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Duration

4 Years

Computer Applications

Alakh Prakash Goyal Shimla University Shimla
Duration
4 Years
Computer Applications UG OFFLINE

Duration

4 Years

Computer Applications

Alakh Prakash Goyal Shimla University Shimla
Duration
Apply

Fees

N/A

Placement

92.0%

Avg Package

₹4,50,000

Highest Package

₹8,00,000

OverviewAdmissionsCurriculumFeesPlacements
4 Years
Computer Applications
UG
OFFLINE

Fees

N/A

Placement

92.0%

Avg Package

₹4,50,000

Highest Package

₹8,00,000

Seats

N/A

Students

N/A

ApplyCollege

Seats

N/A

Students

N/A

Curriculum

Curriculum Overview

The Computer Applications curriculum at Alakh Prakash Goyal Shimla University Shimla is meticulously structured to provide a holistic blend of theoretical knowledge and practical application. It spans eight semesters, integrating core technical subjects, departmental electives, science electives, and laboratory-based learning experiences.

SemesterCourse CodeCourse TitleCredit Structure (L-T-P-C)Prerequisites
1CS101Introduction to Programming3-0-2-4None
1CS102Mathematics I3-0-2-4None
1CS103Computer Fundamentals3-0-2-4None
1CS104English for Technical Communication3-0-2-4None
1CS105Introduction to Data Structures3-0-2-4CS101
1CS106Lab: Programming Fundamentals0-0-4-2CS101
2CS201Data Structures & Algorithms3-0-2-4CS105
2CS202Mathematics II3-0-2-4CS102
2CS203Digital Logic Design3-0-2-4None
2CS204Object-Oriented Programming3-0-2-4CS101
2CS205Database Management Systems3-0-2-4CS105
2CS206Lab: Data Structures & Algorithms0-0-4-2CS201
3CS301Operating Systems3-0-2-4CS203
3CS302Software Engineering3-0-2-4CS204
3CS303Computer Networks3-0-2-4CS203
3CS304Mathematics III3-0-2-4CS202
3CS305Web Technologies3-0-2-4CS204
3CS306Lab: Software Engineering0-0-4-2CS302
4CS401Artificial Intelligence3-0-2-4CS301
4CS402Cybersecurity Fundamentals3-0-2-4CS303
4CS403Data Science3-0-2-4CS205
4CS404Mobile Application Development3-0-2-4CS205
4CS405Project Management3-0-2-4None
4CS406Lab: Mobile App Development0-0-4-2CS404
5CS501Machine Learning3-0-2-4CS401
5CS502Advanced Cybersecurity3-0-2-4CS402
5CS503Big Data Analytics3-0-2-4CS403
5CS504Human-Computer Interaction3-0-2-4CS305
5CS505Internet of Things3-0-2-4CS303
5CS506Lab: IoT Applications0-0-4-2CS505
6CS601Deep Learning3-0-2-4CS501
6CS602Cloud Computing3-0-2-4CS303
6CS603Financial Engineering3-0-2-4CS403
6CS604DevOps Practices3-0-2-4CS302
6CS605Research Methodology3-0-2-4None
6CS606Lab: DevOps Implementation0-0-4-2CS604
7CS701Capstone Project I3-0-2-4CS501, CS502, CS503
7CS702Advanced Algorithms3-0-2-4CS201
7CS703Quantitative Risk Management3-0-2-4CS603
7CS704Entrepreneurship in Tech3-0-2-4None
7CS705Internship0-0-8-0CS701
7CS706Lab: Capstone Project I0-0-4-2CS701
8CS801Capstone Project II3-0-2-4CS701
8CS802Advanced Topics in AI3-0-2-4CS601
8CS803Ethical Implications of Technology3-0-2-4None
8CS804Leadership in Tech3-0-2-4None
8CS805Final Presentation0-0-4-2CS801
8CS806Lab: Final Capstone Project0-0-4-2CS801

Advanced Departmental Elective Courses:

  • Introduction to Artificial Intelligence: This course introduces students to fundamental concepts of AI including search algorithms, knowledge representation, reasoning systems, and machine learning basics. Students will gain hands-on experience with popular frameworks like TensorFlow and PyTorch.
  • Advanced Machine Learning: Focuses on advanced topics in ML such as deep neural networks, reinforcement learning, ensemble methods, and transfer learning. Students will implement complex models using Python and build end-to-end ML pipelines.
  • Cybersecurity and Ethical Hacking: Covers network security protocols, cryptography, penetration testing, digital forensics, and ethical hacking techniques. Includes lab sessions on tools like Kali Linux, Wireshark, and Metasploit.
  • Data Science and Analytics: Students learn statistical analysis, data visualization, predictive modeling, and big data technologies like Hadoop and Spark. Emphasis is placed on applying these concepts to real-world datasets.
  • Software Testing and Quality Assurance: Explores various testing methodologies, automation tools, test design techniques, and quality assurance practices in software development lifecycle.
  • Cloud Computing Technologies: Introduces cloud architectures, virtualization, distributed systems, and services offered by major providers like AWS, Azure, and GCP. Students will deploy applications on cloud platforms using DevOps practices.
  • Internet of Things (IoT) Applications: Examines IoT architecture, sensor networks, embedded systems, communication protocols, and smart city solutions. Hands-on labs involve building IoT projects using Raspberry Pi and Arduino boards.
  • Mobile App Development: Covers both native and cross-platform mobile app development using frameworks like React Native, Flutter, and Xamarin. Students will develop apps for Android and iOS platforms.
  • Human-Computer Interaction: Focuses on user-centered design principles, usability evaluation techniques, prototyping tools, and accessibility standards. Includes projects involving interaction design and user testing.
  • DevOps Practices: Introduces continuous integration, deployment automation, containerization with Docker, orchestration with Kubernetes, and monitoring systems for scalable software delivery.

Project-Based Learning Philosophy

Our department believes that project-based learning is crucial for developing practical skills and fostering innovation among students. The curriculum incorporates both mini-projects and a final-year capstone project to ensure students apply theoretical knowledge in real-world scenarios.

Mini Projects: These are smaller-scale projects undertaken during the second and third years of study. They focus on specific technical domains such as web development, database design, algorithm implementation, or software testing. Each mini-project is assigned a mentor from faculty and evaluated based on project documentation, presentation quality, and code clarity.

Final-Year Capstone Project: This comprehensive project spans the entire eighth semester and serves as the culmination of the undergraduate experience. Students form teams to work on a significant technological challenge related to their chosen specialization track. The project must demonstrate advanced technical proficiency, innovation, and practical relevance.

Project Selection Process: Students are encouraged to propose projects aligned with their interests or identified by faculty members. Proposals undergo review by the departmental advisory committee to ensure feasibility, relevance, and academic rigor. Once approved, students receive mentorship from experienced faculty members who guide them through the implementation phase.

Evaluation Criteria: Projects are assessed based on several factors including technical execution, innovation, documentation quality, presentation effectiveness, teamwork, and adherence to deadlines. Peer reviews, faculty evaluations, and external assessments by industry experts may also be incorporated into the grading process.