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B-SC-COMPUTER-SCIENCE-ARTIFICIAL-INTELLIGENCE-AND-DATA-ANALYTICS in General at Sri Ramachandra Institute of Higher Education and Research
Chennai, Tamil Nadu
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About the Specialization
What is General at Sri Ramachandra Institute of Higher Education and Research Chennai?
This Artificial Intelligence and Data Analytics program at Sri Ramachandra Institute of Higher Education and Research focuses on equipping students with advanced skills in AI, machine learning, and big data technologies. It is designed to meet the growing demand in the Indian IT sector for professionals capable of driving data-driven innovations and intelligent automation across various industries. The program blends theoretical knowledge with practical applications.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and computer science, seeking entry into the rapidly expanding AI and data science domains in India. It also suits working professionals who wish to upskill or career changers transitioning into roles like Data Scientist, AI Engineer, or Business Intelligence Analyst, leveraging their existing technical background for a specialized career path.
Why Choose This Course?
Graduates of this program can expect diverse career paths in Indian companies, including roles such as AI Developer, Machine Learning Engineer, Data Analyst, and Business Intelligence Consultant. Entry-level salaries typically range from INR 4-7 lakhs per annum, with significant growth trajectories for experienced professionals potentially reaching INR 15-30 lakhs or more. The curriculum prepares students for industry certifications and higher studies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Actively engage in Python programming challenges on platforms like HackerRank and LeetCode to solidify basic concepts and data structures. Dedicate time daily to practice coding problems beyond classroom assignments, focusing on algorithmic thinking and efficient code writing.
Tools & Resources
CodeChef, GeeksforGeeks, HackerRank, LeetCode, Python documentation
Career Connection
Strong programming fundamentals are essential for all tech roles and crucial for clearing technical interviews during placements, forming the base for advanced AI/ML implementations.
Build a Strong Mathematical Base- (Semester 1-2)
Consistently review Calculus, Linear Algebra, and Discrete Mathematics concepts. Utilize online tutorials and practice problems to develop a deep understanding of core mathematical principles, as they are the backbone of AI and Data Analytics algorithms.
Tools & Resources
Khan Academy, NPTEL videos, MIT OpenCourseWare, Schaum''''s Outlines
Career Connection
A solid mathematical foundation is critical for understanding, developing, and optimizing complex AI and Machine Learning models, as well as for advanced statistical analysis in data science.
Engage in Peer Learning and Study Groups- (Semester 1-2)
Form study groups with classmates to discuss challenging topics, solve problems collaboratively, and prepare for exams. Active discussion and teaching others reinforce learning, while collaborating on assignments helps develop essential teamwork skills.
Tools & Resources
WhatsApp groups, Google Meet, shared whiteboards, collaborative coding platforms
Career Connection
Enhances problem-solving abilities, communication skills, and prepares students for the collaborative work environments prevalent in the Indian IT and data science industries.
Intermediate Stage
Undertake Mini-Projects and Kaggle Competitions- (Semester 3-5)
Apply learned concepts in DBMS, Java, Data Science, and AI by working on self-initiated mini-projects and participating in online data science competitions. Build a portfolio of practical projects demonstrating skills in data manipulation, model building, and problem-solving.
Tools & Resources
Kaggle, GitHub, Jupyter Notebooks, Google Colab, Tableau Public
Career Connection
Showcases practical skills and analytical thinking to recruiters, helps in understanding real-world data challenges, and significantly enhances the resume for internships and placements.
Seek Industry Internships and Workshops- (Semester 4-5)
Actively search for summer internships or participate in workshops and bootcamps focused on AI, ML, or Big Data. Gain practical industry exposure, understand real-world workflows, and network with professionals in Indian companies offering these opportunities.
Tools & Resources
Internshala, LinkedIn Jobs, college placement cell, company career pages, industry conferences
Career Connection
Provides invaluable practical experience, often leads to pre-placement offers (PPOs), clarifies career interests, and builds a professional network crucial for future career growth.
Specialize in Key AI/ML Frameworks- (Semester 4-5)
Develop hands-on proficiency in popular libraries and frameworks such as TensorFlow, Keras, PyTorch, and scikit-learn. Work through official tutorials and build small projects using these tools beyond classroom assignments to master their practical application.
