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M-TECH in Data Analytics at Indian Institute of Technology (Indian School of Mines), Dhanbad
Dhanbad, Jharkhand
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About the Specialization
What is Data Analytics at Indian Institute of Technology (Indian School of Mines), Dhanbad Dhanbad?
This Data Analytics program at IIT ISM Dhanbad focuses on equipping students with advanced skills in machine learning, big data technologies, and statistical modeling essential for handling complex datasets. It emphasizes theoretical foundations combined with practical applications, catering to the burgeoning demand for data professionals across diverse Indian industries. The curriculum is designed to create experts capable of deriving actionable insights from data.
Who Should Apply?
This program is ideal for engineering graduates from Computer Science, IT, Electronics, or Electrical backgrounds, as well as postgraduates in Computer Science, IT, Mathematics, Statistics, or MCA holders. It caters to fresh graduates seeking entry into the data science domain and working professionals aiming to upgrade their analytical skills for advanced roles in analytics and AI within the Indian market.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths as Data Scientists, Machine Learning Engineers, Data Engineers, or Business Intelligence Analysts in leading Indian and multinational companies operating in India. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals commanding significantly higher packages (INR 20-50+ LPA). The program also prepares students for research and entrepreneurship in the Indian tech ecosystem.
Student Success Practices
Foundation Stage
Master Programming and Math Fundamentals- (Semester 1-2)
Dedicate significant time to solidify Python programming, data structures, algorithms, linear algebra, and probability concepts. These are the bedrock of advanced data analytics and machine learning. Regular practice and problem-solving are crucial.
Tools & Resources
LeetCode, HackerRank, Coursera/NPTEL for Math courses, GeeksforGeeks for DSA
Career Connection
Strong fundamentals are essential for cracking technical interviews at top Indian tech companies and building efficient data solutions.
Active Participation in Lab Sessions- (Semester 1-2)
Utilize Data Analytics Lab sessions to gain hands-on experience with tools like NumPy, Pandas, Scikit-learn, and basic SQL. Actively ask questions and experiment beyond assigned tasks to deepen practical understanding.
Tools & Resources
Jupyter Notebooks, Google Colab, Kaggle tutorials, Official documentation of libraries
Career Connection
Practical proficiency with these tools is a direct requirement for data analyst and junior data scientist roles in India.
Join Data Science Study Groups- (Semester 1-2)
Form or join peer study groups to discuss complex topics, share insights, and collaborate on assignments. Explaining concepts to others reinforces your own understanding and builds a strong academic network.
Tools & Resources
Discord/WhatsApp groups, University library study rooms, Online collaborative whiteboards
Career Connection
Networking and collaborative skills are highly valued in team-oriented data science roles within Indian organizations.
Intermediate Stage
Engage in Kaggle Competitions and Data Challenges- (Semester 2-3)
Participate regularly in online data science competitions (e.g., Kaggle, Analytics Vidhya) to apply learned concepts to real-world datasets, benchmark skills, and learn from diverse approaches. Focus on improving model performance.
Tools & Resources
Kaggle, Analytics Vidhya, GitHub for sharing solutions, Open-source ML frameworks
Career Connection
Winning or performing well in these challenges significantly boosts your resume for Indian data science firms and demonstrates practical problem-solving abilities.
Deep Dive into Specialization Electives- (Semester 2-3)
Carefully choose electives aligned with your career interests (e.g., Deep Learning, NLP, Cloud Analytics). Go beyond classroom content by exploring advanced research papers, implementing concepts, and contributing to open-source projects.
Tools & Resources
arXiv, Towards Data Science blog, GitHub for open-source contributions, Specific MOOCs for advanced topics
Career Connection
Developing niche expertise makes you a highly sought-after candidate for specialized roles in AI/ML startups and R&D divisions of large Indian companies.
Seek Industry Internships- (End of Semester 2, during Semester 3)
Actively pursue summer or semester-long internships with data science teams in Indian tech companies, startups, or research labs. This provides invaluable real-world experience, exposure to industry tools, and networking opportunities.
Tools & Resources
University career portal, LinkedIn, Internshala, Referrals from professors/alumni
Career Connection
Internships are often a direct pathway to pre-placement offers (PPOs) in India and provide a significant advantage in campus placements.
Advanced Stage
Focus on Dissertation/Project Excellence- (Semester 3-4)
Choose a relevant, challenging dissertation topic with high industry impact or research potential. Work closely with your advisor, conduct thorough research, and aim for publishable quality work. This is your capstone project.
Tools & Resources
Research journals (IEEE, ACM), Academic databases, Open-source datasets, Collaboration tools
Career Connection
A strong dissertation showcases deep expertise and research capabilities, crucial for R&D roles, PhD aspirations, or leadership positions in data teams.
Build a Professional Portfolio and Resume- (Semester 3-4)
Curate a portfolio of your best projects (Kaggle, personal projects, internship work, dissertation) on GitHub or a personal website. Tailor your resume to highlight data science skills, tools, and achievements for Indian job market requirements.
