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M-TECH in Advanced Computing at Shanmugha Arts Science Technology & Research Academy (SASTRA)
Thanjavur, Tamil Nadu
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About the Specialization
What is Advanced Computing at Shanmugha Arts Science Technology & Research Academy (SASTRA) Thanjavur?
This M.Tech Advanced Computing program at SASTRA University focuses on equipping students with expertise in cutting-edge computing paradigms. It addresses the increasing demand for professionals proficient in areas like Artificial Intelligence, Machine Learning, Big Data, and Cloud Technologies, critical for India''''s rapidly evolving digital economy. The program emphasizes a strong theoretical foundation coupled with practical, industry-relevant skills and innovation.
Who Should Apply?
This program is ideal for engineering graduates with a background in Computer Science, IT, ECE, or related disciplines who aspire to build a career in advanced technological domains. It caters to fresh graduates seeking entry into high-tech roles and working professionals aiming to upskill for leadership positions in AI, Data Science, or Cloud architecture in the Indian tech industry. A strong analytical bent is beneficial.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths as AI/ML Engineers, Data Scientists, Cloud Architects, or Big Data Analysts in India. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning significantly more based on their expertise. The program aligns with industry certifications and fosters growth trajectories in leading Indian and multinational IT firms.
Student Success Practices
Foundation Stage
Master Core Programming & Data Concepts- (Semester 1-2)
Dedicate time to thoroughly understand advanced data structures, algorithms, and Python programming. Utilize platforms like HackerRank, LeetCode, and GeeksforGeeks for daily coding challenges to build problem-solving skills crucial for technical interviews in India.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation
Career Connection
A strong foundation in these areas is non-negotiable for securing roles in product-based companies and startups, as it directly impacts your coding interview performance and technical aptitude.
Build a Strong Academic Base in ML/DS Fundamentals- (Semester 1-2)
Focus intently on Machine Learning and Data Science core concepts from theory subjects. Complement classroom learning with online courses from Coursera (e.g., Andrew Ng''''s ML course) or edX, and actively participate in academic discussions to clarify doubts and deepen understanding.
Tools & Resources
Coursera (Andrew Ng''''s ML), edX, university library resources
Career Connection
This ensures a deep theoretical understanding, essential for research opportunities and advanced roles in AI/ML engineering or data science, preparing for competitive examinations.
Engage in Peer Learning & Study Groups- (Semester 1-2)
Form study groups with peers to discuss complex topics, solve problems collaboratively, and prepare for internal assessments. Teaching others reinforces your own understanding and develops communication skills valuable in team environments and future collaborations.
Tools & Resources
Dedicated study rooms, online collaboration tools like Google Meet
Career Connection
Enhances problem-solving abilities, builds a professional network, and improves communication skills, which are crucial for team-based projects in the Indian industry.
Intermediate Stage
Undertake Practical Projects & Hackathons- (Semester 2-3)
Apply theoretical knowledge by working on mini-projects, participating in hackathons (e.g., those hosted by MLH, Smart India Hackathon), and contributing to open-source projects. Focus on building a robust portfolio showcasing skills in Cloud, Big Data, and Deep Learning.
Tools & Resources
GitHub, Kaggle, AWS/Azure free tier accounts, Jupyter Notebooks
Career Connection
Practical experience and a strong project portfolio significantly boost employability for internships and full-time roles in Indian tech companies by demonstrating tangible skills.
Seek Industry Internships and Workshops- (Semester 2-3)
Actively search for internships during summer breaks with Indian startups, MNCs, or research labs to gain real-world industry exposure. Attend workshops and seminars organized by professional bodies or the institution to learn about emerging trends and network with professionals.
Tools & Resources
LinkedIn, Internshala, Naukri.com, College placement cell
Career Connection
Internships often convert into pre-placement offers and provide invaluable experience, making you more attractive to employers and aiding in competitive campus placements.
Specialize through Electives & Certifications- (Semester 2-3)
Carefully choose electives aligned with your career interests (e.g., Deep Learning, IoT, Blockchain). Pursue relevant industry certifications (e.g., AWS Certified Cloud Practitioner, Microsoft Azure AI Engineer) to validate your specialized skills and stand out in the job market.
