.png&w=1920&q=75)
B-TECH in Artificial Intelligence Data Analytics at Sri Ramachandra Institute of Higher Education and Research
Chennai, Tamil Nadu
.png&w=1920&q=75)
About the Specialization
What is Artificial Intelligence & Data Analytics at Sri Ramachandra Institute of Higher Education and Research Chennai?
This Artificial Intelligence & Data Analytics program at Sri Ramachandra Institute of Higher Education and Research focuses on equipping students with advanced skills in AI, machine learning, and data science. Designed to meet the burgeoning demand in the Indian industry, the program differentiates itself by integrating robust theoretical foundations with practical, application-oriented learning. It provides a comprehensive understanding of data manipulation, analytical techniques, and the development of intelligent systems, preparing graduates for cutting-edge roles in a data-driven world.
Who Should Apply?
This program is ideal for aspiring engineers and innovators passionate about leveraging data and intelligent algorithms. It caters to fresh graduates seeking entry into the rapidly expanding fields of AI, data science, and machine learning, as well as working professionals looking to upskill or transition into data-centric roles. Candidates with a strong aptitude for mathematics, logical reasoning, and problem-solving, typically from a science or engineering background in their 10+2 education, will find this curriculum particularly rewarding.
Why Choose This Course?
Graduates of this program can expect to secure impactful roles such as AI Engineers, Data Scientists, Machine Learning Specialists, Business Intelligence Analysts, and Data Analysts in India. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning upwards of INR 15-25 LPA. The program aligns with industry-recognized certifications in AI/ML platforms and tools, fostering growth trajectories in leading Indian IT firms, startups, and analytics companies. It prepares students to innovate and solve complex real-world problems.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consistently practice coding in C/Python to build a strong foundation in data structures and algorithms. Participate in coding challenges regularly to enhance logical thinking and problem-solving skills.
Tools & Resources
HackerRank, LeetCode, CodeChef, GeeksforGeeks, NPTEL courses on Data Structures
Career Connection
Essential for clearing technical interviews for core engineering roles and building efficient solutions for AI/ML projects in the future.
Develop Strong Mathematical Acumen- (Semester 1-2)
Focus diligently on Engineering Mathematics, Probability, and Statistics. Understand concepts deeply rather than rote learning, as they are crucial for advanced AI/ML algorithms.
Tools & Resources
Khan Academy, NPTEL lectures, Specialized textbooks, Online problem sets for calculus and linear algebra
Career Connection
Forms the bedrock for understanding machine learning algorithms, statistical modeling, and data analytics techniques, critical for research and development roles.
Engage in Peer Learning and Study Groups- (Semester 1-2)
Form study groups with peers to discuss difficult concepts, solve problems collaboratively, and prepare for exams. Teaching concepts to others further solidifies your understanding.
Tools & Resources
Campus study rooms, Collaborative online whiteboards, Peer mentoring programs if available
Career Connection
Enhances problem-solving abilities, communication skills, and fosters a supportive learning environment, crucial for team-based industry projects.
Intermediate Stage
Build a Portfolio of Mini-Projects- (Semester 3-5)
Apply theoretical knowledge from AI, Machine Learning, and DBMS courses by building small, practical projects. Start with basic data analysis, then move to fundamental ML models.
Tools & Resources
Kaggle datasets, GitHub for version control, Python with libraries like Pandas, NumPy, Scikit-learn, MySQL
Career Connection
Demonstrates practical skills to potential employers, forms tangible proof of capabilities, and helps in gaining hands-on experience for internships.
Seek Early Industry Exposure through Internships/Workshops- (Semester 4-5)
Actively search for summer internships or participate in workshops/bootcamps offered by companies or professional bodies. Focus on understanding industry workflows and tools.
Tools & Resources
College placement cell, LinkedIn, Internshala, Industry events, company career pages
Career Connection
Provides real-world context, helps in networking, and can lead to pre-placement offers or full-time opportunities, giving a competitive edge.
Participate in Hackathons and Data Science Competitions- (Semester 4-5)
Join hackathons, data challenges, and online competitions to test your skills, learn from peers, and work under pressure. This hones problem-solving and teamwork.