Tools & Resources
GitHub, LinkedIn profile optimization, Personal website builders (e.g., WordPress, Jekyll), Resume templates
Career Connection
A strong portfolio is critical for demonstrating practical skills and securing interviews for data science and AI roles across India.
Prepare Rigorously for Placements- (Semester 3-4)
Practice mock interviews covering data structures, algorithms, machine learning concepts, and behavioral questions. Network with alumni and placement cell members to understand company-specific hiring processes and expectations in India.
Tools & Resources
InterviewBit, Glassdoor for company reviews/questions, IIT ISM placement cell resources, Alumni mentorship programs
Career Connection
Effective preparation is key to securing top-tier placements in Indian and global companies that recruit from IIT ISM Dhanbad, ensuring a successful career launch.
Program Structure and Curriculum
Eligibility:
- B.Tech/B.E. in Computer Science & Engineering/Information Technology/Electronics & Communication Engineering/Electrical Engineering or M.Sc. in Computer Science/Information Technology/Mathematics/Statistics/Electronics or MCA with a valid GATE score (CS/MA/ST) or equivalent qualification.
Duration: 4 semesters / 2 years
Credits: 90 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CDA5101 | Mathematical Foundations for Data Analytics | Core | 4 | Linear Algebra, Calculus, Probability and Statistics, Stochastic Processes, Optimization Techniques |
| CDA5102 | Advanced Data Structures & Algorithms | Core | 4 | Asymptotic Analysis, Advanced Data Structures, Graph Algorithms, Dynamic Programming, Approximation and Randomized Algorithms |
| CDA5103 | Advanced Database Management Systems | Core | 4 | Relational Model and SQL, Database Design and Normalization, Query Processing and Optimization, Transaction Management and Concurrency Control, Distributed and NoSQL Databases |
| CDA5104 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Model Evaluation, Ensemble Methods, Feature Engineering |
| CDA5105 | Data Analytics Lab-I | Lab | 2 | Python for Data Science, NumPy, Pandas, Scikit-learn, Data Preprocessing and Exploration, ML Algorithm Implementation, SQL and Database Interaction |
| CDA5106 | Seminar | Project | 2 | Literature Review, Technical Presentation Skills, Report Writing, Current Research Trends, Research Methodology |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CDA5201 | Big Data Technologies | Core | 4 | Hadoop Ecosystem, Spark Framework, NoSQL Databases, Stream Processing, Cloud Big Data Services |
| CDA5202 | Deep Learning | Core | 4 | Neural Networks Fundamentals, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Transformers and Attention |
| CDA5203 | Natural Language Processing | Core | 4 | Text Preprocessing, Word Embeddings, Sequence Models, Neural NLP, Text Classification and Translation |
| CDA5204 | Data Visualization | Core | 4 | Principles of Data Visualization, Exploratory Data Analysis, Visualization Tools and Libraries, Dashboard Design, Interactive Visualizations |
| CDA5205 | Data Analytics Lab-II | Lab | 2 | Hadoop and Spark Implementation, Deep Learning Frameworks (TensorFlow, PyTorch), NLP Tools (NLTK, SpaCy), Advanced Visualization Techniques, End-to-End Data Pipeline Building |
| CDA52XX E1 | Elective-I | Elective | 4 | Chosen from Departmental Elective pool, Advanced topics in Data Analytics, Specialized Machine Learning, Big Data applications, Emerging trends in AI |
| CDA52XX E2 | Elective-II | Elective | 4 | Chosen from Departmental Elective pool, Specialized areas in Reinforcement Learning, Computer Vision applications, Time Series analysis, Ethical AI considerations |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CDA61XX E3 | Elective-III | Elective | 4 | Chosen from Departmental/Open Elective pool, Interdisciplinary topics, Advanced statistical methods, Specific domain analytics, Research-oriented subjects |
| CDA61XX E4 | Elective-IV | Elective | 4 | Chosen from Departmental/Open Elective pool, Advanced cloud data solutions, Data engineering principles, AI governance, Predictive analytics |
| CDA61XX E5 | Elective-V | Elective | 4 | Chosen from Departmental/Open Elective pool, Quantum machine learning concepts, Federated learning applications, Explainable AI techniques, Business intelligence strategies |
| CDA6199 | M. Tech. Dissertation / Project Phase-I | Project | 12 | Problem Identification, Literature Survey, Research Design, Initial Implementation, Interim Report |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CDA6299 | M. Tech. Dissertation / Project Phase-II | Project | 14 | Advanced Implementation, Data Analysis and Interpretation, Results and Discussion, Final Thesis Writing, Project Defense |
