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M-TECH in Artificial Intelligence Data Science at Shanmugha Arts Science Technology & Research Academy (SASTRA)
Thanjavur, Tamil Nadu
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About the Specialization
What is Artificial Intelligence & Data Science at Shanmugha Arts Science Technology & Research Academy (SASTRA) Thanjavur?
This Artificial Intelligence & Data Science program at SASTRA Deemed University focuses on equipping students with advanced theoretical knowledge and practical skills in AI, Machine Learning, and Big Data. The curriculum integrates core computational methods with statistical principles to address complex real-world challenges, catering to India''''s burgeoning demand for skilled professionals in digital transformation and data-driven innovation across various industries.
Who Should Apply?
This program is ideal for engineering graduates with a background in Computer Science, IT, ECE, EEE, or M.Sc. holders in relevant fields like Mathematics or Statistics. It caters to fresh graduates aspiring to enter the AI and Data Science domain, working professionals seeking to upskill or transition into advanced analytical roles, and researchers interested in cutting-edge AI methodologies and applications within the Indian tech landscape.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding careers as Data Scientists, Machine Learning Engineers, AI Developers, or Big Data Analysts in leading Indian and global companies. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning significantly higher. The program also prepares students for advanced research or product development roles, fostering growth trajectories aligned with industry certifications and national digital initiatives.
Student Success Practices
Foundation Stage
Build Robust Mathematical and Programming Fundamentals- (Semester 1-2)
Dedicate time to master core concepts in linear algebra, calculus, probability, and statistics, alongside advanced Python programming. Utilize online platforms like NPTEL, Coursera, and competitive programming sites such as HackerRank or LeetCode to practice data structures and algorithms. This solid foundation is critical for understanding complex AI/ML models and excelling in subsequent specialized courses.
Tools & Resources
NPTEL courses, Coursera/edX for Math/Python, HackerRank, LeetCode, GitHub
Career Connection
Strong fundamentals are essential for cracking technical interviews for AI/ML and Data Science roles and building efficient, scalable solutions in industry.
Engage Actively in Lab Sessions and Peer Learning Groups- (Semester 1-2)
Treat lab sessions as opportunities for hands-on application of theoretical concepts. Proactively collaborate with peers on assignments and projects, forming study groups to discuss complex topics and troubleshoot coding issues. Participate in department-organized workshops or hackathons to gain practical exposure and build a collaborative learning environment.
Tools & Resources
Official Lab Manuals, Jupyter Notebooks, Google Colab, WhatsApp/Discord study groups
Career Connection
Develops problem-solving skills, teamwork abilities, and practical implementation experience valued by employers, preparing for collaborative project work.
Explore Introductory Data Science Competitions- (Semester 1-2)
Participate in beginner-friendly data science competitions on platforms like Kaggle or Analytics Vidhya. Focus on understanding the end-to-end process from data cleaning to model deployment, even for small datasets. This helps bridge the gap between academic learning and real-world problem-solving, building an early portfolio of practical projects.
Tools & Resources
Kaggle ''''Getting Started'''' competitions, Analytics Vidhya, Open-source datasets
Career Connection
Early exposure to real data challenges enhances resume, demonstrates initiative, and provides practical experience crucial for entry-level data roles.
Intermediate Stage
Undertake Practical AI/ML Projects and Internships- (Semester 3)
Initiate personal projects leveraging different AI/ML techniques (e.g., NLP, Computer Vision, Deep Learning) or contribute to academic research projects. Actively seek summer internships in data science, AI engineering, or analytics roles in Indian startups or MNCs. Document all projects on GitHub and articulate lessons learned effectively.
Tools & Resources
GitHub, LinkedIn for internship search, Project-based online courses, Company websites
Career Connection
Hands-on projects and internships are paramount for developing practical skills, understanding industry workflows, and securing full-time placements post-graduation.
Specialize through Electives and Advanced Certifications- (Semester 3)
Strategically choose professional electives that align with your career interests (e.g., Computer Vision, Blockchain, Advanced DBMS). Complement academic learning with industry-recognized certifications from platforms like AWS, Google Cloud, or Microsoft Azure in AI/ML. This specialization demonstrates depth of knowledge to potential employers.
