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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 the University of Delhi provides a robust foundation in advanced computing principles and cutting-edge technologies. Designed to meet the growing demands of the Indian IT industry, it emphasizes theoretical depth and practical application. The program offers diverse specializations to equip students for innovation and research within the dynamic tech landscape.
Who Should Apply?
This program is ideal for Bachelor''''s degree holders in Computer Science, BCA, or B.Tech./B.E. in relevant fields, aspiring to excel in advanced computing roles. It suits fresh graduates seeking entry into R&D, software development, data science, or cybersecurity, as well as working professionals aiming to upskill or transition into specialized technical domains.
Why Choose This Course?
Graduates of this program can expect promising career paths in leading Indian and multinational companies as Data Scientists, AI/ML Engineers, Cybersecurity Analysts, Cloud Architects, and Research Associates. Entry-level salaries typically range from INR 6-12 LPA, with significant growth trajectories. The curriculum aligns with industry certifications in AI, Data Science, and Cloud platforms.
Student Success Practices
Foundation Stage
Strengthen Core Programming and Data Structures- (Semester 1-2)
Dedicate consistent time to mastering advanced data structures and algorithms. Participate in coding competitions to hone problem-solving skills and learn from diverse approaches. Focus on understanding the theoretical underpinnings of computer architecture and operating systems.
Tools & Resources
GeeksforGeeks, HackerRank, LeetCode, NPTEL courses on Algorithms and OS
Career Connection
A strong foundation is crucial for cracking technical interviews at top Indian tech companies and excelling in early-career development roles.
Cultivate Mathematical & Statistical Acumen- (Semester 1-2)
Actively engage with courses in probability and statistical computing. Utilize online platforms for practice problems and real-world case studies to apply statistical concepts in computational contexts. This builds a critical base for machine learning and data science.
Tools & Resources
Khan Academy Statistics, Coursera/edX courses on Probability & Statistics, R/Python for statistical analysis
Career Connection
Essential for roles in Data Science, AI, and Quantitative Analysis, where understanding data patterns and model validity is paramount.
Develop Technical Communication Skills- (undefined)
Actively participate in technical writing and presentation courses. Practice documenting code, writing project reports, and delivering clear presentations. Join university clubs focused on public speaking and technical communication to enhance soft skills.
Tools & Resources
Grammarly, LaTeX, Toastmasters International (local chapters), University Writing Center
Career Connection
Effective communication is vital for collaborating in teams, presenting project outcomes, and writing impactful research papers, improving visibility and career progression.
Intermediate Stage
Gain Hands-on Experience with Machine Learning Frameworks- (Semester 3-4)
Beyond theoretical understanding, implement machine learning and deep learning algorithms using popular libraries. Work on mini-projects to apply concepts to real datasets. Participate in hackathons focused on AI/ML applications.
Tools & Resources
TensorFlow, PyTorch, Scikit-learn, Kaggle, Google Colab
Career Connection
Directly enhances employability for AI/ML Engineer, Data Scientist, and Research roles in various Indian industries from finance to healthcare.
Explore Electives for Specialization and Industry Relevance- (Semester 3-4)
Strategically choose discipline-specific electives (DSEs) that align with career aspirations, whether it''''s Cloud Computing, Cyber Security, or NLP. Deep-dive into practical aspects of these domains through certifications and projects.
Tools & Resources
AWS/Azure/GCP certifications, NIST Cybersecurity Framework, Open-source projects in chosen domain
Career Connection
Helps in developing a niche expertise highly sought after by Indian companies and MNCs for specialized technical roles and consultancies.
Engage in Research Projects and Publications- (undefined)
Proactively seek out research opportunities with faculty or through self-initiated projects. Aim to contribute to research papers, even at student conferences or workshops. This builds critical thinking and research aptitude.
Tools & Resources
arXiv, Google Scholar, IEEE Xplore, ACM Digital Library, University Research Mentorship Program
Career Connection
Essential for pursuing higher education (PhD) or R&D roles in corporate or academic settings, giving a competitive edge in a research-driven economy.
Advanced Stage
Focus on Comprehensive Placement Preparation- (Semester 3-4)
Initiate rigorous preparation for placements by practicing aptitude, logical reasoning, and domain-specific technical questions. Attend campus placement workshops, mock interviews, and resume building sessions. Network with alumni for insights.
Tools & Resources
Placement cells, Mentors, LinkedIn, Glassdoor, InterviewBit
Career Connection
Directly impacts securing desired job roles with competitive packages in Indian and international companies, ensuring a smooth transition into the professional world.
