- Faculty
Faculty of Engineering and Technology
- Department
Department of Computer Applications
- Campus
Technology Campus (Peenya Campus)
- Engagement Mode
Full Time
Overview
The 2-year Master of Computer Applications (MCA) degree is in demand because it provides advanced, industry-relevant knowledge in areas like software development, data science, artificial intelligence, machine learning and cybersecurity, equipping graduates with specialized skills for high-paying roles in the tech sector. This shorter, intensive program allows students with foundational computer science knowledge to quickly upskill and pursue advanced career opportunities. Additionally, the MCA degree is recognized by employers across industries, as companies increasingly need skilled professionals to drive digital transformation and innovation. It offers the NEP-2020 aligned curriculum with focus on Industry 4.0. The program is approved by the All India Council for Technical Education (AICTE), New Delhi.
Program Objectives
- To provide a strong foundation in core areas of computer science such as programming, algorithms, data structures, databases, operating systems, and computer networks.
- To equip students with advanced knowledge in software development, application design, system integration, and emerging technologies such as AI, Data Science, and Cloud Computing.
- To foster analytical and problem-solving skills that can be applied to complex computing problems and interdisciplinary projects.
- To prepare students for employment in the IT industry, government, research, and entrepreneurship by integrating practical training, internships, and industry-oriented coursework.
- To encourage continuous learning and upskilling in response to evolving technology trends and career development needs.
- To develop a research-oriented mindset and the ability to contribute to the development of new computing techniques, tools, and methodologies.
Programme Educational Outcomes (PEOs)
- Professional Competence: To excel in computing careers by applying strong theoretical foundations, programming skills and modern software tools to solve real-world problems in diverse domains.
- Innovation and Life-long learning: To continually evolve as a technology professionals by engaging in life-long learning, pursuing higher education or research in advanced areas of computer applications and emerging technologies.
Programme Outcomes (POs)
- Apply knowledge of mathematics, natural science, computing, engineering fundamentals and an engineering specialization.
- Identify, formulate, review research literature and analyse complex engineering problems reaching substantiated conclusions with consideration for sustainable development.
- Design creative solutions for complex engineering problems and design / develop systems / components / processes to meet identified needs with consideration for the public health and safety, whole-life cost, net zero carbon, culture, society and environment as required.
- Conduct investigations of complex engineering problems using research-based knowledge including design of experiments, modelling, analysis and interpretation of data to provide valid conclusions.
- 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.
- Analyse and evaluate societal and environmental aspects while solving complex engineering problems for its impact on sustainability with reference to economy, health, safety, legal framework, culture and environment.
- Apply ethical principles and commit to professional ethics, human values, diversity and inclusion; adhere to national and international laws.
- Function effectively as an individual and as a member or leader in diverse / multi-disciplinary teams.
- Communicate effectively and inclusively within the engineering community and society at large, such as being able to comprehend and write effective reports and design documentation, make effective presentations considering cultural, language and learning differences.
- Apply knowledge and understanding of engineering management principles and economic decision-making and apply these to one’s own work, as a member and leader in a team, and to manage projects and in multidisciplinary environments.
- Recognize the need for and have the preparation and ability for (i) independent and life-long learning, (ii) adaptability to new and emerging technologies, and (iii) critical thinking in the broadest context of technological change.
