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M-TECH in Computer Science Engineering at SCT Institute of Technology
Bengaluru, Karnataka
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About the Specialization
What is Computer Science & Engineering at SCT Institute of Technology Bengaluru?
This M.Tech Computer Science & Engineering program at SCT Institute of Technology focuses on advanced concepts in computing, preparing students for cutting-edge roles in the rapidly evolving Indian tech landscape. The curriculum emphasizes core CS principles alongside emerging areas like AI, Machine Learning, Big Data, and Cloud Computing, fostering innovation and problem-solving skills highly sought after by Indian industries.
Who Should Apply?
This program is ideal for engineering graduates with a B.E./B.Tech in Computer Science or related fields, aspiring to specialize in advanced computing domains. It attracts fresh graduates seeking entry into high-tech R&D roles and working professionals aiming to upskill for leadership positions in software development, data science, or cybersecurity within the Indian IT sector.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths as AI/ML Engineers, Data Scientists, Cloud Architects, or Cybersecurity Analysts in India. Entry-level salaries range from INR 6-10 LPA, with experienced professionals earning significantly more. The program aligns with industry demands, preparing students for impactful roles in both established MNCs and booming Indian startups.
Student Success Practices
Foundation Stage
Master Core Algorithmics and Mathematics- (Semester 1-2)
Focus intensely on Advanced Engineering Mathematics and Analysis & Design of Algorithms. Participate in coding competitions to apply theoretical knowledge and improve problem-solving speed. Form study groups to discuss complex topics and peer-review solutions, crucial for competitive programming and technical interviews.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses on Algorithms
Career Connection
Strong algorithmic skills are foundational for software development, data science, and competitive programming roles in leading product-based companies across India.
Engage with Advanced Systems and OS Concepts- (Semester 1-2)
Beyond classroom learning, explore practical aspects of Advanced Computer Architecture and Advanced Operating Systems. Experiment with virtual machines, containerization technologies like Docker and Kubernetes, and open-source distributed systems. Contributing to relevant open-source projects can provide valuable practical exposure.
Tools & Resources
GitHub, Docker Hub, Linux kernel documentation, Operating System design books
Career Connection
This engagement is essential for roles in system architecture, cloud engineering, DevOps, and backend development, enabling the design of robust and scalable systems sought by Indian tech giants.
Build Foundational ML/AI Skills through Projects- (Semester 1-2)
Start exploring Machine Learning with hands-on projects, even if it''''s an elective. Utilize platforms like Kaggle for datasets and competitions. Implement basic ML algorithms in Python and understand their real-world applications to gain practical experience.
Tools & Resources
Python (Scikit-learn, TensorFlow, PyTorch), Kaggle, Coursera/edX ML courses
Career Connection
Provides an early edge for aspiring Data Scientists, Machine Learning Engineers, and AI Researchers, a high-demand area in the burgeoning Indian tech industry and research sector.
Intermediate Stage
Deep Dive into Specialization with Electives and Mini-Project- (Semester 2-3)
Carefully choose electives like Big Data Analytics or Cloud Computing based on your career interests. For the Mini Project, select a problem statement aligned with your specialization and develop a robust solution, integrating concepts from multiple subjects to demonstrate comprehensive understanding.
Tools & Resources
AWS/Azure/GCP Free Tier, Hadoop/Spark ecosystems, specific domain libraries/frameworks
Career Connection
Specialization deepens expertise, making graduates more attractive for targeted roles in areas like data engineering, cloud architecture, or IoT development within Indian companies and MNCs.
Maximize Internship Experience and Industry Exposure- (Semester 3)
Actively seek out and secure a meaningful internship in a relevant industry. Treat the internship as an extended learning opportunity, network with professionals, and contribute significantly to real-world projects. This experience can often be integrated with your Phase-I project work.
Tools & Resources
LinkedIn, college placement cell, industry mentorship programs
Career Connection
Internships provide invaluable practical experience, enhance employability, often lead to pre-placement offers, and build a professional network within the Indian tech ecosystem, crucial for securing first jobs.
Execute a High-Impact Master''''s Thesis (Project Work Phase I & II)- (Semester 3-4)
Select a challenging and novel research problem for your M.Tech project. Focus on original contributions, rigorous methodology, and comprehensive experimental validation. Aim for a publication in a reputed conference or journal to showcase your research capabilities.