Undertake an Industry-Relevant Dissertation/Project- (Semester 3-4)
Choose a dissertation topic that solves a real-world problem or has significant industry application. Focus on delivering a demonstrable output, potentially in collaboration with an industry partner. This showcases applied skills.
Tools & Resources
Industry collaboration opportunities, University innovation labs, Open-source contributions
Career Connection
A strong project demonstrates practical capabilities to employers, enhancing portfolio and securing high-value job roles or entrepreneurship opportunities in India.
Build a Professional Network and Personal Brand- (undefined)
Actively network with peers, faculty, industry professionals, and alumni through conferences, workshops, and online platforms. Maintain an updated LinkedIn profile and contribute to relevant online communities to establish your professional identity.
Tools & Resources
LinkedIn, GitHub, Professional conferences (e.g., Data Science Congress, India AI), University Alumni Network
Career Connection
Opens doors to job referrals, mentorship, and collaborative opportunities, which are invaluable for long-term career growth and leadership roles in India''''s competitive job market.
Program Structure and Curriculum
Eligibility:
- Bachelor''''s degree in Computer Science/BCA/BIT/B.Tech./B.E. in Computer Science & Engineering/Information Technology or an equivalent degree from a recognized University/Institution with a minimum of 50% marks or equivalent CGPA. Admission through CUET (PG).
Duration: 2 years (4 semesters)
Credits: 94 (Minimum for degree award) Credits
Assessment: Internal: 30% (for theory courses), External: 70% (for theory courses)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC101 | Advanced Data Structures | Core | 4 | Advanced Trees (AVL, Red-Black, B-trees), Heaps and Priority Queues, Hash Tables and Hashing Techniques, Graph Algorithms (BFS, DFS, Shortest Paths), Disjoint Set Structures, Amortized Analysis |
| DSC102 | Advanced Computer Architecture | Core | 4 | Pipelining and Instruction Level Parallelism, Memory Hierarchy Design, Cache Coherence Protocols, Multiprocessors and Parallel Architectures, Vector Processors, GPUs and CUDA |
| DSC103 | Advanced Algorithms | Core | 4 | Algorithm Analysis and Complexity, Divide and Conquer, Dynamic Programming, Greedy Algorithms and Backtracking, Graph Algorithms (Flow Networks, Matching), NP-Completeness and Approximation Algorithms |
| DSC104 | Probability and Statistical Computing | Core | 4 | Probability Distributions (Discrete & Continuous), Hypothesis Testing and Confidence Intervals, Regression Analysis (Linear, Logistic), ANOVA and Chi-Square Tests, Statistical Modeling for Machine Learning |
| AEC101 | Technical Writing and Presentation Skills | Ability Enhancement Course | 2 | Structure of Technical Reports and Papers, Research Proposal Writing, Effective Oral Presentation Techniques, Academic Honesty and Plagiarism, Literature Review and Referencing Styles |
| GE101-A | Python Programming | Generic Elective | 4 | Python Syntax and Data Types, Control Structures and Functions, Object-Oriented Programming in Python, File Handling and Modules, Data Structures (Lists, Tuples, Dictionaries, Sets) |
| GE101-B | Operating System Concepts | Generic Elective | 4 | Process Management and Scheduling, Memory Management Techniques, File Systems and I/O Management, Deadlocks and Concurrency Control, Virtualization Basics |
| GE101-C | Computer Networks | Generic Elective | 4 | OSI and TCP/IP Reference Models, Addressing and Routing Protocols, Transport Layer Services (TCP, UDP), Application Layer Protocols (HTTP, DNS), Network Security Fundamentals |
| GE101-D | Database Management Systems | Generic Elective | 4 | ER Modeling and Relational Model, SQL Queries and Joins, Normalization and Dependency Theory, Transaction Management and Concurrency Control, Database Security and Recovery |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC201 | Operating Systems | Core | 4 | Process Synchronization and Deadlocks, Memory Management (Paging, Segmentation), Virtual Memory and Page Replacement, File System Implementation and Allocation, I/O Management and Disk Scheduling |
| DSC202 | Object Oriented Software Engineering | Core | 4 | Software Development Life Cycle Models, UML Diagrams and Object-Oriented Design, Software Requirements Specification, Design Patterns and Architectural Styles, Software Testing Techniques and Quality Assurance |
| DSC203 | Design and Analysis of Computer Networks | Core | 4 | Network Topologies and Protocols, Routing Algorithms (Distance Vector, Link State), Congestion Control in TCP, Wireless and Mobile Networks, Network Security Measures and Standards |
