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B-SC in Artificial Intelligence And Data Science at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Artificial Intelligence and Data Science at SRM Institute of Science and Technology Chengalpattu?
This B.Sc. Artificial Intelligence and Data Science program at SRM Institute of Science and Technology focuses on equipping students with a robust foundation in AI/ML algorithms, data analytics, and statistical modeling. Reflecting India''''s booming digital economy, the curriculum is designed to produce professionals capable of handling complex datasets and building intelligent systems, aligning with the country''''s thrust towards technological innovation and data-driven decision making across sectors.
Who Should Apply?
This program is ideal for fresh graduates with a strong mathematical and scientific aptitude seeking entry into the high-demand fields of AI and Data Science. It also caters to aspiring data scientists, machine learning engineers, and business intelligence analysts. Students with a 10+2 background, particularly those with a foundation in Mathematics and Physics, who are keen on problem-solving with data, would find this specialization rewarding.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths as Data Analysts, AI Engineers, Machine Learning Specialists, or Business Intelligence Developers. Entry-level salaries typically range from INR 4-7 lakhs per annum, with experienced professionals earning significantly more in leading tech hubs like Bangalore, Hyderabad, and Pune. The program prepares students for industry certifications and roles in both startups and established IT firms.
Student Success Practices
Foundation Stage
Master Programming and Math Fundamentals- (Semester 1-2)
Dedicate significant time to mastering Python programming and foundational mathematics (Calculus, Linear Algebra, Probability, Statistics). Practice coding daily on platforms like HackerRank or LeetCode, and solve mathematical problems rigorously to build a strong analytical base.
Tools & Resources
Python IDE (VS Code, Jupyter Notebook), NumPy, Pandas, Online courses (Coursera, NPTEL for math refreshers), HackerRank, LeetCode
Career Connection
A solid grasp of programming and mathematics is non-negotiable for AI/DS roles, forming the bedrock for understanding algorithms and statistical models, directly impacting your ability to solve complex data problems in future roles.
Build a Strong Data Structures and Algorithms Base- (Semester 1-2)
Actively practice implementing various data structures (arrays, linked lists, trees, graphs) and algorithms (sorting, searching, dynamic programming). This critical thinking skill is fundamental for efficient data processing and algorithm design in AI/DS. Participate in competitive programming challenges.
Tools & Resources
GeeksforGeeks, CodeChef, TopCoder, Visual Algo
Career Connection
Interview processes for major tech companies in India heavily focus on DSA, making this skill crucial for securing coveted positions as a software or AI engineer.
Initiate Basic Data Exploration Projects- (Semester 1-2)
Start working on small, personal data projects using publicly available datasets (e.g., Kaggle). Focus on data cleaning, exploratory data analysis, and basic visualization. This hands-on experience translates theoretical knowledge into practical skills early on.
Tools & Resources
Kaggle, Google Colab, Matplotlib, Seaborn, Tableau Public (free version)
Career Connection
Early project experience demonstrates initiative and a practical understanding of the data science pipeline, making your profile more attractive to recruiters for internships and entry-level roles.
Intermediate Stage
Engage in Machine Learning and Deep Learning Projects- (Semester 3-5)
Apply learned ML/DL algorithms to real-world problems. Develop projects involving classification, regression, clustering, and neural networks. Experiment with different models and frameworks to understand their strengths and limitations. Participate in hackathons.
Tools & Resources
TensorFlow, Keras, PyTorch, Scikit-learn, Hugging Face, Google Colab, GPU access (via cloud if needed)
Career Connection
Building a portfolio of substantial ML/DL projects is vital for showcasing your expertise, a key requirement for roles like Machine Learning Engineer, Data Scientist, and AI Researcher in Indian tech companies.
Seek Industry Internships and Certifications- (Semester 3-5)
Actively pursue internships in AI/Data Science at startups or established companies during summer breaks. Simultaneously, consider pursuing relevant industry certifications (e.g., AWS Certified Machine Learning Specialty, Google Cloud Professional Data Engineer) to validate your skills.
