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M-SC in Computer Science at SRM Institute of Science and Technology (Deemed to be University)
Chengalpattu, Tamil Nadu
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About the Specialization
What is Computer Science at SRM Institute of Science and Technology (Deemed to be University) Chengalpattu?
This M.Sc Computer Science program at SRMIST focuses on advanced computational theories and practical applications crucial for India''''s digital transformation. It equips students with expertise in cutting-edge domains like AI, Machine Learning, Big Data, and Cloud Computing, preparing them for high-demand roles in the rapidly evolving Indian tech industry. The curriculum is designed to foster innovation and problem-solving skills.
Who Should Apply?
This program is ideal for Bachelor''''s degree holders in Computer Science, Computer Applications, or IT who aspire to specialize in advanced computing fields. It targets fresh graduates seeking entry into AI/ML or data science roles, as well as working professionals looking to upskill for managerial or technical lead positions in India''''s booming software and IT services sector.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including Data Scientist, Machine Learning Engineer, Cloud Architect, or Big Data Analyst. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning INR 10-25+ LPA. The program aligns with certifications from AWS, Azure, and Google Cloud, enhancing career growth in Indian and global MNCs.
Student Success Practices
Foundation Stage
Master Programming Fundamentals and Data Structures- (Semester 1-2)
Dedicate time to consistently practice core programming concepts in Python and C++/Java, focusing on efficient data structures and algorithms. Utilize online coding platforms to solve daily problems and strengthen logical thinking.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses on DSA
Career Connection
Strong fundamentals are crucial for technical interviews in top Indian tech companies and form the basis for advanced ML/AI algorithms.
Build a Foundational Project Portfolio- (Semester 1-2)
Beyond lab assignments, initiate small personal projects related to course content, such as a basic web application, a database management system, or a simple data analysis tool. Collaborate with peers to learn team dynamics.
Tools & Resources
GitHub for version control, VS Code, Python/Java IDEs
Career Connection
Demonstrates practical application of learned skills to recruiters, making you stand out for internships and entry-level positions.
Actively Participate in Technical Workshops and Clubs- (Semester 1-2)
Join departmental computer science clubs and attend workshops on emerging technologies like Python for Data Science or introductory Machine Learning. Engage in discussions and network with faculty and senior students.
Tools & Resources
SRMIST Computer Science Department events, Local tech meetups
Career Connection
Expands knowledge beyond curriculum, fosters community, and develops soft skills essential for team-based roles in the Indian IT sector.
Intermediate Stage
Pursue Internships and Industry Certifications- (Semester 3-4)
Actively seek summer internships in areas like Data Science, Cloud Computing, or Software Development. Additionally, pursue industry-recognized certifications relevant to your specialization from platforms like AWS, Google Cloud, or Coursera.
Tools & Resources
LinkedIn, Internshala, Naukri.com, Coursera, Udemy
Career Connection
Provides real-world experience, validates specialized skills, and significantly boosts your resume for placements in leading Indian and global companies.
Specialize and Build Advanced Projects- (Semester 3-4)
Choose an area of interest (e.g., Deep Learning, Big Data) and work on complex projects that apply theoretical knowledge. Focus on end-to-end implementation, potentially contributing to open-source projects or participating in hackathons.
Tools & Resources
Kaggle for datasets, TensorFlow/PyTorch, Hadoop/Spark, GitHub
Career Connection
Develops deep expertise in a specific domain, creates a strong portfolio, and demonstrates problem-solving capabilities to potential employers.
Engage in Research and Academic Publications- (Semester 3-4)
Collaborate with faculty on research projects. Aim to present findings at college-level symposiums or even publish in national/international conferences. This hones critical thinking and research methodology skills.
Tools & Resources
SRMIST research labs, IEEE Xplore, ACM Digital Library
Career Connection
Enhances academic profile, beneficial for higher studies (PhD) or R&D roles in technology companies and research institutions in India.
Advanced Stage
Intensive Placement Preparation and Mock Interviews- (Semester 4)
Focus rigorously on company-specific placement preparation, including aptitude tests, technical rounds, and HR interviews. Participate in mock interviews with college placement cells and alumni to refine your communication and problem-solving under pressure.
Tools & Resources
Placement cell resources, Glassdoor, PrepInsta, CareerRide
Career Connection
Maximizes chances of securing top-tier placements with competitive salary packages in your desired domain within India''''s tech landscape.
Develop Leadership and Mentorship Skills- (Semester 4)
Take on leadership roles in student organizations or mentor junior students in technical skills. Organize coding competitions or workshops. This develops management and communication skills vital for future career progression.
Tools & Resources
Student council, Departmental clubs
Career Connection
Prepares you for team lead or managerial positions, demonstrating initiative and ability to guide others in a professional setting.
Network Actively with Industry Professionals- (Semester 4)
Attend industry conferences, tech summits, and alumni networking events. Connect with professionals on platforms like LinkedIn to gain insights into industry trends, job market demands, and potential career opportunities.