Tools & Resources
Official framework documentation, Coursera, Udemy, edX, GitHub repositories for examples
Career Connection
Expertise in industry-standard tools is a primary requirement for AI/ML engineer roles, making candidates highly employable and proficient in developing real-world intelligent systems.
Advanced Stage
Focus on a Capstone Project with Industry Relevance- (Semester 6)
Dedicate significant effort to the final year project, choosing a problem with real-world impact, potentially in collaboration with an industry partner or research lab. Aim for innovation, robust implementation, and thorough documentation, seeking faculty mentorship.
Tools & Resources
Advanced AI/ML libraries, cloud platforms (AWS, Azure, GCP), project management tools, research papers
Career Connection
A well-executed and industry-relevant capstone project is a strong talking point in interviews, demonstrating problem-solving capabilities and potentially leading directly to job offers or entrepreneurial ventures in India.
Intensive Placement Preparation and Mock Interviews- (Semester 6)
Engage in rigorous preparation for campus placements, including aptitude tests, technical rounds focused on AI/ML and data structures, and HR interviews. Practice coding, revise core computer science concepts, and participate in mock interviews and group discussions.
Tools & Resources
InterviewBit, LeetCode, GeeksforGeeks, resume building workshops, LinkedIn for company research, mock interview platforms
Career Connection
Directly impacts placement success, enabling students to secure desirable roles with leading Indian and multinational companies by being fully prepared for all stages of the recruitment process.
Continuous Learning and Staying Updated- (Throughout the program, intensified in Semester 6)
Cultivate a habit of lifelong learning by regularly following industry blogs, research papers, and online courses to stay abreast of the latest advancements in AI and Data Analytics. Participate in online forums and attend webinars to understand emerging trends.
Tools & Resources
Towards Data Science, arXiv.org, Google AI Blog, LinkedIn Learning, Coursera, NPTEL
Career Connection
Ensures long-term career growth, adaptability to new technologies, and leadership potential in a dynamic technological landscape, allowing professionals to remain relevant and competitive.
Program Structure and Curriculum
Eligibility:
- Pass in H.Sc. (Academic) / CBSE / ISCE with minimum 50% aggregate in Physics, Chemistry, Biology / Botany & Zoology / Computer Science & Mathematics. OR Pass in H.Sc. (Vocational) with minimum 50% aggregate in the vocational courses mentioned above with 50% of marks.
Duration: 3 years (6 semesters)
Credits: 140 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UL2101 | Professional English | Core | 3 | Grammar and Vocabulary, Reading Comprehension, Writing Skills, Speaking Skills, Listening Comprehension |
| UA2102 | Calculus and Linear Algebra | Core | 4 | Differential Calculus, Integral Calculus, Matrices and Determinants, Vector Spaces, Eigenvalues and Eigenvectors |
| UC2103 | Programming in Python | Core | 3 | Python Fundamentals, Control Flow and Functions, Data Structures in Python, Object-Oriented Programming, File Handling |
| UC2104 | Operating System | Core | 3 | Operating System Concepts, Process Management, Memory Management, File Systems, Deadlocks and Protection |
| UC21L5 | Python Programming Lab | Lab | 2 | Basic Python Programming, Conditional Statements and Loops, Functions and Modules, Data Structures Implementation, OOP Concepts in Python |
| UM21E6 | Environmental Studies | Elective (Mandatory Credit Course) | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Social Issues and Environment, Environmental Ethics |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UL2201 | English for Computer Science | Core | 3 | Technical Communication, Report Writing, Presentation Skills, Research Paper Writing, Group Discussion |
| UA2202 | Discrete Mathematics | Core | 4 | Logic and Proofs, Set Theory and Relations, Functions and Counting, Graphs and Trees, Algebraic Structures |
| UC2203 | Data Structures and Algorithms | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| UC2204 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Network Protocols, Network Devices, Network Security Basics |
| UC22L5 | Data Structures Lab | Lab | 2 | Array and List Implementations, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| UE22E6 | Indian Constitution and Human Rights | Elective (Mandatory Credit Course) | 2 | Constitutional Framework, Fundamental Rights and Duties, Directive Principles, Human Rights Concepts, Constitutional Amendments |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UC2301 | Database Management System | Core | 3 | DBMS Architecture, Relational Model, SQL Queries, Normalization, Transaction Management |
