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B-TECH-B-E in Artificial Intelligence And Data Science at Saveetha Institute of Medical and Technical Sciences
Chennai, Tamil Nadu
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About the Specialization
What is Artificial Intelligence and Data Science at Saveetha Institute of Medical and Technical Sciences Chennai?
This Artificial Intelligence and Data Science program at Saveetha Institute of Medical and Technical Sciences focuses on equipping students with advanced skills in designing, developing, and deploying intelligent systems. With India''''s rapidly expanding digital economy and IT sector, this specialization meets the high demand for professionals who can harness data for innovation. The program differentiates itself by integrating theoretical foundations with hands-on project-based learning, preparing graduates for real-world challenges.
Who Should Apply?
This program is ideal for aspiring engineers and innovators passionate about leveraging data and AI to solve complex problems. It caters to fresh 10+2 graduates with a strong aptitude for mathematics and computing. Working professionals aiming to transition into high-demand AI/DS roles or upskill in latest technologies will also find this curriculum beneficial. Strong analytical skills and a foundational understanding of programming are beneficial prerequisites.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding India-specific career paths as Data Scientists, AI Engineers, Machine Learning Engineers, Business Intelligence Developers, or Big Data Analysts in top-tier companies. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly higher. The program also aligns with certifications from leading industry players like Google, Microsoft, and AWS, enhancing career growth trajectories in the Indian market.
Student Success Practices
Foundation Stage
Master Core Programming & Math Fundamentals- (Semester 1-2)
Dedicate significant effort to building a strong foundation in C/C++, Python, data structures, and algorithms. Simultaneously, excel in engineering mathematics, particularly discrete mathematics and probability, as these are critical for understanding AI/DS concepts. Form study groups to solve complex problems and reinforce understanding.
Tools & Resources
GeeksforGeeks, HackerRank, Coursera/NPTEL for foundational math courses, Local study circles
Career Connection
Strong fundamentals are non-negotiable for cracking technical interviews at Indian IT firms and startups. They form the bedrock for advanced subjects and project implementation.
Develop Early Problem-Solving Skills through Coding Platforms- (Semester 1-2)
Regularly practice coding problems on platforms to improve logical thinking and algorithm implementation. Focus on competitive programming to develop speed and accuracy. Participate in university-level coding challenges to gain exposure and build a portfolio of solved problems.
Tools & Resources
CodeChef, LeetCode, HackerEarth, GitHub for personal code repositories
Career Connection
Excelling in competitive programming sharpens problem-solving abilities, a key skill sought by top product-based companies and tech startups in India during recruitment drives.
Engage in Interdisciplinary Learning & Communication- (Semester 1-2)
Beyond core subjects, actively participate in English communication classes to refine presentation and report writing skills. Join college clubs for debates, public speaking, or technical discussions to enhance soft skills, which are crucial for team projects and professional interactions.
Tools & Resources
Toastmasters International (local chapters), University Communication Labs, TED Talks for inspiration
Career Connection
Effective communication is vital for explaining technical concepts to non-technical stakeholders, leading teams, and succeeding in interviews and client-facing roles in India''''s diverse corporate landscape.
Intermediate Stage
Undertake Practical AI/DS Projects & Kaggle Competitions- (Semester 3-5)
Start building small-scale AI/DS projects using learned concepts, even if basic. Actively participate in Kaggle or similar data science competitions to apply theoretical knowledge to real-world datasets, collaborate with peers, and learn from experienced data scientists.
Tools & Resources
Kaggle, Google Colab, GitHub for project showcase, Medium/Towards Data Science blogs
Career Connection
A strong project portfolio and competition experience are highly valued by Indian companies, demonstrating practical application skills and initiative, significantly boosting internship and job prospects.
Pursue Industry-Relevant Certifications & Internships- (Semester 3-5)
Obtain certifications in popular AI/DS tools and platforms like TensorFlow, PyTorch, AWS/Azure AI services, or Tableau. Actively seek and complete internships during summer breaks with startups or established companies to gain industry exposure and network with professionals.
