ATLAS SkillTech University: India's 1st Urban Multidisciplinary University ATLAS SkillTech University

A Curriculum Designed for the Future

Built on the ‘Goal for Each Term’ (GET) philosophy, our curriculum ensures every semester has a clear, outcome-driven focus; continuously evolving with emerging technology trends and the learning needs of our students.

Semester 1

Foundations of Computing and Intelligent Systems

This semester builds a strong foundation in computing, mathematics, and scientific principles essential for artificial intelligence and machine learning. Students learn to program, analyze data, and understand robotics fundamentals while developing core digital communication.

Introduction to Computer Science & Programming 1

Provides a hands-on introduction to computational thinking and programming fundamentals.

Calculus 1

Introduces foundational mathematical concepts and techniques for modeling and problem-solving in AI and design.

Foundations of Statistics & Probability for AI & ML

Covers statistical reasoning and probabilistic methods essential for machine learning and AI applications.

Databases & SQL

Teaches principles of database design, querying, and management using SQL for data-driven projects.

Chemistry

Explores basic chemical principles and their applications in technology, materials, and interactive systems.

Fundamentals of CT, AI, ML, Robotics, and Data Science

Provides interdisciplinary exposure to computing, robotics, and machine learning frameworks driving intelligent automation.

Communication Skills for Digital World

Builds digital communication, presentation, and collaboration skills for effective engagement in tech-driven environments.

ATLAS Electives

Offers interdisciplinary elective courses that combine technology, design, and creative innovation.

Simulation/Computer Game Design (Pinnacle I)

Develops computational design skills through interactive simulations blending programming, logic, and visual storytelling.

Semester 2

Computational Structures and Applied Data Insights

This semester deepens programming, mathematics, and analytical reasoning for intelligent systems. Students enhance their understanding of algorithms, data modeling, and applied sciences while developing interdisciplinary perspectives that merge computation with human and physical sciences.

Introduction to Computer Science and Programming 2

Builds upon basic programming to develop efficient algorithms and modular code.

Mathematics for Computer Science

Focuses on discrete mathematics, logic, and proofs essential for computer algorithms and computation.

Calculus 2

Extends differential and integral calculus to multivariable systems and complex applications.

Physics

Covers mechanics, electromagnetism, and thermodynamics to strengthen scientific grounding in computational systems.

Statistical Modeling

Teaches how to construct and interpret statistical models to analyze real-world data patterns.

Indian Health Sciences

Examines traditional health knowledge systems and their relation to technology, ethics, and data innovation.

Expository Writing

Develops clarity and precision in technical writing, research presentation, and academic communication.

ATLAS Electives

Offers creative, cross-disciplinary courses blending design, computation, and innovation.

Data Analyst Project (Pinnacle II)

Applies data analytics and visualization skills in a practical, research-based computing project.

Semester 3

Foundations of Machine Learning and Computational Intelligence

Semester 3 focuses on developing strong analytical and computational foundations through machine learning, data science, and algorithmic thinking. Students explore structured and unstructured data systems while applying mathematical models to real-world problem-solving, shaping their ability to design intelligent, data-driven solutions.

Machine Learning Foundations

Provides the mathematical foundation for AI, computer graphics, and machine learning algorithms.

Computer Organization and Architecture

Explores processor design, memory hierarchy, and instruction execution essential for computational efficiency.

Linear Algebra

Provides the mathematical foundation for AI, computer graphics, and machine learning algorithms.

Data Structures and Algorithms

Develops problem-solving efficiency through algorithmic design and data optimization techniques.

Fundamentals of Data Science and Analytics

Explains data cleaning, visualization, and analysis methods to derive actionable insights.

Model Thinking

Applies mathematical and conceptual models to understand patterns in data and complex systems.

Foundational Literature of Indian Civilization

Examines Indian philosophical and scientific thought, connecting heritage with modern innovation.

ATLAS Electives

Offers creative, cross-disciplinary courses blending design, computation, and innovation.

Machine Learning: Astrophysics, Particles, Drug Design (Pinnacle III)

Applies machine learning to advanced scientific domains through real-world research projects.

Semester 4

Applied Intelligence and System Integration

Semester 4 integrates advanced machine learning with core concepts of software engineering, databases, and IoT to create scalable AI systems. Students gain hands-on experience in data mining, system design, and enterprise-grade applications, bridging technical development with practical business innovation.

Big Data Analyst

Explores deep neural networks, reinforcement learning, and optimization techniques for complex AI systems.