Tools & Resources
Internshala, LinkedIn, College career services, Official certification websites (AWS, Google Cloud)
Career Connection
Internships provide invaluable practical exposure and networking opportunities, often leading to pre-placement offers. Certifications enhance credibility and improve job prospects in a competitive Indian market.
Develop Strong Communication and Presentation Skills- (Semester 3-5)
Beyond technical prowess, focus on articulating complex technical concepts clearly. Participate in college clubs, conduct workshops, and present your project work regularly. Practice explaining your data insights and algorithm choices effectively.
Tools & Resources
Toastmasters International (if available), Departmental seminars, Mock presentations, Peer feedback
Career Connection
Effective communication is crucial for data scientists and AI professionals to convey findings to non-technical stakeholders, collaborate in teams, and excel in client-facing roles within Indian organizations.
Advanced Stage
Undertake a Capstone Project or Industry Internship- (Semester 6)
Engage in a significant capstone project (or extend your final semester project) addressing a real-world problem, ideally in collaboration with an industry partner. Focus on end-to-end solution development, deploying models, and evaluating impact, preparing for industry readiness.
Tools & Resources
Enterprise-grade platforms, Version control (Git), Cloud platforms for deployment, Project management tools
Career Connection
A robust capstone project demonstrating practical problem-solving and deployment skills is often a deciding factor for placements, showcasing readiness for an industry role in India.
Master Interview Preparation and Networking- (Semester 6)
Start rigorous interview preparation focusing on technical questions (ML algorithms, data structures, SQL, system design) and behavioral aspects. Attend industry conferences, workshops, and alumni meetups to network with professionals and explore career opportunities.
Tools & Resources
LeetCode, HackerRank, GeeksforGeeks, Glassdoor for company-specific interview experiences, LinkedIn for networking
Career Connection
Strategic interview preparation and an active professional network are critical for securing top placements and navigating the job market effectively in India''''s competitive AI/DS landscape.
Explore Niche Specializations and Research Opportunities- (Semester 6 and beyond)
Delve deeper into a specific sub-field of AI/DS that aligns with your interests (e.g., Computer Vision, NLP, Reinforcement Learning, MLOps, Ethical AI). Consider pursuing research projects or publishing papers if you are inclined towards higher studies or R&D roles.
Tools & Resources
Research papers (arXiv, Google Scholar), Advanced online courses, Specialized libraries/frameworks (e.g., OpenCV, spaCy)
Career Connection
Specializing makes you a valuable expert in a niche area, opening doors to advanced research roles, highly specialized industry positions, or postgraduate studies in India or abroad.
Program Structure and Curriculum
Eligibility:
- A Pass in Higher Secondary Examination (10+2 pattern) or its equivalent, with 60% aggregate in Physics, Mathematics, and any one of the following subjects: Chemistry/ Computer Science/ Statistics/ Biology/ Biotechnology/ Engineering Drawing.