Tools & Resources
Kaggle, Analytics Vidhya, GitHub, Collaborative coding platforms
Career Connection
Builds a strong resume, provides experience in diverse problem domains, and is highly valued by recruiters for showcasing practical application skills.
Advanced Stage
Specialize with Advanced Electives and Capstone Project- (Semester 6-8)
Choose professional electives wisely based on career interests (e.g., NLP, Computer Vision, Deep Reinforcement Learning). Dedicate significant effort to the major project, aiming for a novel solution or significant impact.
Tools & Resources
Advanced ML/DL frameworks (TensorFlow, PyTorch), Cloud platforms (AWS, Azure), Research papers (arXiv), Specialized libraries
Career Connection
Develops deep expertise in a chosen sub-field of AI/DA, making you a specialist for advanced roles and research opportunities.
Focus on Communication and Presentation Skills- (Semester 6-8)
Actively participate in seminars, project presentations, and technical writing. Practice explaining complex AI/ML concepts clearly and concisely to diverse audiences, both technical and non-technical.
Tools & Resources
Toastmasters clubs, College communication workshops, Mock interviews, LinkedIn Learning courses on presentation skills
Career Connection
Crucial for client interactions, team leadership, and effectively conveying project insights and technical solutions in professional settings and interviews.
Prepare for Placements and Professional Certifications- (Semester 7-8)
Begin focused preparation for placement drives, including resume building, mock interviews, and aptitude tests. Pursue relevant professional certifications (e.g., AWS Certified Machine Learning Specialty, Google Professional Data Engineer).
Tools & Resources
College placement cell, Online interview platforms (Pramp, InterviewBit), Industry certification providers, Networking events
Career Connection
Maximizes chances of securing desired jobs in top companies and adds a recognized credential that validates your skills to employers, enhancing marketability.
Program Structure and Curriculum
Eligibility:
- A pass in H.Sc. / CBSE / ISC (10+2) or equivalent examination with a minimum of 45% marks in Physics, Chemistry and Mathematics / Computer Science / Vocational subject with English as one of the subjects.
Duration: 8 semesters / 4 years
Credits: 166 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS23111 | Technical English - I | Core | 3 | Communication Skills, Grammar and Vocabulary, Reading Comprehension, Listening Skills, Writing Paragraphs and Essays |
| MA23101 | Engineering Mathematics - I | Core | 4 | Matrices, Differential Calculus, Functions of Several Variables, Multiple Integrals, Vector Calculus |
| PH23101 | Engineering Physics | Core | 3 | Properties of Matter, Optics, Quantum Physics, Crystal Physics, Material Science |
| CY23101 | Engineering Chemistry | Core | 3 | Water Technology, Electrochemistry and Corrosion, Phase Rule and Alloys, Fuels and Combustion, Green Chemistry |
| CS23101 | Programming for Problem Solving | Core | 3 | Problem Solving Methodologies, C Programming Fundamentals, Control Structures, Functions and Pointers, Arrays and Strings |
| ES23101 | Engineering Graphics | Lab | 2 | Engineering Curves, Orthographic Projections, Sectional Views, Isometric Projections, Perspective Projections |
| CS23102 | Programming for Problem Solving Lab | Lab | 2 | C Program Debugging, Conditional and Loop Structures, Function Implementation, Array Manipulation, Pointer Operations |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS23211 | Technical English - II | Core | 3 | Advanced Communication Skills, Technical Report Writing, Presentation Skills, Group Discussion Techniques, Interview Preparation |
| MA23201 | Engineering Mathematics - II | Core | 4 | Ordinary Differential Equations, Laplace Transforms, Vector Spaces, Eigenvalue Problems, Complex Variables |
| PH23201 | Materials Science for Engineers | Core | 3 | Electrical Materials, Magnetic Materials, Dielectric Materials, Nanomaterials, Smart Materials |