Database Design and Management

Covers relational models, normalization, and data integrity for efficient and secure information storage.

Operating Systems and Networks

Focuses on process management, memory systems, and network protocols essential for system-level computing.

Data Mining & Warehousing

Introduces data extraction, transformation, and analytics for large-scale decision-making frameworks.

Software Engineering Principles

Covers design methodologies, version control, and testing for robust, maintainable software systems.

Embedded System and IoT

Examines microcontrollers, sensors, and connectivity protocols for real-time intelligent devices.

Business Plan Writing

Develops entrepreneurial thinking through strategic, financial, and innovation-driven project planning.

ATLAS Electives

Encourages interdisciplinary engagement with human-centered, creative, or ethical perspectives in AI.

An Enterprise Grade AI Application: Recommendation Engines for OTT Platforms (Pinnacle IV)

Applies machine learning to personalize user experiences and enhance data-driven media recommendations.

Semester 5

Intelligent Systems and Innovation

This semester advances skills in AI-driven automation through reinforcement learning, computer vision, and NLP. Students apply cloud and data analytics to real-world systems while fostering entrepreneurial thinking and developing innovative projects such as self-driving technologies.

Reinforcement Learning and NLP

Master the art of making big-picture decisions. Learn how to evaluate competition, allocate resources, and plan for long-term business success in volatile environments.

Computer Vision and Deep Learning

Focuses on enabling machines to interpret and process visual information from the world.

Image Processing

Covers enhancement, transformation, and segmentation of digital images for computational applications.

Data Analytics and Visualization

Teaches methods to interpret, visualize, and communicate complex datasets through modern analytical tools.

Cloud Application Development

Explores cloud infrastructure, APIs, and deployment pipelines for scalable AI-based applications.

Entrepreneurship Development

Encourages innovation through idea validation, resource planning, and tech-based business creation.

Cultural and Intellectual Heritage of English

Introduces global communication, critical thinking, and cultural perspectives in academic and professional contexts.

Enterprise Grade Connected Device Application: Self-Driving Cars (Pinnacle V)

Applies AI, sensor fusion, and machine learning to develop autonomous and intelligent mobility systems.

Semester 6

Advanced Data Intelligence and Systems Security

This semester focuses on integrating large-scale data analysis with secure AI systems and predictive modeling. Students explore multi-modal learning and advanced forecasting while applying cross-disciplinary insights through internships and foundational studies in science and computation.

Big Data Analytics

Covers distributed computing frameworks, large-scale data handling, and predictive insight generation.

Secure Coding

Teaches best practices to write robust, vulnerability-free software applications.

Data Science for Generic AI

Focuses on building adaptable AI models through data-driven reasoning, generalization, and automation.

Time Series and Forecasting

Explores trend analysis and temporal data prediction using statistical and machine learning models.

Multi-Modal Learning (Text + Vision + Speech) Analysis

Integrates diverse sensory data types to train AI systems for richer contextual understanding.

Language of Science and Technology (English)

Strengthens scientific writing, communication, and presentation within technological and research contexts.

Introduction to Indian Mathematics & Astronomy

Examines classical mathematical logic and astronomical innovation from an Indian scientific heritage perspective.

Summer Internship

Provides practical exposure to AI development, data research, or tech innovation in professional settings.

Semester 7

Research and Ethical Innovation in AI

This semester deepens analytical and computational rigor through Bayesian inference, quantum computing, and advanced electives. Students refine their research skills while engaging with ethical and governance frameworks to ensure fairness and responsibility in AI-driven systems.

Advanced Statistical Inference & Bayesian Analysis

Focuses on probabilistic reasoning, Bayesian modeling, and uncertainty quantification for predictive AI systems.

Quantum Computing & Algorithms

Introduces quantum logic, superposition, and algorithmic frameworks for high-performance computational problem solving.

Core Elective 1

Allows focused study in a chosen AI or computer science specialization area.

Core Elective 2

Expands interdisciplinary expertise through advanced application-based or theoretical exploration.

Core Elective 3

Encourages independent inquiry into emerging fields or niche AI technologies.

Data Ethics, Fairness, and Governance in AI Systems

Examines AI accountability, bias mitigation, and regulatory frameworks for equitable digital systems.

Research Methodology

Develops systematic research design, data collection, and analytical skills for academic or industrial innovation.

Semester 8

Applied AI Innovation and Human-Centric Research

The final semester brings together computing, design, and innovation through an internship and capstone project, allowing students to apply theory, gain practical experience, and prepare for careers, research, or entrepreneurship.