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M-SC in Computer Science at University of Delhi
Delhi, Delhi
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About the Specialization
What is Computer Science at University of Delhi Delhi?
This M.Sc. Computer Science program at University of Delhi focuses on advanced theoretical knowledge and practical skills essential for modern computing. It prepares students for cutting-edge roles in the Indian IT sector, emphasizing strong foundational principles and emerging technologies. The curriculum is designed to meet the evolving demands of industry and research in India.
Who Should Apply?
This program is ideal for Bachelor''''s graduates in Computer Science, IT, or related engineering disciplines seeking to deepen their expertise. It suits fresh graduates aiming for high-demand entry-level positions and working professionals looking to upskill in advanced computing areas. Aspirants passionate about research, software development, and data science will thrive here.
Why Choose This Course?
Graduates of this program can expect promising career paths in software development, data analytics, AI/ML engineering, and cybersecurity within Indian companies and MNCs. Entry-level salaries typically range from INR 6-12 LPA, with significant growth potential. The program aligns with industry needs, fostering skills for leadership and innovation in the rapidly expanding digital economy.
Student Success Practices
Foundation Stage
Master Core Programming & Data Structures- (Semester 1-2)
Dedicate consistent time to practice coding problems in C++/Java. Focus on understanding and implementing advanced data structures and algorithms, which form the backbone of competitive programming and industry interviews. Engage in daily problem-solving sessions.
Tools & Resources
GeeksforGeeks, HackerRank, LeetCode, NPTEL courses on Algorithms
Career Connection
Strong fundamentals are crucial for cracking technical interviews at top Indian IT companies and for building robust software systems in your career.
Build a Strong Academic Network- (Semester 1-2)
Actively participate in departmental seminars, workshops, and study groups. Connect with professors for mentorship and peer students for collaborative learning. Discuss concepts, share notes, and engage in constructive academic debates to deepen understanding.
Tools & Resources
Departmental forums, LinkedIn for academic connections, University library resources
Career Connection
Networking opens doors to research opportunities, project collaborations, and often leads to valuable referrals for internships and placements in India.
Develop Excellent Documentation Skills- (Semester 1-2)
Practice writing clear, concise, and technically accurate project reports, lab manuals, and research summaries. Pay attention to proper formatting, citation, and logical flow. This includes commenting code thoroughly and maintaining version control for projects.
Tools & Resources
LaTeX, GitHub/GitLab, Microsoft Word/Google Docs
Career Connection
Effective documentation is vital for clear communication in team environments and for presenting your work professionally to future employers and academic bodies.
Intermediate Stage
Engage in Practical Project Development- (Semester 3-4)
Beyond lab assignments, identify real-world problems and develop mini-projects or contribute to open-source initiatives. Apply concepts learned in DBMS, AI, and Machine Learning to build functional applications. Seek faculty guidance for project ideas.
Tools & Resources
Python/Java frameworks (e.g., Django, Spring Boot), GitHub, Kaggle for datasets
Career Connection
Practical projects demonstrate your problem-solving abilities and technical proficiency, significantly boosting your resume for Indian tech companies and startups.
Seek Internships and Industry Exposure- (Semester 3-4)
Actively search for summer internships or part-time roles in your area of interest (e.g., data science, software development, AI). These experiences provide invaluable insights into industry practices, professional work culture, and networking opportunities within India.
Tools & Resources
Internshala, Naukri.com, LinkedIn Jobs, University placement cell
Career Connection
Internships are often a direct path to pre-placement offers (PPOs) and provide a competitive edge in securing full-time employment after graduation.
Participate in Coding Competitions & Hackathons- (Semester 3-4)
Regularly participate in online coding contests and hackathons organized by colleges or companies. This hones your rapid problem-solving skills, teamwork, and ability to work under pressure, which are highly valued in the Indian tech industry.
Tools & Resources
CodeChef, HackerEarth, Google Code Jam, Local tech meetups
Career Connection
Winning or performing well in these events can directly lead to interview calls and recognition from top tech recruiters in India.
Advanced Stage
Undertake a Comprehensive Major Project/Dissertation- (Semester 4)
Choose a challenging project that aligns with your career aspirations and showcases advanced skills. Conduct thorough research, implement a robust solution, and rigorously evaluate its performance. Aim for a publication or a deployable product.
Tools & Resources
Research papers (IEEE, ACM), Advanced IDEs, Cloud platforms (AWS, Azure, GCP), Open-source libraries
Career Connection
A strong major project demonstrates expertise and innovation, making you highly attractive to employers, especially for R&D or specialized engineering roles in India.
Focus on Specialized Skill Development & Certifications- (Semester 4)
Based on your elective choices and career goals, acquire specific certifications in areas like Cloud Computing, Deep Learning, Cybersecurity, or Data Engineering. These validate your skills and make you more competitive in the Indian job market.
Tools & Resources
Coursera, edX, Udemy, Official certification bodies (AWS, Azure, Google Cloud, Cisco)
Career Connection
Certifications signal to recruiters that you possess job-ready skills and are committed to continuous learning, directly improving your placement prospects.
