiness
An Innovative Way to Graduate at KGCAS
An innovative pathway into the world of artificial intelligence, machine learning, data analytics and data science through KGCAS’ distinctive experiential learning approach:
PEOs | PSOs | POs
Programme Educational Objectives
The B.Sc. Computer Science (AI&DS) programme describes accomplishments that graduates are expected to attain within five to seven years after graduation.
PEO1
To enrich knowledge in core areas related to the field of Artificial Intelligence and Data Science.
PEO2
To provide opportunities for acquiring in-depth knowledge in AI tools and techniques and thereby design and implement software projects to meet customers' business objectives.
PEO3
To enable graduates to pursue higher education leading to Master and Research Degrees or have a successful career in industries associated with Artificial Intelligence or as entrepreneurs or as a startup.
PEO4
To enhance communicative skills and inculcate team spirit through professional activities and skills in handling complex problems in data science and research projects to make them a better team player.
PEO5
To embed human values and professional ethics in the young minds and contribute towards nation building.
Programme Specific Outcomes
After the successful completion of the B.Sc. Computer Science (AI&DS) programme, the students are expected to:
PSO1
Impart the fundamental principles and methods of Artificial Intelligence and Data Science across diverse applications.
PSO2
Develop and deploy applications of varying complexity using the acquired knowledge in various programming languages, understanding the ethical implications of AI algorithms.
PSO3
Investigate the transformative potential in solving complex problems, automating processes, and generating insights from data.
PSO4
Identify and solve complex problems, enhancing efficiency and driving innovation across multiple sectors.
PSO5
Ability to identify, interpret, analyze, and design solutions using appropriate algorithms of varying complexities in the field of Artificial Intelligence and Data Science.
Programme Outcomes
On successful completion of the B.Sc. Computer Science (AI&DS) programme:
PO1
Disciplinary Knowledge: Capable to apply the knowledge of mathematics, algorithmic principles, and computing fundamentals in the AI models, machine learning algorithms, and data-driven systems of varying complexity.
PO2
Scientific Reasoning / Problem Analysis: Ability to critically analyse, categorise, formulate, and solve the problems that emerge in the field of Artificial Intelligence.
PO3
Problem Solving: Able to provide a structured approach to understand, analyze, and develop solutions to complex problems using mathematical, algorithmic, and computational techniques.
PO4
Environment and Sustainability: Understand the impact of software solutions in data-driven insights, optimize resource use, and drive efficiencies across various sectors.
PO5
Modern Tool Usage: Use AI-powered tools necessary for integrated solutions.
PO6
Ethics: Function effectively with social, cultural, and ethical responsibility as an individual or as a team member with a positive attitude.
PO7
Cooperation / Teamwork: Function effectively as a member or leader on multidisciplinary teams to accomplish a common objective.
PO8
Communication Skills: Ability to communicate effectively with diverse types of audiences and prepare and present technical documents to different groups.
PO9
Self-directed and Life-long Learning: Recognize the need for self-motivation to engage in lifelong learning to stay up-to-date with evolving technology.
PO10
Research Culture: Enhance research culture and uphold scientific integrity and objectivity.
Curriculum Overview
The curriculum follows the Choice-Based Credit System (CBCS), offering academic flexibility and an outcome-orientated learning approach. It balances core computational principles with specialised training in Artificial Intelligence and Data Science.
Core Curriculum
Year I
Programming in Python (Core)
Data Literacy & Fundamentals of Data Science (Core)
Linear Algebra & Discrete Mathematics (Allied)
Java Programming (Core)
Advanced Excel for Data Analytics (Core)
Optimization Techniques (Allied)
Year II
Data Structures (Core)
Foundations of Artificial Intelligence (Core)
Probability and Statistics (Allied)
Database Management Systems (Core)
Software Engineering (Core)
Fuzzy Logic (Allied)
Year III
R Programming (Core)
Tensor Flow (Core)
Social Network Analysis (Core)
Soft Computing (Core)
Embedded Systems (Core)
Additional Programme Components
Elective Courses
Discipline-focused electives
Data Mining and Data Warehousing / Big Data Analytics / Data Visualisation
Neural Network and Deep Learning / Robotic Process Automation / Text and Speech Analysis
Skill Enhancement Courses
Practical and application-oriented skill development
Advanced Generative Models and Ethics
Business Intelligence
Machine Learning & Advanced SQL Lab
Data Visualisation using Tableau Lab
Communication Skills
Aptitude & Logical Thinking
Coding Practice
Git & Version Control
Creative Circle
Problem-Solving Techniques
Value Added Courses
Generative AI for Software Developers
Data Literacy & Fundamentals of Data Science
Data Analysis and Visualization with Power BI
Advanced Java Development
Internships / Projects
Industry exposure and experiential learning opportunities
Careers and Futures
Graduates of the B.Sc. CS (AI & DS) programme pursue careers across IT, Analytics, Research, and Emerging Technology sectors. The programme also provides a strong foundation for postgraduate studies and competitive examinations.
Career Opportunities
Professional Certifications
Mainstream professional pathways
CA
Chartered Accountancy
CMA
Cost and Management Accountancy
CS
Company Secretary
International professional pathways
ACCA
Association of Chartered Certified Accountants
CFA
Chartered Financial Analyst
Higher Studies
M.Sc. Artificial Intelligence
M.Sc. Data Science / Data Analytics
M.Sc. Machine Learning
M.Sc. Software Systems
MCA (Master of Computer Applications)
MBA (IT/Analytics)
MBA (IT/Analytics)
Eligibility and Admission
Admission is open to candidates who meet the prescribed eligibility criteria as per university and institutional norms. The selection process follows a transparent, merit-based admission framework.
Check-list of enclosures with application