Semester course
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CDA5001 | Reinforcement Learning | Elective | 4 | Markov Decision Processes, Q-Learning and SARSA, Deep Reinforcement Learning, Actor-Critic Methods, Exploration-Exploitation Tradeoff |
| CDA5002 | Time Series Analytics | Elective | 4 | ARIMA Models, Forecasting Techniques, Spectral Analysis, State Space Models, Deep Learning for Time Series |
| CDA5003 | Optimization for Machine Learning | Elective | 4 | Convex Optimization, Gradient Descent Variants, Stochastic Gradient Descent, Primal-Dual Methods, Large-Scale Optimization |
| CDA5004 | Ethical AI | Elective | 4 | Fairness, Accountability, Transparency, Bias in AI Systems, Privacy-Preserving AI, Legal and Societal Implications, Explainable AI |
| CDA5005 | Cloud Computing for Data Analytics | Elective | 4 | Cloud Architectures, AWS, Azure, GCP Data Services, Cloud Data Storage, Big Data Processing in Cloud, Serverless Analytics |
| CDA5006 | Computer Vision | Elective | 4 | Image Processing, Object Detection and Recognition, Image Segmentation, Deep Learning for Vision, Computer Vision Applications |
| CDA5007 | Speech Analytics | Elective | 4 | Speech Production and Perception, Feature Extraction for Speech, Speech Recognition, Speaker Identification, Speech Synthesis |
| CDA5008 | Social Media Analytics | Elective | 4 | Social Network Analysis, Sentiment Analysis on Social Data, Influence Maximization, Community Detection, Trend Prediction |
| CDA5009 | Graph Analytics | Elective | 4 | Graph Representation, Centrality Measures, Community Detection, Graph Neural Networks, Link Prediction |
| CDA5010 | Business Intelligence | Elective | 4 | Data Warehousing and ETL, OLAP and Data Cubes, Reporting and Dashboarding, Decision Support Systems, Data Mining for Business |
| CDA5011 | Text Analytics | Elective | 4 | Information Retrieval, Topic Modeling, Named Entity Recognition, Text Summarization, Knowledge Graph Construction |
| CDA5012 | Pattern Recognition | Elective | 4 | Statistical Pattern Recognition, Clustering and Classification, Feature Selection and Extraction, Dimensionality Reduction, Neural Network based Recognition |
| CDA5013 | Sensor Data Analytics | Elective | 4 | IoT Data Acquisition, Sensor Data Preprocessing, Anomaly Detection in Streams, Time-Series Analysis for Sensors, Edge Computing for Analytics |
| CDA5014 | Health Informatics | Elective | 4 | Electronic Health Records Data, Medical Image Analysis, Predictive Models for Diseases, Drug Discovery Analytics, Healthcare Data Privacy |
| CDA5015 | Financial Analytics | Elective | 4 | Time Series in Finance, Algorithmic Trading, Risk Management and Fraud, Portfolio Optimization, Financial Forecasting |
| CDA5016 | Recommender Systems | Elective | 4 | Collaborative Filtering, Content-Based Filtering, Hybrid Recommenders, Matrix Factorization, Evaluation Metrics |
| CDA5017 | AI for Games | Elective | 4 | Game Theory Fundamentals, Pathfinding Algorithms, Decision Making in Games, Reinforcement Learning for Games, Procedural Content Generation |
| CDA5018 | Quantum Machine Learning | Elective | 4 | Quantum Computing Basics, Quantum Superposition and Entanglement, Quantum Algorithms for ML, Quantum Neural Networks, Applications and Challenges |
| CDA5019 | Data Engineering | Elective | 4 | Data Pipelines and ETL, Data Lake and Warehouse Design, Data Governance and Quality, Data Orchestration Tools, Scalable Data Architectures |
| CDA5020 | Information Retrieval | Elective | 4 | Boolean and Vector Space Models, Ranking Algorithms, Indexing and Query Processing, Evaluation Metrics, Web Search and Recommenders |
| CDA5021 | Probabilistic Graphical Models | Elective | 4 | Bayesian Networks, Markov Random Fields, Inference Algorithms, Learning in PGMs, Applications in AI and ML |
| CDA5022 | Explainable Artificial Intelligence (XAI) | Elective | 4 | Interpretable vs Explainable AI, Model-Agnostic Explanations, Model-Specific Explanations, Counterfactual Explanations, Human-AI Collaboration |
| CDA5023 | Federated Learning | Elective | 4 | Privacy-Preserving ML, Distributed Machine Learning, Homomorphic Encryption, Secure Multi-Party Computation, Challenges and Applications |
| HUL5001 | Research and Publication Ethics | Open Elective | 4 | Ethics in Research, Plagiarism and Misconduct, Publication Best Practices, Intellectual Honesty, Data Integrity |
| HUL5002 | Intellectual Property Rights | Open Elective | 4 | Introduction to IPR, Patents, Copyrights, Trademarks, Trade Secrets, IPR Infringement, IPR Management |
| MGT5001 | Operation Research | Open Elective | 4 | Linear Programming, Simplex Method, Transportation and Assignment Problems, Network Analysis, Queuing Theory |