Tools & Resources
LinkedIn, Conference websites, SRMIST Alumni Network
Career Connection
Opens doors to exclusive job opportunities, mentorship, and a deeper understanding of the professional world, fostering long-term career growth in India and abroad.
Program Structure and Curriculum
Eligibility:
- Bachelor''''s degree in Computer Science/Computer Applications/Information Technology or any equivalent degree with a minimum of 50% marks.
Duration: 4 semesters / 2 years
Credits: 73 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCC101 | Advanced Data Structures and Algorithms | Core | 4 | Data structures complexity analysis, Advanced tree structures, Hashing and collision resolution, Graph algorithms, Dynamic programming, Amortized analysis |
| PCC102 | Object Oriented Software Engineering | Core | 4 | Software development life cycle, Object-oriented modeling, UML diagrams and notations, Software design patterns, Testing strategies and methodologies, Software project management |
| PCC103 | Advanced Database Management Systems | Core | 4 | Relational database design, Query processing and optimization, Transaction management, Concurrency control and recovery, Distributed databases, NoSQL databases and their applications |
| PCC104 | Programming in Python | Core | 4 | Python language fundamentals, Data structures in Python, Functions and modules, Object-oriented programming in Python, File I/O and exception handling, Introduction to Python libraries for data science |
| PCC105 | Advanced Data Structures and Algorithms Lab | Lab | 2 | Implementation of advanced data structures, Graph traversal algorithms, Sorting and searching techniques, Dynamic programming problems, Algorithm efficiency analysis |
| PCC106 | Object Oriented Software Engineering Lab | Lab | 2 | UML modeling with tools, Design pattern implementation, Software requirement analysis, Test case generation and execution, Version control systems |
| PCC107 | Advanced Database Management Systems Lab | Lab | 2 | Advanced SQL queries and stored procedures, Database design and normalization, NoSQL database operations, Transaction isolation levels, Database connectivity with programming languages |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCC201 | Machine Learning | Core | 4 | Introduction to machine learning, Supervised learning algorithms, Unsupervised learning algorithms, Model evaluation and selection, Feature engineering, Introduction to deep learning |
| PCC202 | Advanced Computer Networks | Core | 4 | Network architectures and protocols, TCP/IP protocol suite, Routing and switching concepts, Network security principles, Wireless and mobile networks, Introduction to IoT networking |
| PCC203 | Cloud Computing | Core | 4 | Cloud computing concepts and architecture, Service models (IaaS, PaaS, SaaS), Deployment models (public, private, hybrid), Virtualization technologies, Cloud security and privacy, Cloud platforms and tools |
| PE | Elective I (e.g., Advanced Java Programming) | Professional Elective | 3 | JVM architecture and memory management, Multithreading and concurrency, Generics and collections framework, Database connectivity (JDBC), Web programming with Servlets/JSP, Introduction to Spring framework |
| PCC205 | Machine Learning Lab | Lab | 2 | Data preprocessing and exploration, Implementation of classification algorithms, Implementation of clustering algorithms, Model training and evaluation metrics, Using ML libraries like Scikit-learn |
| PCC206 | Cloud Computing Lab | Lab | 2 | Setting up virtual machines, Deploying applications on cloud platforms, Working with cloud storage services, Containerization with Docker, Cloud security configurations |
| PCC207 | Mini Project | Core | 2 | Problem identification and analysis, System design and architecture, Implementation of a mini-project, Testing and debugging, Project report writing and presentation |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCC301 | Big Data Analytics | Core | 4 | Introduction to Big Data concepts, Hadoop ecosystem (HDFS, MapReduce), Spark for big data processing, Data warehousing and ETL processes, Data visualization techniques, Stream processing with Kafka/Flink |
| PCC302 | Deep Learning | Core | 4 | Fundamentals of neural networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GANs), Deep learning frameworks (TensorFlow, Keras), Optimization techniques for deep learning |
| PE | Elective II (e.g., Blockchain Technology) | Professional Elective | 3 | Cryptography and hash functions, Distributed ledger technology (DLT), Consensus mechanisms (PoW, PoS), Smart contracts and DApps, Blockchain platforms (Ethereum, Hyperledger), Blockchain applications and challenges |
| PE | Elective III (e.g., Cyber Security) | Professional Elective | 3 | Network security fundamentals, Cryptographic algorithms and protocols, Web application security, Malware analysis and prevention, Digital forensics, Security auditing and risk management |
| PCC305 | Big Data Analytics Lab | Lab | 2 | Hadoop MapReduce programming, Spark RDD and DataFrame operations, Hive queries for data warehousing, Implementing data visualization, Working with large datasets |
| PCC306 | Deep Learning Lab | Lab | 2 | Building CNN models for image classification, Implementing RNNs for sequence prediction, Transfer learning applications, Fine-tuning pre-trained models, Using GPU for deep learning |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCC401 | Project Work and Dissertation | Core | 12 | Research problem identification, Literature review and gap analysis, Methodology design and implementation, Data collection and analysis, Dissertation writing and formatting, Project presentation and viva-voce |
