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M-TECH in Computer Science And Technology at Indian Institute of Engineering Science and Technology, Shibpur
Howrah, West Bengal
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About the Specialization
What is Computer Science and Technology at Indian Institute of Engineering Science and Technology, Shibpur Howrah?
This M.Tech Computer Science and Technology program at Indian Institute of Engineering Science and Technology, Shibpur focuses on advanced concepts and research in computing. It delves into theoretical foundations, cutting-edge technologies like AI, Big Data, and Cloud, and practical applications essential for the evolving Indian tech industry, equipping students with robust problem-solving skills and a research-oriented mindset.
Who Should Apply?
This program is ideal for engineering graduates with a B.E./B.Tech in Computer Science, Information Technology, or related fields, especially those with a valid GATE score, seeking to deepen their technical expertise. It also caters to aspiring researchers and academicians, as well as working professionals aiming to transition into advanced roles in software development, data science, or cybersecurity within the Indian IT sector.
Why Choose This Course?
Graduates of this program can expect to secure high-demand roles in leading Indian IT companies, product-based firms, and research organizations, with entry-level salaries typically ranging from INR 6-12 LPA and significant growth potential. Career paths include Data Scientist, AI/ML Engineer, Cloud Architect, Cybersecurity Specialist, and Research Engineer, aligning with global professional certifications and contributing to India''''s digital transformation.
Student Success Practices
Foundation Stage
Master Core Concepts & Problem Solving- (Semester 1-2)
Focus intensely on understanding the fundamental principles of algorithms, operating systems, databases, and computer networks. Regularly practice problem-solving on platforms like HackerRank, LeetCode, and GeeksforGeeks to strengthen logical thinking and coding skills crucial for technical interviews.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses, Standard Textbooks
Career Connection
Builds a strong foundation for software development roles, clears technical screening rounds, and enhances analytical abilities required across all tech domains.
Active Participation & Peer Learning- (Semester 1-2)
Engage actively in classroom discussions, form study groups, and collaborate on assignments and lab projects. Teaching concepts to peers reinforces your understanding, and diverse perspectives aid in tackling complex problems. Participate in departmental seminars and workshops.
Tools & Resources
Online Collaboration Tools, Departmental Forums, Library Resources
Career Connection
Develops teamwork, communication, and leadership skills vital for corporate environments and collaborative research projects.
Explore Early Research Interests- (Semester 1-2)
Attend research talks and departmental project showcases. Identify potential areas of interest early on. Discuss ideas with professors and senior students to understand ongoing research at the institute, which can guide elective choices and future thesis work.
Tools & Resources
Institute Research Portal, Faculty Profiles, Research Paper Databases
Career Connection
Helps in selecting relevant electives, prepares for research-oriented roles or higher studies (Ph.D.), and identifies potential thesis mentors.
Intermediate Stage
Specialize through Electives & Projects- (Semester 3)
Choose electives strategically based on your career aspirations and emerging industry trends like AI/ML, Cloud Computing, or Cybersecurity. Actively pursue mini-projects or term papers related to your chosen specialization, applying theoretical knowledge to practical problems.
Tools & Resources
Kaggle, GitHub, Domain Libraries (TensorFlow, PyTorch), Cloud Platforms (AWS, Azure)
Career Connection
Builds a specialized skill set highly sought after in focused tech roles, creates a portfolio of practical work, and deepens expertise for your thesis.
Seek Internships & Industry Exposure- (Semester 3)
Actively search for summer/winter internships in reputed Indian tech companies or startups. This provides invaluable real-world experience, helps in understanding industry workflows, and builds professional networks. Attend industry conclaves and tech summits.
Tools & Resources
LinkedIn, Internshala, College Placement Cell, Company Career Pages
Career Connection
Converts theoretical knowledge into practical skills, often leads to pre-placement offers, and strengthens resume for final placements.
Participate in Hackathons & Competitions- (Semester 3)
Engage in university or national-level hackathons, coding competitions, and data science challenges. These platforms provide opportunities to test skills under pressure, innovate, and collaborate with diverse teams, enhancing problem-solving and rapid prototyping abilities.
Tools & Resources
Devpost, Google Developer Student Clubs, National Hackathon Platforms
Career Connection
Showcases practical skills, problem-solving aptitude, and teamwork to potential employers, making your profile stand out.
Advanced Stage
Excellence in Thesis Research & Publication- (Semester 4)
Dedicate significant effort to your M.Tech thesis, aiming for impactful research outcomes. Strive to publish your work in reputed conferences or journals, as this significantly boosts your academic and professional profile. Seek regular feedback from your supervisor.
Tools & Resources
Research Journals (IEEE, ACM), Academic Writing Tools (LaTeX), Plagiarism Checkers
Career Connection
Essential for Ph.D. aspirations, demonstrates independent research capability, and enhances credibility for R&D roles in industry.
Intensive Placement Preparation- (Semester 4)
Begin comprehensive placement preparation focusing on mock interviews (technical and HR), resume building, and aptitude tests. Network with alumni who are in desired roles and seek their guidance. Polish your communication and presentation skills.
Tools & Resources
InterviewBit, Glassdoor, Professional Networking Events, Alumni Connect Platforms
Career Connection
Maximizes chances of securing top-tier placements in core tech companies, prepares for real-world interview scenarios.