Curriculum Details
| Sl. No. | Course Type | Course Name | Theory | Tutorials | Practical | Max Marks | Total Credits |
|---|---|---|---|---|---|---|---|
| 1 | PCC | Mathematical Foundation for Computer Applications | 3 | 1 | 0 | 100 | 4 |
| 2 | PCC | Operating Systems | 3 | 0 | 2 | 100 | 4 |
| 3 | PCC | Data Structures with Algorithms | 4 | 0 | 0 | 100 | 4 |
| 4 | PCC | Computer Networks | 3 | 0 | 0 | 100 | 3 |
| 5 | PCC | Design and Analysis of Algorithms | 3 | 1 | 0 | 100 | 4 |
| 6 | PCC | Data Structures Laboratory | 0 | 0 | 4 | 100 | 2 |
| 7 | PCC | Computer Networks Laboratory | 0 | 0 | 4 | 100 | 2 |
| 8 | PCC | Research Methodology and IPR | 2 | 0 | 0 | 100 | 2 |
| 9 | PCC | Programming in C & Computer Organisation | 2 | 2 | 0 | 100 | 0 |
| Total | 20 | 4 | 10 | 900 | 25 | ||
| Total number of contact hours per week: 34 | |||||||
| Sl. No. | Course Type | Course Name | Theory | Tutorials | Practical | Max Marks | Total Credits |
|---|---|---|---|---|---|---|---|
| 1 | PCC | Database Management System | 3 | 0 | 0 | 100 | 3 |
| 2 | PCC | Object Oriented Programming Using Java | 3 | 0 | 0 | 100 | 3 |
| 3 | PCC | Software Engineering | 4 | 0 | 0 | 100 | 4 |
| 4 | SEC | Full Stack Development | 3 | 0 | 2 | 100 | 4 |
| 5 | SEC | Artificial Intelligence and Machine Learning | 4 | 0 | 0 | 100 | 4 |
| 6 | PEC | Professional Elective-1: Distributed Computing / Cloud Computing | 4 | 0 | 0 | 100 | 4 |
| 7 | PCC | DBMS Laboratory | 0 | 0 | 4 | 100 | 2 |
| 8 | PCC | Java Programming Laboratory | 0 | 0 | 4 | 100 | 2 |
| 9 | PCC | Internship | 0 | 0 | 12 | 100 | 6 |
| Total | 21 | 0 | 22 | 900 | 32 | ||
| Total number of contact hours per week: 44 | |||||||
| Sl. No. | Course Type | Course Name | Theory | Tutorials | Practical | Max Marks | Total Credits |
|---|---|---|---|---|---|---|---|
| 1 | SEC | Data Analytics with Python | 3 | 0 | 0 | 100 | 3 |
| 2 | SEC | Internet of Things | 3 | 0 | 0 | 100 | 3 |
| 3 | PEC | Professional Elective-2: Deep Learning / Natural Language Processing | 4 | 0 | 0 | 100 | 4 |
| 4 | PEC | Professional Elective-3: Blockchain Technology / AI for Healthcare | 4 | 0 | 0 | 100 | 4 |
| 5 | SEC | User Interface Design | 4 | 0 | 0 | 100 | 4 |
| 6 | SEC | Cyber Security | 3 | 0 | 2 | 100 | 4 |
| 7 | SEC | Data Analytics Lab | 0 | 0 | 4 | 100 | 2 |
| 8 | SEC | IoT Laboratory | 0 | 0 | 4 | 100 | 2 |
| 9 | PCC | Project Work - Phase 1 | 0 | 0 | 4 | 100 | 2 |
| Total | 21 | 0 | 14 | 1000 | 28 | ||
| Total number of contact hours per week: 35 | |||||||
| Sl. No. | Course Type | Course Name | Theory | Tutorials | Practical | Max Marks | Total Credits |
|---|---|---|---|---|---|---|---|
| 1 | PCC | Project Work - Phase 2 | 0 | 0 | 40 | 200 | 20 |
| Total number of contact hours per week: 40 | |||||||
| Sl. No. | Course Name |
|---|---|
| 1 | Distributed Computing |
| 2 | Cloud Computing |
| 3 | Deep Learning |
| 4 | Natural Language Processing |
| 5 | Blockchain Technology |
| 6 | AI for Healthcare |
Eligibility Criteria
- Graduates with a bachelor's degree from an recognized institution in any BCA/ Bachelor Degree in Computer Science or Engineering or equivalent degree or B.Sc/B Com/BA with Mathematics at 10+2 level or at Graduation level (with additional bridge courses as per the norms of concerned University and a minimum aggregate score of 50%.
- Admission selection process for will be based on RUAS AT scores, and CUET-PG/KMAT/PGCET scores are also accepted and RUAS AT will be exempted for these candidates.
Structure
Fee Structure
| Course | Total Fee Per Year |
|---|---|
| Master of Computer Application (MCA) | 2,30,000 |
Intake
120
Career Path
The two years MCA program is well-accepted and recognized by the industry for its various roles like
- Software Developer
- Data Scientist
- Machine Learning (ML) Specialist
- Artificial Intelligence (AI) Engineer
- Cloud Architect
- Web Developer
- Software Engineer
- Business Analyst
- Network Engineer
- Quality Assurance Professional
- Application Developer
- Cyber Security Analyst and Test Engineer
ADDRESS