Tools & Resources
Coursera/edX for specialization certificates, LinkedIn Learning, Naukri/Internshala for internships
Career Connection
Certifications validate skills, making resumes stand out. Internships provide invaluable real-world experience, often leading to pre-placement offers (PPOs) in Indian companies, a crucial career launchpad.
Network with Professionals and Join Tech Communities- (Semester 3-5)
Attend local tech meetups, workshops, and industry conferences focused on AI and Data Science in Chennai or other Indian cities. Join online communities and forums to connect with professionals, learn about emerging trends, and seek mentorship. Build a professional presence on LinkedIn.
Tools & Resources
LinkedIn, Meetup.com for local tech events, Discord/Slack communities for AI/DS, Professional associations
Career Connection
Networking opens doors to hidden job opportunities, mentorship, and industry insights in the competitive Indian job market, offering a significant advantage over passive job searching.
Advanced Stage
Specialized Capstone Project & Research Publication- (Semester 6-8)
Undertake a significant capstone project in a niche area of AI/DS that aligns with your career interests, potentially collaborating with faculty or industry. Aim for publishing a research paper in a conference or journal, even a local one, to demonstrate deep expertise and research aptitude.
Tools & Resources
University Research Labs, arXiv, IEEE Xplore, Scopus for relevant publications
Career Connection
A high-impact capstone project and publication enhance your profile for specialized roles, higher studies (M.Tech/Ph.D.), or R&D positions in India, showcasing original contribution and advanced skills.
Intensive Placement Preparation and Mock Interviews- (Semester 6-8)
Engage in rigorous placement preparation focusing on technical interview questions, aptitude tests, and HR rounds. Participate in mock interviews conducted by the college placement cell or external agencies. Refine your resume and cover letter to highlight AI/DS skills and projects.
Tools & Resources
InterviewBit, Glassdoor, College Placement Cell, Professional interview coaches
Career Connection
Thorough preparation is paramount for securing placements in top companies during campus recruitment drives, ensuring you can articulate your technical knowledge and problem-solving approach effectively.
Explore Entrepreneurship and Innovation in AI- (Semester 6-8)
For those with an entrepreneurial spirit, explore opportunities to develop AI-driven solutions to real-world problems, potentially through startup incubators or university innovation cells. Learn about business models, intellectual property, and funding avenues in the Indian startup ecosystem.
Tools & Resources
NASSCOM 10,000 Startups, Startup India, Incubators within Saveetha, Entrepreneurship workshops
Career Connection
This path fosters innovation, leadership, and the ability to create new ventures, contributing to India''''s growing startup landscape and potentially leading to significant personal and professional impact.
Program Structure and Curriculum
Eligibility:
- A pass in H.Sc. (Academic) or Vocational or Equivalent with a minimum aggregate percentage (typically 45-50%) in Physics, Chemistry and Mathematics. Based on official norms, specific minimum scores in entrance exams like JEE/CET might also be required.