Prepare Rigorously for Placements & Interviews- (Semester 4)
Start preparing for technical, aptitude, and HR rounds well in advance. Practice mock interviews, review core CS concepts, and stay updated on current industry trends. Tailor your resume and cover letter for specific job roles.
Tools & Resources
Glassdoor, Mock interview platforms, Campus placement cell workshops, Professional mentors
Career Connection
Thorough preparation is paramount for securing desirable job offers from top-tier companies during campus placements or off-campus recruitment drives across India.
Program Structure and Curriculum
Eligibility:
- B.Sc. (Hons.) Computer Science/B.Sc. (Physical Sciences/Mathematical Sciences) with Computer Science/B.Tech./B.E. (Computer Science/IT/Electronics/ECE)/MCA/M.Sc. (Mathematics/Statistics/Operational Research) with an undergraduate degree in Computer Science or equivalent, with a minimum of 60% marks or equivalent CGPA.
Duration: 2 years / 4 semesters
Credits: 80 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCS 101 | Advanced Data Structures | Core | 4 | Analysis of Algorithms, Trees and Heaps, Graphs and Graph Algorithms, Hashing Techniques, Disjoint Set Union, Amortized Analysis |
| MCS 102 | Object Oriented Programming | Core | 4 | Object-Oriented Concepts, Classes, Objects, Methods, Inheritance and Polymorphism, Abstraction and Encapsulation, Exception Handling, Templates and Collections |
| MCS 103 | Design and Analysis of Algorithms | Core | 4 | Asymptotic Notations, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms, NP-Completeness |
| MCS 104 | Computer Networks | Core | 4 | OSI and TCP/IP Models, Network Topologies and Devices, Data Link Layer Protocols, Network Layer (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols |
| MCS 105 | Advanced Data Structures Lab | Lab | 2 | Implementation of Trees, Implementation of Graphs, Hashing techniques, Sorting Algorithms, Shortest Path Algorithms, Minimum Spanning Trees |
| MCS 106 | Object Oriented Programming Lab | Lab | 2 | Class and Object Creation, Inheritance and Polymorphism, Abstract Classes and Interfaces, File I/O Operations, Exception Handling, Generics and Collections |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCS 201 | Advanced Database Management Systems | Core | 4 | Relational Model and SQL, ER Modeling and Normalization, Transaction Management, Concurrency Control, Query Processing and Optimization, Distributed Databases |
| MCS 202 | Advanced Operating Systems | Core | 4 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, Distributed Operating Systems |
| MCS 203 | Artificial Intelligence | Core | 4 | Intelligent Agents, Search Algorithms (DFS, BFS, A*), Game Playing, Knowledge Representation, Logical Reasoning, Uncertainty and Probabilistic Reasoning |
| MCS 204 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression and Classification, Clustering Algorithms, Neural Networks Basics, Model Evaluation and Validation |
| MCS 205 | DBMS Lab | Lab | 2 | SQL Queries, PL/SQL Programming, Database Design, Transaction Implementation, Indexing and Views, Report Generation |
| MCS 206 | AI & ML Lab | Lab | 2 | Implementing Search Algorithms, Logic Programming (Prolog), Classification Algorithms, Clustering Algorithms, Neural Network Training, Using ML Libraries (Scikit-learn) |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCS 301 | Advanced Software Engineering | Core | 4 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing Strategies, Software Project Management, Agile and DevOps Methodologies |
| MCS 302 | Compiler Design | Core | 4 | Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization, Target Code Generation |
| MCS E1 | Elective I: Data Mining (Example) | Elective | 4 | Data Warehousing Concepts, OLAP and Data Cubes, Association Rule Mining, Classification Techniques, Clustering Algorithms, Anomaly Detection |
| MCS E2 | Elective II: Big Data Analytics (Example) | Elective | 4 | Introduction to Big Data, Hadoop Ecosystem, MapReduce Framework, Spark and Data Processing, NoSQL Databases, Data Stream Mining |
| MCS 303 | Elective Lab I (Based on E1/E2) | Lab | 2 | Data Preprocessing, Implementing Classification Models, Clustering Analysis, Hadoop/Spark Programming, NoSQL Database Operations, Data Visualization |
| MCS 304 | Minor Project / Dissertation Part I | Project | 4 | Problem Identification, Literature Review, Methodology Design, Data Collection Planning, Preliminary Analysis, Project Proposal Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCS 401 | Major Project / Dissertation | Project | 8 | System Design and Architecture, Implementation and Coding, Testing and Debugging, Performance Evaluation, Report Writing and Documentation, Project Presentation and Viva |
| MCS E3 | Elective III: Deep Learning (Example) | Elective | 4 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Reinforcement Learning, Transfer Learning |
| MCS E4 | Elective IV: Cloud Computing (Example) | Elective | 4 | Cloud Computing Paradigms, Virtualization Technologies, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Cloud Security Challenges, Serverless Computing |
