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M-TECH in Computer Science And Engineering at Sharda University
Gautam Buddh Nagar, Uttar Pradesh
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About the Specialization
What is Computer Science and Engineering at Sharda University Gautam Buddh Nagar?
This M.Tech Computer Science and Engineering program at Sharda University focuses on advanced concepts in AI, Machine Learning, Data Science, and Cybersecurity. It prepares students for high-demand roles in India''''s rapidly evolving tech industry by imparting theoretical knowledge and practical skills, emphasizing cutting-edge technologies relevant to modern digital transformation and innovation.
Who Should Apply?
This program is ideal for engineering graduates with a B.Tech/B.E. in CSE, IT, ECE, or similar, seeking to deepen their technical expertise. It also caters to working professionals aiming to upgrade their skills for leadership roles, and those looking to transition into specialized domains like AI/ML research, big data analytics, or cybersecurity consulting within the Indian market.
Why Choose This Course?
Graduates of this program can expect to secure roles as AI/ML Engineers, Data Scientists, Cybersecurity Analysts, or Cloud Architects in leading Indian IT firms, startups, and MNCs. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning significantly more. The curriculum aligns with industry certifications, enhancing career growth trajectories in the dynamic Indian tech landscape.
Student Success Practices
Foundation Stage
Master Advanced Data Structures & Algorithms- (Semester 1-2)
Dedicate significant time to understanding complex data structures and algorithms. Practice problem-solving on platforms like HackerRank and LeetCode, focusing on topics like graph theory, dynamic programming, and advanced trees to build a robust computational foundation.
Tools & Resources
GeeksforGeeks, LeetCode, CodeChef, NPTEL courses on Algorithms
Career Connection
Essential for cracking coding interviews at top tech companies and building efficient, scalable software solutions, crucial for high-demand roles.
Build a Strong Research Foundation- (Semester 1-2)
Actively engage in Research Methodology coursework and lab sessions. Identify a preliminary research interest early, conduct thorough literature reviews using academic databases, and begin exploring potential problem statements for your dissertation to foster analytical thinking.
Tools & Resources
Google Scholar, IEEE Xplore, ACM Digital Library, Zotero (reference manager), Academic writing guides
Career Connection
Develops critical thinking, problem-solving, and analytical skills, which are vital for innovation, R&D roles, and future academic pursuits in industry or higher education.
Gain Hands-on Operating Systems & Database Experience- (Semester 1-2)
Beyond theoretical understanding, deeply engage with lab assignments for Advanced Operating Systems and DBMS. Experiment with distributed system concepts, shell scripting, and various database technologies (SQL, NoSQL, data warehousing) to acquire practical implementation skills.
Tools & Resources
Linux environments, VirtualBox/VMware, MySQL, MongoDB, Hadoop ecosystem basics
Career Connection
Practical skills in OS and DBMS are fundamental for backend development, system administration, and data engineering roles, making you a versatile candidate.
Intermediate Stage
Specialize in Core Technologies through Electives- (Semester 2-3)
Thoughtfully choose program electives in AI/ML, Data Science, or Cybersecurity based on your career interests. Deep dive into these chosen areas, pursuing projects and certifications relevant to your specialization to build a focused skill set.
Tools & Resources
Coursera/edX for specialized courses, Kaggle for data science competitions, TryHackMe for cybersecurity labs, TensorFlow/PyTorch documentation
Career Connection
Develops focused expertise, making you a strong candidate for specialized roles in AI, ML, Data Science, or Cybersecurity, aligning with industry demands.
Develop Machine Learning Proficiency- (Semester 2-3)
Actively participate in the Machine Learning course and lab. Implement various ML algorithms from scratch, experiment with different datasets, and utilize popular libraries to build and evaluate models. Contribute to open-source ML projects if possible to enhance practical skills.
Tools & Resources
Python, Scikit-learn, TensorFlow, PyTorch, Jupyter Notebooks, Google Colab
Career Connection
Essential for roles as Machine Learning Engineer, Data Scientist, or AI Researcher in India''''s booming AI sector, opening doors to cutting-edge tech careers.