Tools & Resources
AWS Machine Learning Specialty, Google Professional Data Engineer, Microsoft Certified: Azure AI Engineer Associate
Career Connection
Specialized skills and certifications make candidates highly competitive for niche roles and command better compensation in the Indian job market.
Network Actively and Attend Industry Events- (Semester 3)
Actively participate in university career fairs, industry seminars, and tech meetups. Connect with alumni and industry professionals on LinkedIn, seeking mentorship and insights into career paths. Building a robust professional network is invaluable for job referrals, career advice, and staying updated with industry trends in India.
Tools & Resources
LinkedIn, Professional Conferences (e.g., AI Summit India), University Alumni Network
Career Connection
Networking opens doors to hidden job opportunities, mentorship, and helps understand market expectations, crucial for career advancement.
Advanced Stage
Excel in Capstone Project and Research Publications- (Semester 4)
Dedicate significant effort to your M.Tech project (Project Work I and II), aiming to solve a novel or complex real-world problem. If feasible, work towards publishing your research findings in reputable conferences or journals. A strong, well-executed project is your ultimate portfolio piece for showcasing expertise and problem-solving capabilities.
Tools & Resources
Research Papers, Thesis Writing Guides, LaTeX, Academic Databases (Scopus, Web of Science)
Career Connection
A high-quality capstone project and publications significantly boost your resume, especially for research-oriented roles, R&D departments, or Ph.D. aspirations.
Intensive Placement Preparation and Mock Interviews- (Semester 4)
Engage in rigorous placement preparation focusing on technical problem-solving (algorithms, data structures, ML concepts), case studies, and behavioral questions. Participate in mock interviews conducted by the university''''s placement cell or external career services. Practice articulating your project experiences and technical skills clearly and concisely.
Tools & Resources
GeeksforGeeks, LeetCode, InterviewBit, University Placement Cell workshops, Mock Interview platforms
Career Connection
Comprehensive preparation is vital for converting interview opportunities into job offers from top tech companies and startups in India.
Cultivate Leadership and Communication Skills- (Semester 4)
Seek opportunities to lead technical discussions, mentor junior students, or present your project work effectively. Focus on improving verbal and written communication, which are critical for conveying complex technical concepts to diverse audiences. Enroll in workshops focused on public speaking and professional communication.
Tools & Resources
Toastmasters International (if available), University Communication Center, Presentation software (PowerPoint, Google Slides)
Career Connection
Strong leadership and communication skills differentiate candidates, enabling them to take on managerial or client-facing roles and progress faster in their careers.
Program Structure and Curriculum
Eligibility:
- A pass in B.E./B.Tech. in Computer Science Engineering/Information Technology/Software Engineering/Electronics & Communication Engineering/Instrumentation & Control Engineering/Electrical & Electronics Engineering or M.Sc. (Computer Science/Information Technology/Software Engineering/Mathematics/Statistics/Data Science) with minimum of 60% aggregate marks.