| DSC204 | Machine Learning | Core | 4 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Neural Networks Fundamentals, Ensemble Methods (Bagging, Boosting), Model Evaluation and Hyperparameter Tuning |
| SEC201 | Introduction to R-Programming | Skill Enhancement Course | 2 | R Environment and Basics, Data Structures in R (Vectors, Lists, Data Frames), Data Manipulation with dplyr, Statistical Graphics with ggplot2, Writing Functions and Packages in R |
| DSE2XX-1 | Compiler Design | Discipline Specific Elective | 4 | Lexical Analysis and Finite Automata, Parsing Techniques (LL, LR, Recursive Descent), Syntax Directed Translation, Intermediate Code Generation, Code Optimization and Target Code Generation |
| DSE2XX-2 | Theory of Computation | Discipline Specific Elective | 4 | Finite Automata and Regular Expressions, Context-Free Grammars and Pushdown Automata, Turing Machines and Computability, Decidability and Undecidability, Complexity Classes (P, NP, NP-Complete) |
| DSE2XX-3 | Advanced Java Programming | Discipline Specific Elective | 4 | Generics and Collections Framework, Multithreading and Concurrency, JDBC for Database Connectivity, Networking with Sockets, Web Applications with Servlets and JSP |
| DSE2XX-4 | Advanced .NET Technologies | Discipline Specific Elective | 4 | C# Language Features and .NET Framework, ASP.NET Web Forms and MVC, ADO.NET for Data Access, Windows Presentation Foundation (WPF), Web Services and WCF |
| DSE2XX-5 | Android Programming | Discipline Specific Elective | 4 | Android Architecture and Components, User Interface Design with XML, Activities, Services, Broadcast Receivers, Data Storage and SQLite Databases, Location-Based Services and Google Maps |
| DSE2XX-6 | Cryptography and Network Security | Discipline Specific Elective | 4 | Symmetric Key Cryptography (DES, AES), Asymmetric Key Cryptography (RSA), Hash Functions and Digital Signatures, Network Security Protocols (IPSec, SSL/TLS), Firewalls, IDS/IPS, Malware |
| DSE2XX-7 | Digital Image Processing | Discipline Specific Elective | 4 | Image Enhancement and Restoration, Image Segmentation Techniques, Color Image Processing, Image Compression Standards, Morphological Image Processing |
| DSE2XX-8 | Computer Graphics | Discipline Specific Elective | 4 | 2D and 3D Transformations, Viewing and Projections, Clipping and Rasterization, Shading Models and Ray Tracing, Animation Techniques |
| DSE2XX-9 | Advanced Database Management Systems | Discipline Specific Elective | 4 | Distributed Databases and Architectures, NoSQL Databases (Key-Value, Document, Graph), Data Warehousing and OLAP, Big Data Concepts and Hadoop, Query Optimization and Tuning |
| DSE2XX-10 | Cloud Computing | Discipline Specific Elective | 4 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Virtualization Technologies, Cloud Security Challenges and Solutions, Big Data Processing in Cloud (MapReduce, Spark) |
| DSE2XX-11 | Internet of Things (IoT) | Discipline Specific Elective | 4 | IoT Architecture and Design Principles, Sensors, Actuators, and Microcontrollers, IoT Communication Protocols (MQTT, CoAP), IoT Data Analytics and Cloud Platforms, Security and Privacy in IoT |
| DSE2XX-12 | Blockchain Technology | Discipline Specific Elective | 4 | Cryptographic Hashing and Digital Signatures, Blockchain Architecture and Consensus Mechanisms, Distributed Ledger Technologies, Smart Contracts and DApps, Blockchain Platforms (Ethereum, Hyperledger) |
| DSE2XX-13 | Natural Language Processing | Discipline Specific Elective | 4 | Text Preprocessing and Tokenization, N-grams and Language Models, Part-of-Speech Tagging and Chunking, Named Entity Recognition, Machine Translation and Text Summarization |
| DSE2XX-14 | Data Mining | Discipline Specific Elective | 4 | Data Preprocessing and Data Cleaning, Association Rule Mining (Apriori), Classification Algorithms (Decision Trees, SVM), Clustering Techniques (K-Means, Hierarchical), Outlier Detection and Data Visualization |
| DSE2XX-15 | Software Testing and Quality Assurance | Discipline Specific Elective | 4 | Software Testing Life Cycle, Black Box and White Box Testing, Test Case Design and Test Automation, Software Quality Metrics and Models, Quality Assurance Standards (ISO, CMMI) |
| DSE2XX-16 | Software Project Management | Discipline Specific Elective | 4 | Project Planning and Estimation Techniques, Project Scheduling and Tracking (PERT, Gantt), Risk Management and Mitigation, Software Configuration Management, Team Management and Communication |
