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M-TECH in Artificial Intelligence And Data Science at B. S. Abdur Rahman Crescent Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Artificial Intelligence and Data Science at B. S. Abdur Rahman Crescent Institute of Science and Technology Chengalpattu?
This Artificial Intelligence and Data Science program at B.S. Abdur Rahman Crescent Institute of Science and Technology focuses on equipping students with advanced knowledge in AI, machine learning, deep learning, and big data technologies. It is designed to meet the escalating demand for skilled professionals in India''''s rapidly digitalizing economy, offering a blend of theoretical foundations and practical applications crucial for innovation and problem-solving in various industries.
Who Should Apply?
This program is ideal for engineering graduates from Computer Science, IT, ECE, EEE, and related fields, as well as MCA and MSc Computer Science/Statistics postgraduates, who aspire to build a career in cutting-edge AI and data science domains. It also caters to working professionals seeking to upskill or transition into data-driven roles, provided they possess a strong aptitude for mathematics, statistics, and programming.
Why Choose This Course?
Graduates of this program can expect to pursue high-demand career paths such as Data Scientist, Machine Learning Engineer, AI Architect, Big Data Engineer, and Business Intelligence Analyst within India''''s thriving tech sector. Entry-level salaries typically range from 6-12 LPA, with experienced professionals commanding 15-30+ LPA. The program prepares students for leadership roles in AI innovation across startups, MNCs, and research organizations.
Student Success Practices
Foundation Stage
Master Core Programming and Mathematics- (Semester 1-2)
Dedicate significant time to mastering Python, R, and foundational mathematical concepts like linear algebra, calculus, probability, and statistics. Utilize platforms like HackerRank, LeetCode, and NPTEL courses for consistent practice and conceptual clarity, which are non-negotiable for understanding advanced AI/DS algorithms.
Tools & Resources
Python, R, Numpy, Pandas, HackerRank, LeetCode, NPTEL
Career Connection
A strong foundation in programming and mathematics is critical for acing technical interviews and efficiently implementing complex AI/ML models in future roles.
Build Strong Data Structures & Algorithms- (Semester 1-2)
Engage rigorously in Data Structures and Algorithms (DSA) problem-solving. Participate in online coding competitions and challenges regularly to enhance logical thinking and coding efficiency. This skill is universally valued in tech hiring, especially for roles requiring optimized solutions.
Tools & Resources
GeeksforGeeks, CodeChef, Codeforces, TopCoder
Career Connection
Proficiency in DSA is a primary filtering criterion for many tech companies during placement drives, demonstrating problem-solving capabilities.
Engage in Mini-Projects and Hackathons- (Semester 1-2)
Actively participate in mini-projects, departmental competitions, and external hackathons. Apply theoretical knowledge to solve real-world problems using publicly available datasets (e.g., Kaggle). Start building a portfolio of practical work early on, showcasing diverse skills.
Tools & Resources
Kaggle, GitHub, Jupyter Notebooks, Google Colab
Career Connection
A robust project portfolio significantly enhances resume appeal and provides concrete examples of skills during interviews, crucial for securing internships and placements.
Intermediate Stage
Specialized Skill Development via Electives- (Semester 3)
Deep dive into your chosen specialization through electives (NLP, CV, Reinforcement Learning, etc.). Supplement classroom learning with industry-recognized certifications (e.g., AWS Certified Machine Learning Specialty, Google Cloud Professional Machine Learning Engineer). Utilize online learning platforms for advanced topics.
Tools & Resources
Coursera, edX, Udemy, AWS/Azure/GCP AI/ML certifications
Career Connection
Specialized skills make you a more targeted and valuable candidate for specific roles within AI/DS, opening doors to advanced positions and higher compensation.
Seek Industry Internships and Workshops- (Semester 3)
Actively pursue summer or semester-long internships in AI/DS roles within Indian companies or MNCs with presence in India. Attend industry workshops, tech talks, and seminars organized by professional bodies. This provides invaluable practical exposure and helps build a professional network.
Tools & Resources
LinkedIn, Internshala, Company career portals, Tech conferences
Career Connection
Internships convert into full-time offers and provide real-world experience, making you highly employable. Networking helps discover hidden job opportunities.
Participate in Research and Publications- (Semester 3)
Collaborate with faculty members on research projects leading to publications in conferences or journals. This develops critical thinking, problem-solving abilities, and exposes you to the academic side of AI/DS, which can be beneficial for R&D roles or further studies.
Tools & Resources
Scopus, Web of Science, Google Scholar, LaTeX
Career Connection
Research experience and publications are highly valued for R&D positions, academic careers, and differentiate you for roles requiring innovative solutions.
Advanced Stage
Execute Strategic Project Work (Phase II)- (Semester 4)
Focus on developing a comprehensive, industry-relevant final year project. Aim for a deployable solution or a significant research contribution. Document your work meticulously and be prepared to articulate its impact, challenges, and solutions effectively.
Tools & Resources
Full stack development tools for deployment, Version control (Git), Project management tools
Career Connection
A strong capstone project serves as a showcase of your entire learning journey and technical prowess, often being the centerpiece of placement interviews.
Intensive Placement and Interview Preparation- (Semester 4)
Tailor your resume and LinkedIn profile to highlight AI/DS skills and projects. Practice mock technical and HR interviews extensively. Prepare for common AI/ML concepts, case studies, and coding questions asked by target companies. Leverage alumni network for guidance.
