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M-TECH in Data Science at Indian Institute of Technology Delhi
Delhi, Delhi
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About the Specialization
What is Data Science at Indian Institute of Technology Delhi Delhi?
This Machine Intelligence & Data Science program at Indian Institute of Technology Delhi focuses on equipping students with advanced theoretical foundations and practical skills in AI, Machine Learning, and Data Science. It emphasizes computational thinking and problem-solving for real-world challenges, catering to the rapidly growing demand for skilled professionals in India''''s technology sector. The curriculum integrates core computer science principles with specialized knowledge.
Who Should Apply?
This program is ideal for engineering graduates, especially from Computer Science, Electrical, or Electronics backgrounds, seeking entry into high-tech roles in data science and AI. It also suits working professionals aiming to upskill for leadership or specialized roles in analytics, machine learning engineering, or research, and career changers with strong analytical aptitude transitioning into the AI industry in India.
Why Choose This Course?
Graduates of this program can expect to secure impactful roles as Data Scientists, Machine Learning Engineers, AI Researchers, or Consultants in top Indian and multinational companies. Starting salaries for M.Tech graduates in India typically range from INR 12-25 LPA, with significant growth potential. The program also prepares individuals for advanced research and Ph.D. studies, contributing to India''''s burgeoning AI ecosystem.
Student Success Practices
Foundation Stage
Master Core Mathematical and Programming Skills- (Semester 1-2)
Dedicate significant time to thoroughly understand the mathematical foundations (linear algebra, probability, optimization) and programming paradigms (Python, data structures, algorithms) introduced in the first semester. These are crucial building blocks for advanced AI/ML concepts.
Tools & Resources
NPTEL courses, Coursera/edX for foundational math and Python, LeetCode/GeeksforGeeks for algorithms practice, Textbooks like ''''Deep Learning'''' by Goodfellow et al.
Career Connection
Strong fundamentals are essential for cracking technical interviews, understanding research papers, and developing robust AI solutions, providing a solid base for various data science roles.
Active Participation in Learning and Peer Collaboration- (Semester 1-2)
Engage actively in classroom discussions, complete assignments diligently, and form study groups with peers. Collaborating on problem sets and coding challenges enhances understanding, exposes different perspectives, and builds essential teamwork skills.
Tools & Resources
Campus study rooms, Online collaborative coding platforms, Academic forums and discussion groups
Career Connection
Developing strong communication and teamwork abilities is highly valued in industry, facilitating effective collaboration on complex data science projects and improving overall professional soft skills.
Explore Foundational ML Libraries and Frameworks- (Semester 1-2)
Beyond theoretical understanding, gain hands-on experience with key machine learning libraries like Scikit-learn, TensorFlow, and PyTorch. Implement simple models and explore their functionalities through practical projects and tutorials.
Tools & Resources
Kaggle notebooks, Official documentation of Scikit-learn, TensorFlow, PyTorch, Online tutorials and MOOCs focusing on practical implementation
Career Connection
Proficiency in these tools is a non-negotiable skill for any Machine Learning Engineer or Data Scientist, directly impacting internship and job prospects by demonstrating practical readiness.
Intermediate Stage
Undertake Mini-Projects and Competitions- (Semester 2-3)
Apply learned concepts by working on self-initiated mini-projects or participating in data science competitions on platforms like Kaggle. This helps in understanding real-world data complexities, model selection, and performance optimization.
Tools & Resources
Kaggle, HackerEarth, GitHub for project showcase, Jupyter Notebooks
Career Connection
Project portfolios and competition rankings are powerful differentiators during placements, showcasing practical problem-solving skills and a proactive approach to learning, crucial for Indian tech roles.
Focus on Elective Specialization and Research- (Semester 2-3)
Strategically choose electives that align with your career interests (e.g., NLP, Computer Vision, Reinforcement Learning). Start exploring research papers in your area of interest and begin brainstorming for your Master''''s project (SID800).