| DSE2XX-17 | Soft Computing | Discipline Specific Elective | 4 | Fuzzy Logic and Fuzzy Sets, Artificial Neural Networks (ANN), Genetic Algorithms and Evolutionary Computing, Hybrid Soft Computing Systems, Applications in Optimization and Pattern Recognition |
| DSE2XX-18 | Mobile Computing | Discipline Specific Elective | 4 | Wireless Communication Technologies (GSM, LTE), Mobile Operating Systems (Android, iOS), Mobile Application Development Challenges, Context-Aware Computing, Security Issues in Mobile Computing |
| DSE2XX-19 | Quantum Computing | Discipline Specific Elective | 4 | Qubits and Superposition, Quantum Entanglement, Quantum Gates and Circuits, Quantum Algorithms (Shor''''s, Grover''''s), Quantum Computing Hardware |
| DSE2XX-20 | Reinforcement Learning | Discipline Specific Elective | 4 | Markov Decision Processes (MDP), Bellman Equations and Value Iteration, Q-Learning and SARSA, Policy Gradient Methods, Deep Reinforcement Learning |
| DSE2XX-21 | Ethical Hacking | Discipline Specific Elective | 4 | Ethical Hacking Methodologies, Footprinting and Reconnaissance, Scanning Networks and Vulnerability Analysis, System Hacking and Malware Threats, Web Application Hacking |
| DSE2XX-22 | Information Retrieval | Discipline Specific Elective | 4 | Boolean and Vector Space Models, Indexing and Term Weighting, Query Processing and Ranking Algorithms, Evaluation Metrics (Precision, Recall, F-measure), Web Search and Link Analysis |
| DSE2XX-23 | Computer Vision | Discipline Specific Elective | 4 | Image Formation and Filtering, Feature Detection and Matching, Object Recognition and Classification, Deep Learning for Computer Vision, Motion Estimation and Tracking |
| DSE2XX-24 | Parallel and Distributed Computing | Discipline Specific Elective | 4 | Parallel Computing Architectures, Message Passing Interface (MPI), Shared Memory Programming (OpenMP), Distributed Algorithms and Consensus, Cloud and Grid Computing Concepts |
| DSE2XX-25 | Advanced Machine Learning | Discipline Specific Elective | 4 | Support Vector Machines and Kernel Methods, Bayesian Learning and Graphical Models, Dimensionality Reduction (t-SNE, LDA), Generative Models (GANs, VAEs), Deep Reinforcement Learning |
| DSE2XX-26 | Data Science with R | Discipline Specific Elective | 4 | R Programming for Data Analysis, Data Import, Cleaning, and Transformation, Exploratory Data Analysis and Visualization, Statistical Modeling in R, Machine Learning Algorithms in R |
| DSE2XX-27 | Financial Computing | Discipline Specific Elective | 4 | Introduction to Financial Markets, Computational Finance Models, Risk Management and Portfolio Optimization, Algorithmic Trading Strategies, Financial Data Analysis and Time Series |
| DSE2XX-28 | High Performance Computing | Discipline Specific Elective | 4 | HPC Architectures and Processors, Parallel Programming Models (CUDA, OpenCL), Performance Optimization Techniques, Distributed Shared Memory Systems, Cluster and Grid Computing |
| DSE2XX-29 | Robotics | Discipline Specific Elective | 4 | Robot Kinematics and Dynamics, Robot Control Systems, Robot Sensing and Vision, Path Planning and Navigation, AI in Robotics |
| DSE2XX-30 | Big Data Analytics | Discipline Specific Elective | 4 | Big Data Ecosystem (Hadoop, Spark), Distributed File Systems (HDFS), NoSQL Databases for Big Data, Stream Processing (Kafka, Flink), Big Data Visualization |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC301 | Deep Learning | Core | 4 | Fundamentals of Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs, LSTMs), Generative Adversarial Networks (GANs), Deep Reinforcement Learning Basics |
| VAC301 | Digital Forensics | Value Addition Course | 2 | Digital Forensic Process and Principles, Evidence Acquisition and Handling, File System Analysis and Data Recovery, Network Forensics and Log Analysis, Mobile Device Forensics |
| MCSC301 | Research Project/Dissertation – I | Project | 4 | Research Problem Identification, Literature Review and Gap Analysis, Research Methodology Design, Data Collection and Analysis Planning, Technical Report Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC401 | Research Methodology | Core | 4 | Research Design and Types, Data Collection Methods and Sampling, Statistical Analysis for Research, Scientific Writing and Citation, Ethical Considerations in Research |
| MCSC401 | Research Project/Dissertation – II | Project | 4 | Advanced Implementation and Experimentation, Result Analysis and Interpretation, Dissertation Writing and Formatting, Presentation of Research Findings, Viva-Voce Examination Preparation |