Duration: 3 years (6 semesters)
Credits: 135 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21ADS101J | Programming in Python | Core | 4 | Introduction to Python, Data Types and Operators, Control Flow, Functions and Modules, Object-Oriented Programming, File Handling |
| 21MA105J | Calculus and Linear Algebra for AI & DS | Core | 4 | Differential Calculus, Integral Calculus, Matrices and Determinants, Vector Spaces, Eigenvalues and Eigenvectors |
| 21ADSA101T | Data Science Fundamentals | Core | 3 | Introduction to Data Science, Data Collection, Data Preprocessing, Data Visualization, Introduction to Machine Learning |
| 21CY101J | Chemistry for Computer Science | Core | 3 | Water Technology, Electrochemistry, Polymers, Phase Rule, Green Chemistry |
| 21HS101T | Communicative English | Core | 3 | Reading Comprehension, Writing Skills, Grammar and Vocabulary, Listening and Speaking, Presentation Skills |
| 21ADSL101L | Programming in Python Lab | Lab | 2 | Python basic syntax, Conditional statements, Loops, Functions, Data structures |
| 21ADSL102L | Data Science Fundamentals Lab | Lab | 2 | Data preprocessing using Python, Data visualization tools, Exploratory Data Analysis, Basic statistical analysis |
| 21PD101L | Soft Skills I | Skill Elective | 2 | Communication Skills, Personality Development, Goal Setting, Time Management, Teamwork |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21ADS102J | Data Structures and Algorithms | Core | 4 | Arrays, Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms |
| 21MA106J | Probability and Statistics for AI & DS | Core | 4 | Probability Theory, Random Variables and Distributions, Joint Probability Distributions, Hypothesis Testing, Correlation and Regression |
| 21ADSA102T | Database Management Systems | Core | 3 | Introduction to DBMS, Relational Model, Structured Query Language (SQL), Entity-Relationship Model, Normalization, Query Processing and Optimization |
| 21PH101J | Physics for Computer Science | Core | 3 | Quantum Mechanics, Solid State Physics, Semiconductor Devices, Lasers and Fiber Optics, Nanomaterials and Applications |
| 21ADSA103T | Fundamentals of Artificial Intelligence | Core | 3 | Introduction to AI, Intelligent Agents, Problem Solving and Search Algorithms, Knowledge Representation and Reasoning, Introduction to Machine Learning |
| 21ADSL103L | Data Structures and Algorithms Lab | Lab | 2 | Implementation of arrays, linked lists, Stack and queue operations, Tree traversals, Graph algorithms, Sorting and searching implementations |
| 21ADSL104L | Database Management Systems Lab | Lab | 2 | SQL DDL and DML commands, Advanced SQL queries, Database design and creation, Stored procedures and functions, Triggers and views |
| 21PD102L | Soft Skills II | Skill Elective | 2 | Interview Skills, Group Discussions, Presentation Skills, Conflict Resolution, Professional Etiquette |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21ADS201J | Object Oriented Programming with Java | Core | 4 | OOP Concepts, Classes, Objects, Methods, Inheritance and Polymorphism, Exception Handling, Collections Framework, Multithreading |
| 21ADS202J | Computer Networks | Core | 4 | Network Topologies, OSI and TCP/IP Models, Network Devices, Routing Protocols, Transport Layer Protocols, Network Security Basics |
| 21ADSA201T | Operating Systems | Core | 3 | OS Concepts, Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| 21ADSA202T | Introduction to Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression Algorithms, Classification Algorithms, Clustering Algorithms, Model Evaluation and Validation |
| 21ADSL201L | Object Oriented Programming with Java Lab | Lab | 2 | Java program implementation, OOP principles application, File I/O operations, GUI applications, JDBC connectivity |
| 21ADSL202L | Introduction to Machine Learning Lab | Lab | 2 | Scikit-learn usage, Implementing regression models, Implementing classification models, Clustering algorithms, Data preprocessing and feature engineering |
| 21ADSL203L | Mini Project | Project | 2 | Project Planning, Requirements Analysis, Design and Development, Implementation and Testing, Documentation and Presentation |
| 21ADSAE01 / 21ADSAE02 | Generic Elective / Ability Enhancement Course I | Generic Elective / Ability Enhancement | 3 | Students choose one from approved list., Examples include: Universal Human Values (21ADSAE01), Environmental Science and Sustainability (21ADSAE02) |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21ADS203J | Web Technology | Core | 4 | HTML and CSS, JavaScript Programming, DOM Manipulation, Web Servers and Protocols, Client-Side Scripting, Introduction to Web Frameworks |