| CY23201 | Environmental Science and Engineering | Core | 3 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Social Issues and Environment, Human Population and Environment |
| CS23201 | Data Structures | Core | 3 | Abstract Data Types, Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting, Searching, and Hashing |
| EE23201 | Basic Electrical and Electronics Engineering | Core | 2 | DC Circuit Analysis, AC Circuit Analysis, Transformers and Motors, Semiconductor Diodes, Transistors and Amplifiers |
| CS23202 | Data Structures Lab | Lab | 2 | Implementation of Linked Lists, Stack and Queue Operations, Binary Tree Traversal, Graph Algorithms, Sorting and Searching Algorithms |
| EE23202 | Basic Electrical and Electronics Engineering Lab | Lab | 1 | Ohm''''s Law and KVL/KCL Verification, PN Junction Diode Characteristics, Transistor Amplifier Circuits, Digital Logic Gates, CRO Applications |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA23301 | Probability and Statistics for Data Analytics | Core | 4 | Probability Theory, Random Variables and Distributions, Sampling Distributions, Hypothesis Testing, Correlation and Regression Analysis |
| AI23301 | Computer Architecture and Organization | Core | 3 | Data Representation, CPU Organization and Design, Control Unit Design, Memory Hierarchy, Input/Output Organization |
| AI23302 | Object Oriented Programming and Design | Core | 3 | OOP Concepts (Encapsulation, Inheritance), Polymorphism and Abstraction, Exception Handling, File I/O and Streams, UML Diagrams and Design Patterns |
| AI23303 | Design and Analysis of Algorithms | Core | 4 | Algorithmic Paradigms, Sorting and Searching Algorithms, Graph Algorithms, Dynamic Programming, Greedy Algorithms |
| AI23304 | Database Management Systems | Core | 3 | Data Models, Relational Algebra and Calculus, SQL Queries and Constraints, Normalization, Transaction Management and Concurrency Control |
| AI23305 | Object Oriented Programming and Design Lab | Lab | 2 | Class and Object Implementation, Inheritance and Polymorphism, Interface and Abstract Class Usage, Exception Handling, GUI Programming (Basic) |
| AI23306 | Database Management Systems Lab | Lab | 2 | SQL Data Definition and Manipulation, Advanced SQL Queries, Database Design and Normalization, Stored Procedures and Functions, Trigger Implementation |
| AI23307 | Constitution of India and Essence of Indian Traditional Knowledge | Mandatory Course | 0 | Indian Constitution, Fundamental Rights and Duties, Indian Knowledge Systems, Yoga and Ayurveda, Traditional Indian Arts and Literature |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI23401 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Deadlocks, Memory Management, File Systems and I/O Systems |
| AI23402 | Artificial Intelligence | Core | 3 | Introduction to AI Agents, Heuristic Search Techniques, Knowledge Representation, Logical Reasoning (Propositional and First-Order), Planning and Uncertainty |
| AI23403 | Computer Networks | Core | 3 | Network Models (OSI, TCP/IP), Physical Layer and Data Link Layer, Network Layer Protocols, Transport Layer Protocols, Application Layer Services |
| AI23404 | Machine Learning | Core | 4 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Validation, Ensemble Methods, Feature Engineering |
| AI23405 | Data Warehousing and Data Mining | Core | 3 | Data Warehouse Architecture, OLAP Operations, Data Preprocessing, Association Rule Mining, Classification and Clustering Techniques |
| AI23406 | Operating Systems Lab | Lab | 2 | Linux Commands and Shell Scripting, Process Management Programs, CPU Scheduling Algorithms, Memory Management Techniques, Deadlock Avoidance and Prevention |
| AI23407 | Machine Learning Lab | Lab | 2 | Python Libraries (Numpy, Pandas, Scikit-learn), Data Preprocessing and Visualization, Implementing Regression Models, Implementing Classification Models, Clustering Algorithms |
| AI23408 | Universal Human Values | Mandatory Course | 0 | Introduction to Value Education, Harmony in the Human Being, Harmony in Family and Society, Harmony in Nature, Professional Ethics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI23501 | Deep Learning | Core | 4 | Neural Network Architectures, Backpropagation and Optimization, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Models and Autoencoders |