Tools & Resources
Official certification platforms (AWS, Azure, Google Cloud), NPTEL
Career Connection
Specialized skills and certifications directly lead to roles in niche areas like Cloud Engineering, AI Research, or Cybersecurity, commanding higher packages in India.
Advanced Stage
Excel in Final Year Project with Publication Focus- (Semester 3-4)
Dedicate significant effort to the final year project, aiming for an innovative solution with potential for research publication in reputed conferences (e.g., IEEE, ACM) or journals. Collaborate with faculty mentors for guidance and explore patenting possibilities for cutting-edge work.
Tools & Resources
Research journals, conference proceedings, academic databases, IP Cell guidance
Career Connection
A strong research project with publications demonstrates problem-solving and innovation, highly valued for R&D roles, higher studies (Ph.D.), and advanced tech positions globally and in India.
Intensive Placement Preparation & Mock Interviews- (Semester 4)
Begin placement preparation well in advance, focusing on technical aptitude, quantitative ability, verbal skills, and domain-specific knowledge. Participate in mock interviews (technical and HR) organized by the career services cell or alumni network for comprehensive feedback.
Tools & Resources
Placement papers, mock interview platforms, alumni network, online coding assessments
Career Connection
This structured preparation is critical for converting job interviews into offers from top Indian and multinational companies during campus placements, ensuring a successful career launch.
Network Strategically & Mentor Juniors- (Semester 3-4)
Actively participate in alumni events, industry meetups, and professional networking platforms. Leverage connections for mentorship, job referrals, and career insights. Mentor junior students, enhancing your leadership and communication abilities while giving back to the community.
Tools & Resources
LinkedIn, professional conferences, alumni portals
Career Connection
Networking opens doors to hidden job markets, mentorship opportunities, and builds a professional reputation, essential for long-term career growth and leadership roles in the Indian tech industry.
Program Structure and Curriculum
Eligibility:
- B.E. / B.Tech. in Computer Science & Engineering / Information & Communication Technology / Information Technology / Software Engineering / Electrical & Electronics Engineering / Electronics & Communication Engineering / Electronics & Instrumentation Engineering / Instrumentation & Control Engineering / Mechatronics Engineering / Computer & Communication Engineering / Computer Science & Business Systems / Applied Electronics / Artificial Intelligence and Data Science / Computer Science / Artificial Intelligence / Data Science / Cyber Security / IoT / equivalent or M.Sc. in Computer Science / Information Technology / Software Engineering / Applied Electronics / Artificial Intelligence and Data Science / equivalent or MCA. Candidates must have secured 60% of aggregate marks or above in the qualifying examination.
Duration: 2 years / 4 semesters
Credits: 73 Credits
Assessment: Internal: 40% (for theory subjects), External: 60% (for theory subjects)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAC001 | Advanced Data Structures and Algorithms | Core | 4 | Advanced Data Structures (Heaps, Trees, Hashing), Graph Algorithms (DFS, BFS, Shortest Paths), Dynamic Programming Principles, Greedy Algorithms and Divide and Conquer, Amortized Analysis and NP-Completeness, Randomized Algorithms |
| MAC002 | Advanced Computer Architecture | Core | 4 | Pipelining and Instruction Level Parallelism, Memory Hierarchy Design and Performance, Multicore and Parallel Processors, GPU Architecture and Programming, Interconnection Networks and Topology, Advanced Storage Systems |
| MAC003 | Machine Learning | Core | 4 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, Dimensionality Reduction), Reinforcement Learning Fundamentals, Neural Networks and Perceptrons, Ensemble Methods and Model Evaluation, Bias-Variance Tradeoff |
| MAC004 | Data Science with Python | Core | 4 | Python for Data Science (NumPy, SciPy), Data Manipulation with Pandas, Data Visualization with Matplotlib and Seaborn, Statistical Analysis and Hypothesis Testing, Machine Learning with Scikit-learn, Introduction to Big Data Ecosystem |