Tools & Resources
Google Scholar, ArXiv, Zotero for reference management, Departmental research labs and faculty mentors
Career Connection
Specialized knowledge makes you a strong candidate for niche roles and advanced research positions. A well-defined research project can lead to publications or patent opportunities, enhancing your profile.
Network Actively and Seek Internships- (Semester 2-3)
Attend departmental seminars, workshops, and industry talks. Connect with faculty, senior students, and industry professionals. Actively apply for summer internships to gain practical industry exposure and build a professional network.
Tools & Resources
LinkedIn, Professional conferences (e.g., ICLR, NeurIPS, workshops at IITD), IIT Delhi''''s Training and Placement Unit
Career Connection
Networking opens doors to internship and full-time job opportunities. Internships provide invaluable experience, often leading to pre-placement offers (PPOs) in leading Indian companies and MNCs.
Advanced Stage
Intensive Master''''s Project Work and Thesis Writing- (Semester 3-4)
Dedicate yourself fully to the Master''''s Project (SID800, SID801), aiming for high-quality research outcomes. Focus on rigorous experimentation, analysis, and effective thesis writing. Seek regular feedback from your advisor.
Tools & Resources
LaTeX for thesis writing, Cloud computing resources (AWS, GCP, Azure) for experiments, Version control (Git) for code management, Research methodology guidelines
Career Connection
A strong Master''''s project is a capstone achievement, demonstrating independent research capability, problem-solving prowess, and technical expertise, highly valued for R&D roles and academic pursuits.
Comprehensive Placement Preparation- (Semester 3-4)
Begin intensive preparation for placements, including revising core concepts, practicing coding interviews, and mock interviews. Tailor your resume and portfolio to highlight your project work, skills, and achievements relevant to data science roles.
Tools & Resources
InterviewBit, HackerRank, Glassdoor for company-specific interview experiences, IIT Delhi Placement Cell resources and workshops
Career Connection
Effective placement preparation is critical for securing top-tier positions. A well-prepared candidate with a strong portfolio stands out in the competitive Indian job market for AI/ML roles.
Engage in Advanced Skill Development and Certifications- (Semester 3-4)
Beyond the curriculum, explore advanced topics and consider professional certifications in areas like cloud data engineering (e.g., AWS Certified Machine Learning Specialty), specialized ML frameworks, or domain-specific analytics to further enhance your profile.
Tools & Resources
Coursera Specializations, Udacity Nanodegrees, Official cloud provider certifications, DeepLearning.AI courses
Career Connection
Acquiring advanced certifications demonstrates a commitment to continuous learning and adds a competitive edge, validating specialized skills demanded by employers in India''''s fast-evolving data science landscape.
Program Structure and Curriculum
Eligibility:
- B.Tech/BE or equivalent in Computer Science & Engineering, Information Technology, Electrical Engineering, Electronics & Communication Engineering, Mathematics & Computing, or other relevant engineering/science disciplines, with a minimum CPI of 6.5 (or 60% marks) and a valid GATE score in CS, EC, EE, MA, ST, DA. Specific details are available in the IIT Delhi M.Tech admission brochure.