Duration: 2 years / 4 semesters
Credits: 72 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTCY301T | Applied Probability & Statistical Inference | Core | 3 | Probability Theory, Random Variables and Distributions, Sampling Distributions, Hypothesis Testing, Analysis of Variance, Regression Analysis |
| MTAI301T | Advanced Data Structures and Algorithms | Core | 3 | Asymptotic Analysis, Advanced Tree Structures, Graph Algorithms, Dynamic Programming, Greedy Algorithms, Computational Complexity |
| MTAI302T | Principles of Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving by Searching, Knowledge Representation, Logical Reasoning, Machine Learning Basics, AI Applications |
| MTAI303T | Mathematical Foundations for Data Science | Core | 3 | Linear Algebra, Calculus Fundamentals, Vector Spaces, Matrix Decompositions, Optimization Techniques, Eigenvalues and Eigenvectors |
| MTAI304L | Advanced Data Structures and Algorithms Lab | Lab | 2 | Implementation of Trees, Graph Traversal Algorithms, Sorting and Searching Techniques, Dynamic Programming Problems, Hashing Techniques |
| MTAI305L | AI and ML Programming Lab | Lab | 2 | Python for Data Science, NumPy and Pandas, Scikit-learn, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Model Evaluation Metrics |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTAI306T | Machine Learning | Core | 3 | Regression Models, Classification Algorithms, Clustering Techniques, Dimensionality Reduction, Ensemble Methods, Model Selection and Evaluation |
| MTAI307T | Big Data Analytics | Core | 3 | Big Data Concepts, Hadoop Ecosystem, MapReduce Programming, Apache Spark, NoSQL Databases, Data Stream Processing |
| MTAI308T | Natural Language Processing | Core | 3 | Text Preprocessing, N-grams and Language Models, Word Embeddings, Syntactic and Semantic Analysis, Sequence Models, Information Retrieval |
| MTAI309T | Deep Learning | Core | 3 | Neural Network Architectures, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Learning Frameworks |
| MTAI310L | Big Data Lab | Lab | 2 | Hadoop File System, MapReduce Programs, Spark RDD and DataFrames, Hive and Pig, NoSQL Operations (e.g., MongoDB, Cassandra), Kafka for Streaming Data |
| MTAI311L | Deep Learning Lab | Lab | 2 | TensorFlow and Keras, PyTorch Implementations, CNN for Image Classification, RNN for Sequence Prediction, Transfer Learning, Model Optimization Techniques |
| MTAIE01T | Computer Vision | Professional Elective – I | 3 | Image Formation, Feature Detection and Description, Image Segmentation, Object Recognition, Motion Analysis, 3D Vision |
| MTAIE02T | Cloud Computing | Professional Elective – I | 3 | Cloud Service Models, Virtualization, Cloud Storage, Cloud Security, Cloud Deployment Models, Serverless Computing |
| MTAIE03T | Internet of Things | Professional Elective – I | 3 | |
| MTAIE04T | Reinforcement Learning | Professional Elective – I | 3 |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTAI401T | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming in RL, Monte Carlo Methods, Temporal Difference Learning, Policy Gradient Algorithms, Deep Reinforcement Learning |
| MTAI402T | Data Visualization | Core | 3 | Principles of Data Visualization, Perception and Cognition, Dashboard Design, Interactive Visualizations, Tools (Tableau, PowerBI), Storytelling with Data |
| MTAI403R | Research Methodology | Core | 2 | Research Problem Formulation, Literature Review, Research Design, Data Collection Methods, Statistical Data Analysis, Report Writing and Presentation |
| MTAIE05T | Advanced Database Management Systems | Professional Elective – II | 3 | Distributed Databases, NoSQL Databases, Data Warehousing, Query Processing and Optimization, Transaction Management, Database Security |
| MTAIE06T | Speech and Language Processing | Professional Elective – II | 3 | Speech Production and Perception, Phonetics and Phonology, Speech Recognition, Speech Synthesis, Dialogue Systems, Sentiment Analysis |
| MTAIE07T | Blockchain Technology | Professional Elective – II | 3 | |
| MTAIE08T | Computational Social Science | Professional Elective – II | 3 | |
| MTAIE09T | Image and Video Analytics | Professional Elective – III | 3 | Image Processing Fundamentals, Video Feature Extraction, Object Tracking, Event Recognition, Video Surveillance, Deep Learning for Video |
| MTAIE10T | Human Computer Interaction for AI | Professional Elective – III | 3 | HCI Principles, User Centered Design, AI in User Interfaces, Explainable AI (XAI), Conversational AI, Ethical AI Design |
| MTAIE11T | Quantum Computing for AI | Professional Elective – III | 3 | |
| MTAIE12T | Ethics of AI | Professional Elective – III | 3 | |
| MTAI404P | Project Work - I | Project | 6 | Problem Identification, Literature Survey, Methodology Design, Preliminary Implementation, Data Collection Strategy, Progress Report |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTAI405P | Project Work - II | Project | 12 | System Development, Experimental Design and Evaluation, Results Analysis, Dissertation Writing, Project Defense, Innovation and Impact |
