Master of computer application (MCA)
Specialization: Artificial Intelligence
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& Powered By
Course Highlights
Introduction
The Master of Computer Application (MCA) with Specialization in Artificial Intelligence (AI) is a cutting-edge, postgraduate program designed to equip students with advanced computing skills and a deep understanding of AI-driven technologies. This 2-year program blends core principles of computer science with emerging fields like machine learning, deep learning, natural language processing, and intelligent systems.
The curriculum emphasizes both theoretical knowledge and hands-on experience, preparing students to develop smart applications, automate complex tasks, and drive innovation in domains such as healthcare, finance, cybersecurity, robotics, and beyond. Backed by industry leaders like Intel and NASSCOM, the program ensures that graduates are aligned with the latest trends, tools, and ethical practices in AI.
Whether aiming for industry roles, research, or entrepreneurship, this
specialization empowers students to become AI professionals capable of solving
real-world problems and contributing meaningfully to the digital future.
Eligibility
Passed any graduation degree (e.g.: B.E./ B.Tech./B.Sc/ B.Com. /
B.A./ B. Voc./ BCA etc.,) preferably with Mathematics at 10+2 level or at Graduation level.
Obtained at least 50% marks (45% marks in case of candidates belonging to reserved category) in the qualifying examination.
(For students having no Mathematics background must undergo a compulsory bridge course on Mathematics & related
Duration
2 Years Full-Time
Tuition Fees
Rs. 62,700 per Semester
*Fees such as Admission, Caution Money, Examination, Hostel, and Transport fees are extra.
Program Details
Programming Fundamentals
Master coding skills using C, C++ , Python, and Java with a focus on logic building and algorithm design
Data Structures and Algorithms
Efficient organization, processing, and optimization of data using real- time algorithmic techniques.
Operating Systems
Learn process management, memory handling, system calls, and OS-level architecture.
Database Management Systems (DBMS)
Study relational database models, SQL, PL/SQL, normalization, triggers, and transactions
Computer Networks
Understand network architecture, OSI/TCP-IP models, protocols, IP addressing, and socket programming
Software Engineering
Cover SDLC, software design patterns, agile methodologies, version control (Git), and testing strategies.
Object-Oriented Programming (OOP)
Build scalable systems using Java or C++ with concepts like inheritance, encapsulation, polymorphism, and interfaces
Artificial Intelligence (AI) Fundamentals
Introduction to intelligent agents, state-space search, knowledge representation, and rule-based systems
Machine Learning
Supervised & unsupervised learning, classification, regression, clustering, decision trees, SVMs, and model evaluation.
Deep Learning
Explore artificial neural networks, CNNs, RNNs, and implement models using TensorFlow or PyTorch.
Natural Language Processing (NLP)
Learn tokenization, POS tagging, sentiment analysis, named entity recognition, and language models.
Computer Vision
Work on image recognition, face detection, object tracking, and visual data processing.
Big Data & Analytics
Study data ingestion, preprocessing, and analysis using Hadoop, Spark, and Python libraries (Pandas, NumPy, Matplotlib).
Cloud Computing for AI
Deploy AI models on cloud platforms (AWS, Azure, GCP), work with Docker/VirtualBox, CI/CD tools.
Cybersecurity Essentials
Learn about network security, authentication protocols, encryption, ethical hacking, and cybersecurity frameworks.
Ethics in Artificial Intelligence
Study legal, ethical, and societal impacts of AI including fairness, bias, accountability, and transparency.
Capstone Project / Industry Internship
Develop an end-to-end AI solution using real-world datasets, tools, and cloud platforms in collaboration with industry
Program Outcome
Apply Programming Skills
Use programming languages to develop AI-powered applications and services
Understand AI Techniques
Apply machine learning, deep learning, and data mining techniques to solve practical problems.
Design Intelligent Systems
Create smart systems that can learn, adapt, and make autonomous decisions.
Data-Driven Decision Making
Use AI and analytics tools to extract meaningful insights from complex datasets.
Deploy Scalable Solutions
Implement and deploy AI models on cloud or edge environments with performance optimization.
Ethical Computing
Address ethical concerns related to bias, transparency, and accountability in AI.
Research and Innovation
Contribute to research,
innovation, and development of
cutting-edge AI technologies.