Tools & Resources
Online interview platforms, Resume builders, LinkedIn, Alumni network
Career Connection
Thorough preparation for placements is crucial for converting skills into job offers at reputable Indian and global companies.
Continuous Learning and Portfolio Building- (Semester 4)
Stay updated with the latest advancements in AI/DS through blogs, research papers, and online courses. Actively contribute to open-source projects or start your own. Maintain an active GitHub repository to demonstrate ongoing learning and practical skills, a key differentiator in the job market.
Tools & Resources
arXiv, Towards Data Science, Kaggle, GitHub
Career Connection
Demonstrates a proactive attitude towards professional growth, which is highly valued by employers looking for candidates who can adapt to rapid technological changes.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in CSE/IT/ECE/EEE/Software Engg./EIE/ICE/Mechatronics or MCA or M.Sc. in CS/IT/Software Engg./Data Science/AI/Applied Math/Statistics. Candidates from other disciplines may require a bridge course. Minimum 50% (45% for reserved category) in qualifying exam. Valid GATE/TANCET/CEPT Score.
Duration: 4 semesters / 2 years
Credits: 69 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAD1101 | Advanced Data Structures and Algorithms | Core | 4 | Algorithm analysis methods, Advanced tree structures, Graph algorithms, Hashing techniques, Dynamic programming, Network flow algorithms |
| MAD1102 | Mathematical Foundations for Data Science | Core | 4 | Linear algebra for data science, Multivariate calculus essentials, Probability theory and distributions, Statistical inference and hypothesis testing, Optimization techniques, Random variables and processes |
| MAD1103 | Applied Machine Learning | Core | 4 | Supervised learning algorithms, Unsupervised learning techniques, Model evaluation and validation, Regression and classification models, Ensemble methods, Feature engineering and selection |
| MAD1104 | Research Methodology and IPR | Core | 3 | Research design and formulation, Data collection and analysis methods, Statistical tools for research, Technical report writing, Intellectual Property Rights (IPR), Patents, copyrights, and trademarks |
| MAD1105 | Advanced Data Structures and Algorithms Lab | Lab | 2 | Implementation of trees and graphs, Algorithm design for efficiency, Sorting and searching implementations, Problem-solving using data structures, Performance analysis of algorithms |
| MAD1106 | Applied Machine Learning Lab | Lab | 2 | Python for machine learning, Data preprocessing and visualization, Model training and hyperparameter tuning, Scikit-learn and other ML libraries, Evaluation metrics implementation |
| MAD1107 | Technical Seminar | Core | 1 | Literature survey techniques, Technical presentation skills, Report writing and documentation, Critical analysis of research papers |
| MAD1108 | Value Added Course – I | Value Added | 2 |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAD1201 | Big Data Technologies | Core | 4 | Hadoop ecosystem fundamentals, MapReduce programming model, Spark for large-scale data processing, NoSQL databases (e.g., MongoDB, Cassandra), Data ingestion and streaming with Kafka, Data warehousing concepts for big data |
| MAD1202 | Deep Learning | Core | 4 | Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformer architectures, Optimization for deep learning (e.g., Adam), Transfer learning and fine-tuning |
| MAD1203 | Elective – I | Elective | 3 | Advanced Data Engineering principles, Natural Language Processing applications, Computer Vision techniques, Explainable AI concepts, Big Data Analytics in specific domains, Cloud Computing for Data Science |
| MAD1204 | Elective – II | Elective | 3 | Data warehousing and ETL processes, Text processing and embeddings, Object detection and image segmentation, Ethical considerations in AI, Graph databases for data science, Reinforcement learning fundamentals |
| MAD1205 | Big Data Technologies Lab | Lab | 2 | Hadoop Distributed File System (HDFS), Spark RDD and DataFrame operations, Hive and Pig scripting, Data integration with Sqoop/Flume, NoSQL database operations |
| MAD1206 | Deep Learning Lab | Lab | 2 | TensorFlow/Keras implementation, PyTorch framework usage, CNN for image classification, RNN for sequence prediction, Generative Adversarial Networks (GANs) |
| MAD1207 | Mini Project with Viva Voce | Project | 2 | Project problem definition, System design and architecture, Implementation and testing, Technical report writing, Presentation and defense of work |
| MAD1208 | Value Added Course – II | Value Added | 2 |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAD2101 | Research Paper Writing and Publication Ethics | Core | 3 | Structure of a research paper, Journal selection strategies, Peer review process, Ethical considerations in research, Plagiarism and self-plagiarism, Citation styles and management |
| MAD2102 | Elective – III | Elective | 3 | Reinforcement Learning algorithms, Advanced topics in Computer Vision, Natural Language Generation, Responsible AI development, Time series analysis with deep learning, Blockchain applications in data science |
| MAD2103 | Elective – IV | Elective | 3 | Data governance and privacy, Speech recognition and synthesis, Medical image analysis, AI in robotics and automation, Fraud detection techniques, Quantum computing for AI |
| MAD2104 | Elective – V | Elective | 3 | Ethical hacking for AI systems, Digital forensics and cyber security, Generative AI models, Federated learning concepts, Predictive analytics for business, Social network analysis |
| MAD2105 | Project Work - Phase I | Project | 3 | Problem identification and scope definition, Comprehensive literature survey, Methodology development, Initial system design and prototyping, Data acquisition and preparation, Preliminary results and analysis |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAD2201 | Project Work - Phase II | Project | 10 | Full system implementation and development, Rigorous testing and debugging, Performance evaluation and optimization, Detailed results analysis and interpretation, Thesis writing and documentation, Project defense and presentation |