Initiate and Progress Dissertation/Industrial Project- (Semester 3)
Begin working on Dissertation Part-I in Semester 3. Select a challenging and industry-relevant problem, define clear objectives, and meticulously execute the literature survey and preliminary work. Seek mentorship from faculty and industry experts for guidance and feedback.
Tools & Resources
Research papers, Academic journals, Project management tools (e.g., Trello), Collaboration platforms
Career Connection
Demonstrates advanced problem-solving, independent research capabilities, and practical application, highly valued by employers for R&D and senior technical roles.
Advanced Stage
Complete and Defend Your Dissertation/Project- (Semester 4)
Focus intensively on Dissertation Part-II, bringing your research or industrial project to a successful conclusion. Refine your methodologies, analyze results thoroughly, write a high-quality thesis, and prepare for a compelling viva-voce presentation to showcase your expertise.
Tools & Resources
LaTeX for thesis writing, Data visualization tools (e.g., Tableau, Power BI), Presentation software, Grammarly
Career Connection
The capstone project is often a key talking point in interviews, showcasing your ability to deliver substantial technical work and manage complex projects.
Master Industry-Relevant Tools and Technologies- (Throughout Semesters 3-4)
Beyond academics, identify and gain proficiency in specific tools and platforms highly demanded in your chosen specialization (e.g., cloud platforms like AWS/Azure for ML/Data, advanced security tools for Cyber). Pursue relevant certifications to validate your skills.
Tools & Resources
Official documentation for cloud platforms, Vendor-specific training programs (e.g., AWS Certifications), Industry workshops and webinars
Career Connection
Bridges the gap between academic knowledge and industry expectations, making you highly job-ready for specialized roles and increasing your market value significantly.
Network and Prepare for Placements- (Semester 3-4 (intensifying in Semester 4))
Actively engage with university placement cells, attend career fairs, and connect with alumni and industry professionals on LinkedIn. Refine your resume, practice technical and HR interviews, and prepare a strong portfolio showcasing your projects and achievements.
Tools & Resources
LinkedIn, Glassdoor, Naukri.com, Mock interview platforms, University placement portal
Career Connection
Maximizes chances of securing high-quality placements in leading Indian and international companies, paving the way for a successful and impactful career in your chosen field.
Program Structure and Curriculum
Eligibility:
- B.Tech / B.E. in Computer Science & Engineering / Computer Science / Information Technology / Electronics & Communication Engineering / Software Engineering / Electrical Engineering or Equivalent with minimum 50% marks or MCA / M.Sc. in Computer Science / Information Technology / Software with 50% marks. Candidates with GATE score will be preferred.
Duration: 2 years (4 semesters)
Credits: 64 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTCSE101 | Advanced Data Structures & Algorithms | Core | 4 | Analysis of Algorithms, Advanced Trees (B-trees, Red-Black Trees), Graph Algorithms (Shortest Path, MST, Max Flow), Hashing Techniques, Amortized Analysis, NP-Completeness and Approximation Algorithms |
| MTCSE102 | Advanced Operating Systems | Core | 4 | Distributed Operating Systems Concepts, Process Synchronization and Deadlocks in Distributed Systems, Distributed File Systems, Security in Distributed OS, Real-time Operating Systems, Case Studies (e.g., UNIX, Windows NT, Amoeba) |
| MTCSE103 | Research Methodology | Core | 3 | Introduction to Research and Research Problem, Research Design and Methods, Data Collection and Analysis Techniques, Hypothesis Testing and Statistical Tools, Report Writing and Presentation, Ethics in Research and Intellectual Property Rights |
| MT101 | Open Elective-I | Elective (Open) | 3 | |