Duration: 4 years / 8 semesters
Credits: 165 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UMA1201 | Engineering Mathematics - I | Core | 4 | Differential Calculus, Integral Calculus, Matrices, Vector Calculus, Ordinary Differential Equations |
| UPH1202 | Engineering Physics | Core | 3 | Properties of Matter, Optics, Quantum Physics, Materials Science, Nanoscience |
| UCY1203 | Engineering Chemistry | Core | 3 | Water Technology, Electrochemistry, Corrosion and its Control, Polymer Chemistry, Energy Sources |
| UCS1204 | Programming for Problem Solving | Core | 3 | Programming Fundamentals, Control Structures, Functions, Arrays and Pointers, File Handling in C |
| UGI1205 | Engineering Graphics | Core | 3 | Plane Curves, Projections of Points and Lines, Projections of Solids, Section of Solids, Isometric and Perspective Views |
| UGE1206 | English for Engineers | Core | 2 | Listening and Speaking Skills, Reading Comprehension, Writing Skills, Grammar and Vocabulary, Presentation Techniques |
| UCS1207 | Programming for Problem Solving Lab | Lab | 2 | Conditional Statements and Loops, Functions and Arrays, Pointers and Structures, Strings and File Operations, Basic Algorithms Implementation |
| ULY1208 | Physics and Chemistry Lab | Lab | 2 | Properties of Materials, Optical Phenomena, Water Analysis, Volumetric Titrations, Spectroscopy |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UMA1251 | Engineering Mathematics - II | Core | 4 | Multivariable Calculus, Laplace Transforms, Fourier Series, Partial Differential Equations, Complex Analysis |
| UEC1252 | Basic Electrical and Electronics Engineering | Core | 3 | DC Circuits, AC Circuits, Semiconductor Devices, Digital Electronics, Transducers |
| UES1253 | Environmental Science and Engineering | Core | 3 | Ecosystems, Biodiversity, Environmental Pollution, Waste Management, Sustainable Development |
| UAD1254 | Data Structures using C++ | Core | 3 | Abstract Data Types, Linear Data Structures (Arrays, Linked Lists, Stacks, Queues), Non-Linear Data Structures (Trees, Graphs), Sorting Algorithms, Searching Algorithms |
| UCS1255 | Object Oriented Programming | Core | 3 | OOP Concepts (Classes, Objects, Inheritance, Polymorphism), Encapsulation and Abstraction, Exception Handling, Templates, File I/O |
| UGE1256 | Professional Communication | Core | 2 | Written Communication, Verbal Communication, Technical Report Writing, Presentation Skills, Interview Preparation |
| UAD1257 | Data Structures Lab | Lab | 2 | Implementation of Linear Data Structures, Implementation of Non-Linear Data Structures, Sorting and Searching Techniques, Hashing Techniques, Graph Algorithms |
| UCS1258 | Object Oriented Programming Lab | Lab | 2 | Classes and Objects, Constructors and Destructors, Inheritance and Polymorphism, Operator Overloading, File Operations |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UMA1301 | Discrete Mathematics | Core | 4 | Logic and Proofs, Set Theory, Relations and Functions, Graph Theory, Combinatorics |
| UAD1302 | Database Management Systems | Core | 3 | Relational Model, SQL Queries, ER Diagrams, Normalization, Transaction Management |
| UCS1303 | Computer Organization and Architecture | Core | 3 | Basic Computer Functions, CPU Organization, Memory System, Input/Output Organization, Pipelining and Parallelism |
| UAD1304 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| UAD1305 | Probability and Statistics for Data Science | Core | 3 | Random Variables and Distributions, Hypothesis Testing, Correlation and Regression, ANOVA, Chi-Square Test |
| UCS1306 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, File Systems, I/O Management |
| UAD1307 | Database Management Systems Lab | Lab | 2 | SQL Commands (DDL, DML, DCL), Joins and Subqueries, Stored Procedures and Functions, Database Connectivity (JDBC/ODBC), Mini Project on DBMS |
| UAD1308 | Algorithms Lab | Lab | 2 | Implementation of Sorting and Searching, Graph Traversal Algorithms, Dynamic Programming Problems, Greedy Algorithm Problems, Backtracking Algorithms |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAD1401 | Artificial Intelligence | Core | 3 | Intelligent Agents, Search Algorithms, Knowledge Representation, Logical Reasoning, Machine Learning Basics |
| UAD1402 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation and Selection |
| UAD1403 | Data Warehousing and Data Mining | Core | 3 | Data Warehousing Concepts, OLAP Operations, Data Preprocessing, Association Rule Mining, Clustering and Classification |
| UCS1404 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Data Link Layer Protocols, Routing Algorithms, Network Security Basics |