| AI23502 | Big Data Analytics | Core | 3 | Big Data Characteristics and Challenges, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark, NoSQL Databases (Cassandra, MongoDB), Stream Processing |
| AI23503 | Cloud Computing | Core | 3 | Cloud Computing Models (IaaS, PaaS, SaaS), Virtualization Technologies, Cloud Security and Data Privacy, Major Cloud Providers (AWS, Azure, GCP), Serverless Computing |
| AI23504 | Professional Elective - I | Elective Slot | 3 | Selection from the list of available professional electives, Focus on specialized areas of AI and Data Analytics |
| AI23505 | Professional Elective - II | Elective Slot | 3 | Selection from the list of available professional electives, Advanced topics in AI, Machine Learning, or Data Science |
| AI23506 | Deep Learning Lab | Lab | 2 | TensorFlow/PyTorch Implementation, Building CNNs for Image Classification, Implementing RNNs for Sequence Data, Hyperparameter Tuning, Transfer Learning |
| AI23507 | Big Data Analytics Lab | Lab | 2 | HDFS Operations, MapReduce Programming, Spark RDDs and DataFrames, Hive and Pig Scripting, NoSQL Database Interaction |
| AI23508 | Project Work - I (Minor Project) | Project | 3 | Problem Definition and Scope, Literature Survey, System Design, Implementation and Testing, Project Report and Presentation |
| AI23PE01 | Natural Language Processing | Professional Elective Option | 3 | Text Preprocessing, N-grams and Language Models, Word Embeddings (Word2Vec, GloVe), POS Tagging and Named Entity Recognition, Sentiment Analysis and Machine Translation |
| AI23PE02 | Computer Vision | Professional Elective Option | 3 | Image Processing Fundamentals, Feature Extraction (SIFT, HOG), Object Detection and Recognition, Image Segmentation, Deep Learning for Computer Vision |
| AI23PE03 | Reinforcement Learning | Professional Elective Option | 3 | Markov Decision Processes (MDPs), Value and Policy Iteration, Q-Learning and SARSA, Deep Reinforcement Learning, Exploration-Exploitation Trade-off |
| AI23PE04 | Speech and Audio Processing | Professional Elective Option | 3 | Speech Production and Perception, Acoustic Features (MFCC, Spectrogram), Speech Recognition Systems, Speaker Recognition, Text-to-Speech Synthesis |
| AI23PE05 | Time Series Analysis and Forecasting | Professional Elective Option | 3 | Time Series Components, ARIMA and SARIMA Models, Exponential Smoothing, Granger Causality, Forecasting Techniques and Evaluation |
| AI23PE06 | Recommender Systems | Professional Elective Option | 3 | Collaborative Filtering, Content-Based Filtering, Hybrid Recommender Systems, Matrix Factorization, Evaluation Metrics for Recommenders |
| AI23PE07 | Business Intelligence | Professional Elective Option | 3 | Data Warehousing Concepts, ETL Processes, OLAP and OLTP, Dashboards and Reporting, Data Visualization for Business Insights |
| AI23PE08 | Social Network Analysis | Professional Elective Option | 3 | Graph Theory Fundamentals, Centrality Measures, Community Detection Algorithms, Network Evolution Models, Link Prediction and Influence Maximization |
| AI23PE09 | Robotic Process Automation | Professional Elective Option | 3 | RPA Fundamentals and Concepts, Process Discovery and Analysis, RPA Tools (e.g., UiPath, Automation Anywhere), Bot Development and Deployment, Attended vs. Unattended Automation |
| AI23PE10 | Quantum Computing | Professional Elective Option | 3 | Quantum Bits (Qubits), Superposition and Entanglement, Quantum Gates and Circuits, Quantum Algorithms (Shor''''s, Grover''''s), Quantum Cryptography |
| AI23PE11 | Cognitive Science and AI | Professional Elective Option | 3 | Introduction to Cognitive Science, Perception and Attention, Memory and Learning, Problem Solving and Decision Making, AI Models of Cognition |
| AI23PE12 | Blockchain Technology | Professional Elective Option | 3 | Cryptography and Hashing, Distributed Ledger Technology, Consensus Mechanisms, Smart Contracts, Blockchain Platforms (Bitcoin, Ethereum) |