Lifelong Learning
Stay updated with evolving tools,
technologies, and frameworks in
AI and software.
Placement Opportunities
AI Engineer
Design and develop intelligent systems
that mimic human decision-making
using AI techniques
Machine Learning Engineer
Build predictive models using supervised and unsupervised learning for automation and analysis.
Data Scientist
Extract meaningful insights from large datasets using statistical and ML techniques.
Data Analyst
Use tools like Excel, Python, SQL, and visualization software to interpret and present data patterns
NLP Engineer
Develop systems that understand and generate human language (e.g., chatbots, voice assistants).
Computer Vision Engineer
Work on image processing, object detection, face recognition, and video intelligence.
Big Data Engineer
Design systems to process and manage large-scale datasets using Hadoop, Spark, and NoSQL databases
Cloud Engineer
Deploy and manage AI services and applications on cloud platforms like AWS, Azure, and GCP.
DevOps Engineer
Automate software development
pipelines, monitor deployments, and
ensure system reliability
Cybersecurity Analyst
Protect digital infrastructure by identifying vulnerabilities and responding to threats.
Ethical Hacker
Test system defenses and identify security weaknesses through authorized penetration testing.
Software Developer
Create desktop, mobile, or web
applications using modern programming languages and frameworks
Full Stack Developer
Work on both frontend and backend development to build complete web applications
Backend Developer
Specialize in server-side logic, databases, and API development.
Frontend Developer
Build visually appealing and interactive user interfaces for web and mobile platforms.
Android/iOS App Developer
Design and develop mobile applications using tools like Android Studio, Flutter, or Swift.
Game Developer
Create engaging 2D/3D games using engines like Unity and Unreal with AI for game logic.
AR/VR Developer
Develop augmented and virtual reality experiences for gaming, education, or training apps.
System Analyst
Analyze system requirements and coordinate between development teams and business needs
Technical Officer (Govt. Sector)
Manage and implement tech solutions for public sector enterprises (e.g., DRDO, ISRO, NIC).
Research Associate
Contribute to AI-focused research projects in academia or R&D labs.
Academic Lecturer
Teach computer science and AI subjects in colleges or universities after qualifying relevant exams.
AI Product Manager
Manage the lifecycle of AI-powered
products, bridging technical and business teams.
Freelance AI/ML Developer
Work independently on AI projects for clients via platforms like Upwork, Fiverr, and Freelancer.
Startup Founder / Tech Entrepreneur
Launch innovative AI-based products or services with support from incubation cells and accelerators
Lab List
1.Programming Lab
C, C++, Python, Java: Basic and advanced coding skills.
2.Data Structures Lab
Implementation of stacks, queues, trees, graphs, sorting, and searching algorithms.
3.Database Management Systems (DBMS) Lab
SQL, PL/SQL, database design, normalization, triggers, and transactions
4.Operating Systems Lab
Process management, scheduling algorithms, memory management, file systems
5.Computer Networks Lab
Socket programming, protocol simulation, packet tracing (e.g.,
Wireshark, Cisco Packet Tracer)
6.Software Engineering Lab
Software design, UML diagrams, testing strategies, version control tools like Git
7.Object-Oriented Programming Lab
Java/C++: Concepts like inheritance, polymorphism, interfaces, and GUI apps
8.Web Technologies Lab
HTML, CSS, JavaScript, PHP , Node.js, basic web app development
9.Compiler Design Lab
Lexical analyzer, parser, intermediate code generation using Lex & Yacc
10.Artificial Intelligence Lab
Search algorithms, logic programming, basic ML models in Python
11.Cybersecurity Lab
Kali Linux, penetration testing tools, basic cryptography, and web app security
12.Cloud Computing Lab
AWS/Azure basics, virtualization using VirtualBox/Docker, deployment of apps
13.Mobile App Development Lab
Android Studio or Flutter for building mobile apps
14.Machine Learning Lab (Optional/Advanced)
Scikit-learn, TensorFlow, or PyTorch for supervised/unsupervised learning
15.Project Lab / Capstone Lab
Final year project work with version control, documentation, and testing
Where Talent Meets Opportunity:
Our Graduates Are Building Futures with Leading Companies