Duration: 2 years (4 semesters)
Credits: 60 Credits
Assessment: Internal: Varies by course, typically includes continuous assessment (quizzes, assignments, mid-semester exams), External: Varies by course, typically includes end-semester examinations for theory courses; practicals, viva-voce, and project reports for lab and project work
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SID501 | Mathematical Foundations for Machine Intelligence & Data Science | Core | 3 | Linear Algebra, Probability Theory, Statistics for ML, Optimization Techniques, Multivariable Calculus |
| SID502 | Data Structures and Algorithms for Machine Intelligence & Data Science | Core | 3 | Algorithmic Complexity, Fundamental Data Structures, Graph Algorithms, Dynamic Programming, Sorting and Searching |
| SID503 | Introduction to Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation and Selection |
| SID504 | Principles of Data Management | Core | 3 | Relational Databases, SQL Querying, NoSQL Systems, Data Warehousing, Big Data Fundamentals |
| SID505 | Programming for Machine Intelligence and Data Science | Core | 3 | Python Programming, Numpy and Pandas, Data Visualization Libraries, Software Engineering Principles, Version Control (Git) |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SID506 | Deep Learning | Core | 3 | Neural Network Architectures, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Optimization Techniques for DL |
| SID507 | Research Methods for Machine Intelligence and Data Science | Core | 2 | Scientific Research Methodology, Literature Review, Experimental Design, Academic Writing, Research Ethics |
| Elective I | Elective Course (chosen from MIDS Elective Pool) | Elective | 3 | Varies based on chosen elective from the MIDS Elective Pool |
| Elective II | Elective Course (chosen from MIDS Elective Pool) | Elective | 3 | Varies based on chosen elective from the MIDS Elective Pool |
| Elective III | Elective Course (chosen from MIDS Elective Pool) | Elective | 3 | Varies based on chosen elective from the MIDS Elective Pool |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SID800 | Master''''s Project Part I | Project | 6 | Problem Identification, Literature Survey, Methodology Design, Initial Implementation, Report Writing |
| SID850 | Seminar | Research | 3 | Review of Research Papers, Presentation Skills, Academic Discussion, Critical Analysis, Topic Specialization |
| Elective IV | Elective Course (chosen from MIDS Elective Pool) | Elective | 3 | Varies based on chosen elective from the MIDS Elective Pool |
| Elective V | Elective Course (chosen from MIDS Elective Pool) | Elective | 3 | Varies based on chosen elective from the MIDS Elective Pool |
| Elective VI | Elective Course (chosen from MIDS Elective Pool) | Elective | 3 | Varies based on chosen elective from the MIDS Elective Pool |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SID801 | Master''''s Project Part II | Project | 7 | Advanced Implementation, Experimentation and Evaluation, Results Analysis, Thesis Writing, Project Defense |
| Elective VII | Elective Course (chosen from MIDS Elective Pool) | Elective | 3 | Varies based on chosen elective from the MIDS Elective Pool |
| Elective VIII | Elective Course (chosen from MIDS Elective Pool) | Elective | 3 | Varies based on chosen elective from the MIDS Elective Pool |
Semester elective
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SID601 | Reinforcement Learning | Elective | 3 | Markov Decision Processes, Q-Learning, Policy Gradients, Deep Reinforcement Learning, Multi-Agent RL |
| SID602 | Probabilistic Graphical Models | Elective | 3 | Bayesian Networks, Markov Random Fields, Inference Algorithms, Learning Graphical Models, Approximate Inference |
| SID603 | Natural Language Processing | Elective | 3 | Text Preprocessing, Language Models, Word Embeddings, Sequence Models, NLP Applications |
| SID604 | Computer Vision | Elective | 3 | Image Processing Basics, Feature Extraction, Object Recognition, Image Segmentation, Deep Learning for Vision |
| SID605 | Information Retrieval | Elective | 3 | Boolean Retrieval, Vector Space Models, Ranking Algorithms, Web Search, Recommender Systems |
| SID606 | Time Series Analysis | Elective | 3 | Stationarity, ARIMA Models, State-Space Models, Forecasting Techniques, Neural Networks for Time Series |
| SID607 | Causal Inference | Elective | 3 | Potential Outcomes Framework, Directed Acyclic Graphs, Instrumental Variables, Difference-in-Differences, Mediation Analysis |