| MTCSE151 | Advanced Data Structures & Algorithms Lab | Lab | 2 | Implementation of Trees (AVL, Red-Black), Graph Traversal and Shortest Path Algorithms, Dynamic Programming Solutions, Hashing Table Implementations |
| MTCSE152 | Advanced Operating Systems Lab | Lab | 2 | Process and Thread Management, Inter-process Communication, Distributed System Programming, Synchronization Mechanisms |
| MTCSE153 | Research Methodology Lab | Lab | 1 | Data Analysis using Statistical Software (e.g., R, Python libraries), Bibliography Management Tools, Presentation Tools for Research Work, Case Study Analysis and Report Generation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTCSE201 | Advanced Database Management Systems | Core | 4 | Distributed Databases, Object-Oriented Databases, Data Warehousing and OLAP, Data Mining Concepts and Techniques, Big Data Fundamentals and NoSQL Databases, Database Security and Privacy |
| MTCSE202 | Machine Learning | Core | 4 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, Dimensionality Reduction), Neural Networks and Deep Learning Fundamentals, Reinforcement Learning Basics, Model Evaluation and Hyperparameter Tuning, Bias-Variance Tradeoff |
| MTCSE203 | Program Elective-I (Pool includes Deep Learning, Big Data Analytics, Cryptography and Network Security etc.) | Elective (Program Specific) | 3 | Neural Network Architectures (e.g., CNN, RNN), Hadoop Ecosystem and MapReduce, Symmetric and Asymmetric Cryptography, Data Stream Processing, Digital Signatures and Certificates, Deep Learning Frameworks |
| MTCSE204 | Program Elective-II (Pool includes Natural Language Processing, Cloud Computing, Ethical Hacking and Penetration Testing etc.) | Elective (Program Specific) | 3 | Text Preprocessing and Word Embeddings, Cloud Service Models (IaaS, PaaS, SaaS), Reconnaissance and Scanning Techniques, Language Models and Machine Translation, Virtualization and Containerization, Web Application Security |
| MTCSE251 | Advanced Database Management Systems Lab | Lab | 2 | SQL Query Optimization, NoSQL Database Operations (e.g., MongoDB, Cassandra), Data Warehousing ETL Processes, Big Data Platform Interaction (e.g., HDFS, Hive) |
| MTCSE252 | Machine Learning Lab | Lab | 2 | Implementing Supervised and Unsupervised Learning Algorithms, Model Training, Testing, and Evaluation, Using Python Libraries (Scikit-learn, Pandas), Introduction to TensorFlow/PyTorch |
| MTCSE253 | Program Elective-I Lab | Lab | 1 | Practical implementation related to chosen Elective-I (e.g., Deep Learning models, Hadoop programs, Network security tools) |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTCSE301 | Program Elective-III (Pool includes Computer Vision, Blockchain Technology, IoT and Cyber Physical Systems etc.) | Elective (Program Specific) | 3 | Image Processing and Feature Extraction, Cryptographic Primitives and Distributed Ledgers, IoT Architectures and Protocols, Object Detection and Image Segmentation, Smart Contracts and Consensus Mechanisms, Security and Privacy in IoT |
| MTCSE302 | Program Elective-IV (Pool includes Big Data Security, Software Defined Networks, Reinforcement Learning etc.) | Elective (Program Specific) | 3 | Data Privacy and Anonymization Techniques, SDN Architecture and OpenFlow Protocol, Markov Decision Processes, Secure Multi-party Computation, Network Function Virtualization, Q-Learning and Deep Q-Networks |
| MTCSE351 | Dissertation Part-I / Industrial Project | Project | 8 | Problem Identification and Formulation, Extensive Literature Survey, Methodology Design and Planning, Preliminary Implementation and Results, Interim Report Writing, Presentation of Research Proposal |
| MT301 | Open Elective-II | Elective (Open) | 2 |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTCSE451 | Dissertation Part-II | Project | 10 | Advanced Research and Development, Data Analysis and Interpretation of Results, Final Solution Implementation and Testing, Comprehensive Thesis Writing, Viva-Voce Examination Preparation, Publication of Research Findings |