| UAD1405 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design, Software Testing, Project Management |
| UGE1406 | Professional Ethics | Core | 2 | Ethical Theories, Professionalism in Engineering, Cyber Ethics, Intellectual Property Rights, Global Issues |
| UAD1407 | Machine Learning Lab | Lab | 2 | Data Preprocessing, Linear Regression Implementation, Classification Algorithms (SVM, Decision Trees), Clustering Techniques (K-Means), Model Evaluation Metrics |
| UAD1408 | Data Mining Lab | Lab | 2 | WEKA Tool for Data Mining, Association Rule Mining Implementation, Classification Model Building, Clustering Algorithm Application, Data Visualization Techniques |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAD1501 | Deep Learning | Core | 3 | Neural Networks Fundamentals, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Architectures, Frameworks like TensorFlow/PyTorch |
| UAD1502 | Big Data Analytics | Core | 3 | Big Data Concepts, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark, NoSQL Databases, Big Data Streaming |
| UAD1503 | Natural Language Processing | Core | 3 | Text Preprocessing, N-grams and Language Models, Word Embeddings, Sequence Models, Sentiment Analysis |
| UAD15XX | Professional Elective - I | Elective | 3 | Advanced Topics in a chosen area (e.g., Reinforcement Learning), Specialized algorithms, Case studies, Practical applications, Emerging trends |
| UAD15YY | Professional Elective - II | Elective | 3 | Another specialized area (e.g., Computer Vision), Techniques and models, System design, Implementation challenges, Research directions |
| UAD1504 | Deep Learning Lab | Lab | 2 | TensorFlow/Keras/PyTorch Basics, CNN for Image Classification, RNN for Sequence Prediction, Transfer Learning, Hyperparameter Tuning |
| UAD1505 | Big Data Analytics Lab | Lab | 2 | Hadoop Ecosystem Setup, MapReduce Programming, Spark RDDs and DataFrames, NoSQL Database Operations, Big Data Project Implementation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAD1601 | Cloud Computing for Data Science | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Virtualization, Cloud Platforms (AWS, Azure, GCP), Cloud Storage and Databases, Data Science on Cloud |
| UAD1602 | Data Visualization | Core | 3 | Principles of Data Visualization, Tools (Tableau, PowerBI, Python libraries), Interactive Visualizations, Dashboard Design, Storytelling with Data |
| UAD1603 | Ethical AI and Responsible Data Science | Core | 3 | AI Ethics Principles, Bias and Fairness in AI, Privacy and Data Security, Transparency and Explainability, AI Regulations and Governance |
| UAD16ZZ | Professional Elective - III | Elective | 3 | Advanced Machine Learning, Time Series Analysis, Reinforcement Learning applications, Probabilistic Graphical Models, Bioinformatics for AI |
| UAD16AA | Professional Elective - IV | Elective | 3 | Speech Recognition, Image and Video Analytics, Generative AI, M.Sc. for AI/ML, Quantum Computing for AI |
| UAD1604 | Cloud and Data Visualization Lab | Lab | 2 | Deploying ML Models on Cloud, Using AWS Sagemaker/Azure ML Studio, Tableau/PowerBI Dashboard Creation, Python Plotting Libraries (Matplotlib, Seaborn), Interactive Visualizations |
| UAD1605 | Mini Project | Project | 3 | Problem Identification, Literature Survey, System Design and Implementation, Testing and Evaluation, Report Writing and Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAD1701 | Internship / Industrial Training | Project | 6 | Industry Work Exposure, Application of AI/DS Skills, Professional Communication, Teamwork and Project Management, Technical Report Submission |
| UAD17BB | Professional Elective - V | Elective | 3 | Advanced Data Science Architectures, Edge AI, MLOps, Explainable AI, Data Governance |
| UGE1702 | Open Elective - I | Elective | 3 | Interdisciplinary subject chosen by student, Skill Enhancement, Broadening perspectives, Non-technical skills, Career exploration |
| UAD1703 | Project Work - Phase I | Project | 6 | Problem Definition and Scope, Literature Review, System Architecture Design, Methodology Planning, Initial Implementation and Report |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAD1801 | Major Project Work | Project | 10 | System Implementation and Development, Testing and Validation, Results Analysis and Interpretation, Comprehensive Report Writing, Final Presentation and Viva Voce |
| UGE1802 | Professional Practice, Law and Ethics | Core | 3 | Professionalism and Ethics in AI/DS, Intellectual Property Law, Cyber Law, Entrepreneurship and Startups, Project Management Best Practices |