| SID608 | Graph Machine Learning | Elective | 3 | Graph Representation Learning, Graph Neural Networks, Node Embeddings, Link Prediction, Graph Classification |
| SID609 | Large Language Models | Elective | 3 | Transformer Architecture, Pre-training Methods, Fine-tuning Techniques, Prompt Engineering, LLM Applications and Limitations |
| SID610 | Trustworthy AI | Elective | 3 | AI Ethics Principles, Fairness in AI, Explainable AI (XAI), Robustness and Security, Privacy-Preserving AI |
| SID611 | Optimization for Machine Learning | Elective | 3 | Convex Optimization, Gradient Descent Variants, Stochastic Optimization, Constrained Optimization, Lagrange Multipliers |
| SID612 | Bayesian Machine Learning | Elective | 3 | Bayes Theorem, Prior and Posterior Distributions, Bayesian Regression, Gaussian Processes, Markov Chain Monte Carlo (MCMC) |
| SID613 | Advanced Deep Learning | Elective | 3 | Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Attention Mechanisms, Meta-Learning, Neural Architecture Search (NAS) |
| SID614 | Speech Technology | Elective | 3 | Speech Signal Processing, Automatic Speech Recognition (ASR), Text-to-Speech (TTS), Speaker Recognition, Speech Emotion Recognition |
| SID615 | Data Privacy | Elective | 3 | Privacy Definitions, Differential Privacy, Homomorphic Encryption, Federated Learning, Data Anonymization Techniques |
| SID616 | Cloud Computing for Data Science | Elective | 3 | Cloud Architectures, AWS/Azure/GCP Services, Containerization (Docker, Kubernetes), Big Data on Cloud, Serverless Computing |
| SID617 | Big Data Analytics | Elective | 3 | Hadoop Ecosystem, Spark Framework, Distributed Data Processing, Data Stream Analytics, NoSQL Databases |
| SID618 | Data Visualization | Elective | 3 | Principles of Visualization, Visual Encoding, Interactive Dashboards, Tools (Tableau, Power BI, D3.js), Storytelling with Data |
| SID619 | Financial Machine Learning | Elective | 3 | Algorithmic Trading, Market Prediction, Risk Management, Portfolio Optimization, Fraud Detection |
| SID620 | Health Informatics | Elective | 3 | Electronic Health Records, Medical Image Analysis, Genomic Data Analysis, Clinical Decision Support Systems, Public Health Surveillance |
| SID621 | Robotics and Autonomous Systems | Elective | 3 | Robot Kinematics, Robot Dynamics, Path Planning, Localization and Mapping (SLAM), Reinforcement Learning for Robotics |
| SID622 | Quantum Machine Learning | Elective | 3 | Quantum Computing Basics, Quantum Algorithms, Quantum Neural Networks, Quantum Optimization, Quantum Data Analysis |
| SID623 | Explainable AI | Elective | 3 | Interpretability vs Explainability, LIME, SHAP, Feature Importance, Model-Agnostic Explanations |
| SID624 | AI Ethics and Governance | Elective | 3 | Ethical AI Frameworks, Bias and Discrimination, Accountability and Transparency, Regulatory Landscape for AI, Societal Impact of AI |
| SID625 | Neuro-Symbolic AI | Elective | 3 | Symbolic AI Foundations, Neural-Symbolic Architectures, Knowledge Representation, Reasoning with Neural Networks, Integration of Learning and Reasoning |
| SID626 | Edge AI | Elective | 3 | Edge Computing Architectures, On-Device Machine Learning, Model Compression, Resource-Constrained AI, Applications of Edge AI |
| SID627 | Generative AI | Elective | 3 | Generative Models (GANs, VAEs), Diffusion Models, Text-to-Image Generation, Text Generation, Creative AI Applications |
| SID628 | Multi-Agent Systems | Elective | 3 | Agent Architectures, Agent Communication, Cooperation and Coordination, Game Theory for Agents, Distributed AI |
| SID629 | Game Theory for AI | Elective | 3 | Static Games, Dynamic Games, Nash Equilibrium, Mechanism Design, Behavioral Game Theory |
| SID630 | Human-Computer Interaction | Elective | 3 | User Centered Design, Usability Evaluation, Interaction Paradigms, Cognitive Psychology in HCI, AI in HCI |
| SID631 | Distributed Machine Learning | Elective | 3 | Parallel Computing for ML, Distributed Optimization, Parameter Server Architecture, Communication Efficient ML, Scalable ML Systems |
| SID632 | Federated Learning | Elective | 3 | Decentralized ML, Privacy in FL, Communication Efficiency, Data Heterogeneity, FL Algorithms |
| SID633 | AI for Social Good | Elective | 3 | AI for Healthcare, AI for Environment, AI for Education, AI for Disaster Management, Ethical Considerations |