| MAC005 | Advanced Data Structures and Algorithms Lab | Lab | 2 | Implementation of Advanced Tree Structures, Graph Algorithms Programming, Dynamic Programming Solutions, Greedy Algorithm Implementations, Complexity Analysis of Algorithms, Practical Application of Data Structures |
| MAC006 | Machine Learning Lab | Lab | 2 | Implementing Supervised Learning Models, Implementing Unsupervised Learning Models, Data Preprocessing and Feature Engineering, Model Training, Validation, and Evaluation, Hyperparameter Tuning and Optimization, Introduction to Deep Learning Frameworks |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAC007 | Advanced Database Management Systems | Core | 4 | Distributed Database Architectures, Object-Oriented and Object-Relational Databases, NoSQL Databases (Key-Value, Document, Graph), Query Processing and Optimization Techniques, Transaction Management and Concurrency Control, Database Security and Privacy |
| MAC008 | Cloud Computing | Core | 4 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models (Public, Private, Hybrid), Virtualization Technologies (VMware, Xen), Cloud Storage Solutions (S3, EBS), Cloud Security and Compliance, Major Cloud Platforms (AWS, Azure, GCP) |
| MAC009 | Big Data Analytics | Core | 4 | Big Data Characteristics (Volume, Velocity, Variety), Hadoop Ecosystem (HDFS, YARN), MapReduce Programming Model, Apache Spark and its Ecosystem, Stream Processing (Kafka, Storm), NoSQL Databases for Big Data |
| MAC010 | Research Methodology and IPR | Core | 3 | Research Problem Formulation and Design, Quantitative and Qualitative Research Methods, Data Collection, Analysis, and Interpretation, Report Writing and Academic Ethics, Intellectual Property Rights (IPR), Patents, Copyrights, Trademarks, and Trade Secrets |
| MACE009 | Deep Learning | Elective | 3 | Foundations of Neural Networks, Convolutional Neural Networks (CNNs) for Images, Recurrent Neural Networks (RNNs) for Sequences, Autoencoders and Generative Adversarial Networks (GANs), Deep Learning Frameworks (TensorFlow, PyTorch), Transfer Learning and Optimization Techniques |
| MAC011 | Cloud Computing & Big Data Lab | Lab | 2 | Setting up Cloud Environments (AWS, Azure, GCP), Virtual Machine and Container Deployment, Hadoop Cluster Configuration and Management, MapReduce Program Development, Spark Application Implementation, Cloud Storage and Database Services |
| MAC012 | Mini Project with Seminar | Project | 2 | Problem Identification and Scope Definition, Literature Survey and Gap Analysis, Design and Development of a Prototype, Testing and Evaluation of the Solution, Technical Report Writing, Seminar Presentation Skills |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MACE002 | Internet of Things | Elective | 3 | IoT Architecture and Design Principles, Sensors, Actuators, and Embedded Systems, IoT Communication Protocols (MQTT, CoAP, LoRaWAN), IoT Platforms and Cloud Integration, Data Analytics and Machine Learning in IoT, IoT Security and Privacy Challenges |
| MACE004 | Artificial Intelligence | Elective | 3 | Intelligent Agents and Problem Solving, Heuristic Search Techniques (A*, IDA*), Knowledge Representation (Logic, Ontology), Automated Reasoning and Expert Systems, Planning and Uncertainty (Bayesian Networks), Natural Language Processing Fundamentals |
| MACE014 | Block Chain Technology | Elective | 3 | Cryptographic Primitives (Hashing, Digital Signatures), Distributed Ledger Technology (DLT), Consensus Mechanisms (PoW, PoS), Smart Contracts and Decentralized Applications (DApps), Ethereum and Hyperledger Platforms, Blockchain Use Cases and Challenges |
| MAC013 | Project Work - Phase I | Project | 6 | In-depth Problem Definition and Justification, Comprehensive Literature Review and Gap Analysis, Methodology Design and Experimental Setup, Preliminary Implementation and Initial Results, Project Management and Timeline Planning, Interim Report Submission and Presentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAC014 | Project Work - Phase II | Project | 16 | Advanced System Implementation and Refinement, Rigorous Testing, Validation, and Performance Analysis, Comparative Study and Optimization Techniques, Comprehensive Thesis Writing and Documentation, Final Project Defense and Viva-Voce, Potential for Research Publication |