| UC2302 | Object Oriented Programming using JAVA | Core | 3 | Java Fundamentals, Classes and Objects, Inheritance and Polymorphism, Interfaces and Packages, Exception Handling |
| UC2303 | Principles of Data Science | Core | 3 | Introduction to Data Science, Data Collection and Cleaning, Exploratory Data Analysis, Statistical Methods, Data Visualization |
| UC2304 | Introduction to Artificial Intelligence | Core | 3 | AI Foundations, Problem Solving Agents, Search Algorithms, Knowledge Representation, Machine Learning Basics |
| UC23L5 | DBMS Lab | Lab | 2 | SQL Commands Practice, Database Design, Query Optimization, Transaction Control, Report Generation |
| UC23L6 | Object Oriented Programming using JAVA Lab | Lab | 2 | Java Program Development, OOP Implementation, Exception Handling, File I/O in Java, GUI Programming Basics |
| UC23P7 | Mini Project - I | Project | 1 | Problem Identification, Requirement Analysis, Design and Implementation, Testing and Debugging, Documentation and Presentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UC2401 | Cloud Computing | Core | 3 | Cloud Computing Concepts, Cloud Service Models, Cloud Deployment Models, Virtualization, Cloud Security |
| UC2402 | Big Data Analytics | Core | 3 | Big Data Fundamentals, Hadoop Ecosystem, MapReduce, Spark Framework, NoSQL Databases |
| UC2403 | Machine Learning | Core | 3 | Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, Model Evaluation, Ensemble Methods |
| UC2404 | Web Technology | Core | 3 | HTML, CSS, JavaScript, Client-Side Scripting, Server-Side Scripting, Web Frameworks, Database Connectivity |
| UC24L5 | Big Data Analytics Lab | Lab | 2 | Hadoop Installation and Configuration, HDFS Operations, MapReduce Programming, Spark Data Processing, NoSQL Database Interaction |
| UC24L6 | Machine Learning Lab | Lab | 2 | Python Libraries for ML, Supervised Model Implementation, Unsupervised Model Implementation, Model Hyperparameter Tuning, Data Preprocessing and Visualization |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UC2501 | Deep Learning | Core | 3 | Neural Network Fundamentals, Convolutional Neural Networks, Recurrent Neural Networks, Deep Learning Architectures, Generative Models |
| UC2502 | Natural Language Processing | Core | 3 | NLP Fundamentals, Text Preprocessing, Language Models, Text Classification, Sentiment Analysis |
| UC2503 | Reinforcement Learning | Core | 3 | RL Basics, Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning |
| UC25E4 | Elective - I | Elective | 3 | Advanced topics in Computer Science, Emerging technologies, Domain-specific applications, Research methodologies, Industry-relevant skills |
| UC25E5 | Elective - II | Elective | 3 | Advanced topics in Computer Science, Emerging technologies, Domain-specific applications, Research methodologies, Industry-relevant skills |
| UC25L6 | Deep Learning Lab | Lab | 2 | TensorFlow/Keras Implementation, CNN for Image Recognition, RNN for Sequence Data, Model Training and Evaluation, Hyperparameter Tuning |
| UC25P7 | Mini Project - II | Project | 2 | Advanced Problem Solving, System Design, Prototyping, Testing and Validation, Technical Report Writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UC2601 | Computer Vision | Core | 3 | Image Processing Basics, Feature Extraction, Object Detection, Image Segmentation, Applications of Computer Vision |
| UC2602 | Business Intelligence and Data Visualization | Core | 3 | BI Concepts, Data Warehousing, ETL Process, Data Visualization Techniques, Dashboard Design |
| UC26E3 | Elective - III | Elective | 3 | Advanced topics in Computer Science, Emerging technologies, Domain-specific applications, Research methodologies, Industry-relevant skills |
| UC26E4 | Elective - IV | Elective | 3 | Advanced topics in Computer Science, Emerging technologies, Domain-specific applications, Research methodologies, Industry-relevant skills |
| UC26L5 | Computer Vision Lab | Lab | 2 | OpenCV for Image Manipulation, Feature Detection, Object Recognition, Image Segmentation, Video Processing |
| UC26L6 | Business Intelligence and Data Visualization Lab | Lab | 2 | BI Tool Usage (e.g., Tableau/Power BI), Data Extraction and Transformation, Dashboard Creation, Interactive Visualizations, Reporting |
| UC26P7 | Project Work and Viva Voce | Project | 4 | Comprehensive Project Planning, Solution Development, Testing and Deployment, Project Report Writing, Oral Examination |