| AI23PE13 | IoT Analytics | Professional Elective Option | 3 | IoT Architecture and Protocols, Sensor Data Acquisition, Edge and Fog Computing, Stream Analytics for IoT, IoT Data Storage and Security |
| AI23PE14 | Explainable AI | Professional Elective Option | 3 | Interpretability vs. Explainability, Local and Global Explanation Methods, LIME and SHAP Techniques, Causal Inference in AI, Ethical Considerations in AI |
| AI23PE15 | Graph Analytics | Professional Elective Option | 3 | Graph Data Structures, Graph Traversal Algorithms, Centrality and PageRank, Community Detection, Knowledge Graphs and Graph Embeddings |
| AI23PE16 | Financial Analytics | Professional Elective Option | 3 | Financial Markets and Instruments, Risk Management, Portfolio Optimization, Algorithmic Trading Strategies, Predictive Models in Finance |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI23601 | Data Visualization and Storytelling | Core | 3 | Principles of Data Visualization, Types of Charts and Graphs, Dashboard Design, Storytelling with Data, Tools like Tableau, Power BI, Python libraries |
| AI23602 | DevOps for AI/ML | Core | 3 | DevOps Principles and Practices, CI/CD for Machine Learning, MLOps Concepts, Version Control for Data and Models, Containerization (Docker) and Orchestration (Kubernetes) |
| AI23603 | Information Security and Cryptography | Core | 3 | Security Threats and Vulnerabilities, Symmetric Key Cryptography, Asymmetric Key Cryptography, Hashing and Digital Signatures, Network Security Concepts |
| AI23604 | Professional Elective - III | Elective Slot | 3 | Selection from the list of available professional electives, Further specialization in advanced AI/DA domains |
| AI23605 | Professional Elective - IV | Elective Slot | 3 | Selection from the list of available professional electives, Deep dive into specific AI or Data Analytics applications |
| AI23606 | Data Visualization Lab | Lab | 2 | Practical with Tableau/Power BI, Creating Interactive Dashboards, Exploratory Data Analysis using Visuals, Designing Infographics, Customizing Visualizations |
| AI23607 | Minor Project – II | Project | 3 | Problem Analysis and Scoping, Solution Design and Architecture, Prototyping and Development, Testing and Debugging, Technical Report and Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI23701 | Ethics in AI and Data Analytics | Core | 3 | AI Bias and Fairness, Accountability and Transparency in AI, Data Privacy and Governance, Ethical Frameworks for AI Development, Societal Impact of AI |
| AI23702 | Industrial Training / Internship (4-6 weeks) | Internship | 2 | On-the-job Training, Industry Practices and Workflow, Application of Academic Knowledge, Technical Report Writing, Presentation of Internship Experience |
| AI23703 | Professional Elective - V | Elective Slot | 3 | Selection from the list of available professional electives, Advanced concepts in specialized AI/DA areas |
| AI23704 | Professional Elective - VI | Elective Slot | 3 | Selection from the list of available professional electives, Deep understanding of a niche area in AI/DA |
| AI23705 | Open Elective - I | Elective | 3 | As per chosen elective from university-wide list, Interdisciplinary or general knowledge topics |
| AI23706 | Seminar | Project | 1 | Literature Review and Research Paper Analysis, Technical Presentation Skills, Report Writing on Emerging Technologies, Communication and Discussion |
| AI23707 | Project Work - III | Project | 6 | Advanced Problem Identification, Detailed System Design, Implementation with Robust Techniques, Comprehensive Testing and Validation, Viva-voce Examination |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI23801 | Project Work - IV (Major Project) | Project | 10 | Research and Innovation, Full-scale System Development, Project Management, Advanced Problem Solving, Comprehensive Technical Report and Defence |
| AI23802 | Open Elective - II | Elective | 2 | As per chosen elective from university-wide list, Broadening knowledge beyond the specialization |