Develop Leadership & Mentorship Skills- (Semester 4)
Take on leadership roles in student organizations or mentor junior students. Organize technical workshops or seminars. This develops soft skills like leadership, communication, and event management, which are crucial for higher positions in the industry.
Tools & Resources
University Student Clubs, Departmental Outreach Programs
Career Connection
Prepares for team lead or managerial roles, demonstrates initiative and ability to guide others, contributing to overall career progression.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in Computer Science & Technology / Information Technology or Equivalent with valid GATE Score. Refer to the official admission brochure for specific minimum marks or CGPA criteria.
Duration: 4 semesters / 2 years
Credits: 60 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CST 1001 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis Techniques, Divide and Conquer Algorithms, Greedy Algorithms, Dynamic Programming, Graph Algorithms, NP-Completeness and Approximation Algorithms |
| CST 1002 | Advanced Computer Architecture | Core | 3 | Pipelining and Instruction Level Parallelism (ILP), Multiprocessors and Cache Coherence, Memory Hierarchy Design, Vector Processors, VLIW/EPIC Architectures, Performance Metrics and Evaluation |
| CST 1003 | Advanced Operating Systems | Core | 3 | Process and Thread Management, Distributed Operating Systems, Real-time Operating Systems, Advanced File Systems, Virtualization Techniques, Operating System Security |
| CST 1004 | Data Structure and Algorithm Lab | Lab | 2 | Implementation of Searching and Sorting Algorithms, Graph Traversal and Shortest Path Algorithms, Dynamic Programming Problem Solutions, Advanced Data Structures (Trees, Heaps, Hash Tables), Empirical Analysis of Algorithm Complexity |
| CST 1005 | Operating Systems Lab | Lab | 2 | System Calls Programming in C/C++, Process and Thread Synchronization Mechanisms, Deadlock Detection and Prevention Simulation, Memory Management Techniques Implementation, Shell Scripting for OS Administration |
| CST 1006 | Seminar-I | Project/Seminar | 1 | Technical Presentation Skills, Research Paper Review and Analysis, Effective Communication and Public Speaking, Literature Survey Methodologies, Identification of Research Problem |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CST 2001 | Advanced Database Management System | Core | 3 | Transaction Processing and Recovery, Concurrency Control Protocols, Distributed Database Systems, Object-Oriented and Object-Relational Databases, Data Warehousing and Data Mining Concepts, Introduction to Big Data Management |
| CST 2002 | Advanced Computer Networks | Core | 3 | Network Architectures and Models, Advanced Routing Algorithms, Congestion Control and QoS, Network Security Fundamentals, Wireless and Mobile Networks, Software Defined Networking (SDN) |
| CST 2003 | Machine Learning | Core | 3 | Supervised Learning Algorithms, Unsupervised Learning Techniques, Reinforcement Learning Basics, Neural Networks and Deep Learning Introduction, Model Evaluation and Validation, Ensemble Methods |
| CST 2004 | Database Management System Lab | Lab | 2 | Advanced SQL Queries and Optimization, Database Design and Schema Creation, PL/SQL Programming for Stored Procedures, Transaction Management Implementation, Database Connectivity using JDBC/ODBC |
| CST 2005 | Computer Networks Lab | Lab | 2 | Network Configuration and Troubleshooting, Socket Programming for Network Applications, Implementation of Routing Protocols, Network Traffic Analysis using Tools like Wireshark, Firewall and Security Policy Configuration |
| CST 2006 | Machine Learning Lab | Lab | 2 | Implementation of Core ML Algorithms, Data Preprocessing and Feature Engineering, Model Training and Evaluation, Introduction to Scikit-learn Library, Basics of Deep Learning Frameworks (TensorFlow/Keras) |
| CST 2007 | Seminar-II | Project/Seminar | 1 | Advanced Technical Presentation, In-depth Literature Review, Research Methodology and Experimental Design, Problem Formulation and Solution Approaches, Scholarly Writing and Referencing |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CST 3XXX | Elective-I (One from list of electives such as Cloud Computing, Big Data Analytics, etc.) | Elective | 3 | Cloud Architecture and Deployment Models, Virtualization Technologies, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Security Challenges and Solutions, Big Data Processing in Cloud Environments, Containerization and Serverless Computing |
| CST 3XXX | Elective-II (One from list of electives such as Cryptography and Network Security, Soft Computing, etc.) | Elective | 3 | Hadoop Ecosystem Components, MapReduce Programming Model, HDFS Architecture and Operations, Apache Spark for Data Processing, NoSQL Databases (Cassandra, MongoDB), Real-time Data Streaming with Kafka |
| CST 3001 | Thesis/Project Part-I | Project | 8 | Problem Identification and Scoping, Detailed Literature Survey and Gap Analysis, Methodology Design and Planning, Initial System Design and Architecture, Feasibility Study and Technology Selection, Preliminary Implementation and Report Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CST 4001 | Thesis/Project Part-II | Project | 16 | Advanced Implementation and Development, Experimental Setup and Data Collection, Result Analysis and Interpretation, Performance Evaluation and Optimization, Comprehensive Thesis Writing and Documentation, Project Defense and Presentation |