| 21ADS204J | Big Data Analytics | Core | 4 | Introduction to Big Data, Hadoop Ecosystem, HDFS and MapReduce, Apache Spark, NoSQL Databases, Data Warehousing Concepts |
| 21ADSA203T | Deep Learning | Core | 3 | Neural Network Fundamentals, Activation Functions and Optimizers, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning |
| 21ADSPE01 / 21ADSPE02 / 21ADSPE03 | Professional Elective I | Professional Elective | 3 | Students choose one from approved list., Examples include: Web Development Fundamentals (21ADSPE01), Mobile Application Development (21ADSPE02), Cyber Security Fundamentals (21ADSPE03) |
| 21ADSL204L | Web Technology Lab | Lab | 2 | HTML/CSS page creation, JavaScript interactive elements, Form validation, Responsive web design, Simple web application development |
| 21ADSL205L | Big Data Analytics Lab | Lab | 2 | Hadoop environment setup, MapReduce programming, Spark applications, Hive query language, NoSQL database operations |
| 21ADSL206L | Deep Learning Lab | Lab | 2 | TensorFlow/Keras implementation, Training neural networks, Building CNN models, Implementing RNNs, Image classification tasks |
| 21ADSAE03 / 21ADSAE04 | Generic Elective / Ability Enhancement Course II | Generic Elective / Ability Enhancement | 3 | Students choose one from approved list., Examples include: Indian Constitution (21ADSAE03), Sustainable Development Goals (21ADSAE04) |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21ADS301J | Data Visualization | Core | 4 | Principles of Data Visualization, Exploratory Data Analysis, Tableau/Power BI Basics, Matplotlib and Seaborn, Interactive Dashboards, Storytelling with Data |
| 21ADS302J | Natural Language Processing | Core | 4 | NLP Fundamentals, Text Preprocessing, Word Embeddings (Word2Vec, GloVe), Sentiment Analysis, Text Classification, Sequence Models (RNN, LSTM) |
| 21ADSA301T | Cloud Computing for AI & DS | Core | 3 | Cloud Computing Concepts, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, AWS/Azure/GCP Fundamentals, Cloud Storage and Databases, Serverless Computing |
| 21ADSPE04 / 21ADSPE05 / 21ADSPE06 | Professional Elective II | Professional Elective | 3 | Students choose one from approved list., Examples include: Cloud Computing (21ADSPE04), IoT Fundamentals (21ADSPE05), Game Development (21ADSPE06) |
| 21ADSL301L | Data Visualization Lab | Lab | 2 | Tableau/Power BI dashboard creation, Python visualization libraries, Interactive plots, Infographics design, Data insights presentation |
| 21ADSL302L | Natural Language Processing Lab | Lab | 2 | NLTK and spaCy usage, Text preprocessing techniques, Sentiment analysis implementation, Chatbot development basics, Named Entity Recognition |
| 21ADSL303L | Cloud Computing for AI & DS Lab | Lab | 2 | AWS/Azure/GCP VM deployment, Cloud storage services, Serverless functions implementation, Using cloud AI/ML services, Containerization with Docker |
| 21ADSPE07 / 21ADSPE08 / 21ADSPE09 | Professional Elective III | Professional Elective | 3 | Students choose one from approved list., Examples include: Cyber Physical Systems (21ADSPE07), Augmented Reality/Virtual Reality (21ADSPE08), Blockchain Technologies (21ADSPE09) |
| 21ADSAE05 / 21ADSAE06 | Generic Elective / Ability Enhancement Course III | Generic Elective / Ability Enhancement | 3 | Students choose one from approved list., Examples include: Startup and Innovation (21ADSAE05), Disaster Management (21ADSAE06) |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21ADS303J | AI Ethics and Governance | Core | 4 | Ethical AI Principles, Bias and Fairness in AI, Data Privacy and Security, AI Regulations and Policies, Explainable AI (XAI), Societal Impact of AI |
| 21ADSPE10 / 21ADSPE11 / 21ADSPE12 | Professional Elective IV | Professional Elective | 3 | Students choose one from approved list., Examples include: Reinforcement Learning (21ADSPE10), Computer Vision (21ADSPE11), Explainable AI (21ADSPE12) |
| 21ADSPE13 / 21ADSPE14 / 21ADSPE15 | Professional Elective V | Professional Elective | 3 | Students choose one from approved list., Examples include: Robotics Process Automation (21ADSPE13), Quantum Computing (21ADSPE14), Edge AI (21ADSPE15) |
| 21ADSS304P | Project Work / Internship | Project/Internship | 10 | Problem Identification, Literature Review, Methodology Design, Implementation and Testing, Report Writing and Presentation, Project Deployment |