Tools & Resources
Research papers (IEEE Xplore, ACM Digital Library), LaTeX for thesis writing, simulation tools, high-performance computing resources
Career Connection
A strong thesis demonstrates advanced research capabilities and problem-solving prowess, crucial for R&D roles, academic pursuits, and distinguishes candidates in competitive job markets for senior technical positions.
Advanced Stage
Program Structure and Curriculum
Eligibility:
- B.E/B.Tech in relevant branch with 50% aggregate marks (45% for SC/ST/Cat-I) and valid GATE/PGCET score.
Duration: 2 years / 4 semesters
Credits: 79 Credits
Assessment: Internal: 40% (Theory), 50% (Practical/Lab), External: 60% (Theory), 50% (Practical/Lab)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MCS11 | Advanced Engineering Mathematics | Core | 4 | Linear Algebra, Probability and Statistics, Numerical Methods, Transform Techniques, Optimization Techniques |
| 18MCS12 | Analysis and Design of Algorithms | Core | 4 | Introduction to Algorithms, Divide and Conquer, Greedy Method, Dynamic Programming, NP-Hard and NP-Complete Problems |
| 18MCS13 | Advanced Computer Architecture | Core | 4 | Fundamentals of Computer Design, Pipelining, Instruction-Level Parallelism, Data-Level Parallelism, Thread-Level Parallelism |
| 18MCS14 | Advanced Operating Systems | Core | 4 | Architectures of Distributed Systems, Communication and Synchronization, Distributed File Systems, Distributed Shared Memory, Security in Distributed Systems |
| 18MCS151 | Machine Learning | Elective | 3 | Introduction to Machine Learning, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Reinforcement Learning Basics, Deep Learning Concepts |
| 18MCSL16 | Advanced Computer Networks Lab | Lab | 2 | Network Emulators and Simulators, TCP/IP Programming, Routing Protocol Implementation, Network Security Tools, Wireless Network Configuration |
| 18MCS17 | Technical Seminar | Seminar | 1 | Literature Review, Technical Report Writing, Presentation Skills, Emerging Technologies, Research Area Identification |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MCS21 | Soft Computing | Core | 4 | Fuzzy Logic, Artificial Neural Networks, Genetic Algorithms, Hybrid Systems, Evolutionary Computing |
| 18MCS22 | Advanced Database Management Systems | Core | 4 | Transaction Management, Concurrency Control, Distributed Databases, Object-Oriented Databases, Data Warehousing and Mining |
| 18MCS23 | Research Methodology and IPR | Core | 3 | Research Design, Data Collection and Analysis, Statistical Interpretation, Intellectual Property Rights Fundamentals, Patenting and Copyrights |
| 18MCS241 | Wireless Communication and Mobile Computing | Elective | 3 | Wireless Networks, Mobile IP, GSM and GPRS Architectures, Ad-hoc Networks, Sensor Networks |
| 18MCS251 | Big Data Analytics | Elective | 3 | Introduction to Big Data, Hadoop Ecosystem, MapReduce Programming, Spark Framework, Data Visualization Techniques |
| 18MCIL26 | Mini Project | Project | 2 | Problem Identification, Literature Survey, Design and Implementation, Testing and Evaluation, Report Writing |
| 18MCL27 | Soft Computing Lab | Lab | 2 | Fuzzy Logic Implementation, Neural Network Training, Genetic Algorithm Applications, Hybrid System Design, Evolutionary Algorithms |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MCS311 | Web 2.0 and Rich Internet Applications | Elective | 3 | Web Technologies Evolution, AJAX and JSON, Web Services, Semantic Web, Cloud-based Web Applications |
| 18MCS321 | Advanced topics in Data Mining | Elective | 3 | Association Rule Mining, Advanced Classification Techniques, Clustering Algorithms, Stream Data Mining, Spatial and Temporal Data Mining |
| 18MCS33 | Internship | Internship | 4 | Industry Exposure, Project Implementation, Professional Skill Development, Report Submission, Presentation of Work |
| 18MCSP34 | Project Work Phase – I | Project | 6 | Problem Definition, Extensive Literature Review, Methodology Design, Preliminary Implementation, Project Proposal Development |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MCSP41 | Project Work Phase – II | Project | 20 | Advanced Implementation, Experimental Results Analysis, Performance Evaluation, Thesis Writing and Documentation, Final Presentation and Defense |
