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M-TECH in Data Science And Artificial Intelligence at Indian Institute of Technology Bhilai
Raipur, Chhattisgarh
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About the Specialization
What is Data Science and Artificial Intelligence at Indian Institute of Technology Bhilai Raipur?
This Data Science and Artificial Intelligence program at Indian Institute of Technology Bhilai focuses on theoretical foundations and practical applications in data science and AI. It addresses the surging demand for skilled professionals in India''''s rapidly evolving tech landscape, offering a blend of advanced algorithms, machine learning, and big data technologies. The program aims to develop experts capable of tackling complex data-driven challenges across various sectors. Its curriculum is designed to produce innovators and leaders in the field.
Who Should Apply?
This program is ideal for engineering graduates with a background in Computer Science, IT, or related fields, as well as M.Sc. holders in Mathematics, Statistics, or Computer Science, who possess a strong analytical aptitude. It caters to fresh graduates aspiring for cutting-edge roles and working professionals seeking to upskill or transition into the burgeoning fields of data science and artificial intelligence in India''''s dynamic job market.
Why Choose This Course?
Graduates of this program can expect to secure roles as Data Scientists, AI Engineers, Machine Learning Engineers, or Big Data Architects in top Indian and multinational companies. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals earning significantly more. The program fosters advanced problem-solving skills, preparing students for impactful careers and further research opportunities within India''''s tech ecosystem and globally, with a strong emphasis on practical application.
Student Success Practices
Foundation Stage
Master Core Mathematical & Algorithmic Foundations- (Semester 1)
Dedicate significant effort to solidifying concepts in linear algebra, probability, statistics, data structures, and algorithms (DS501, DS502). Actively leverage online platforms and textbooks for supplementary learning and practice to build a robust theoretical base.
Tools & Resources
NPTEL courses on Algorithms and Linear Algebra, MIT OpenCourseware, HackerRank, LeetCode, GeeksforGeeks for coding practice
Career Connection
A strong foundation is crucial for excelling in technical interviews for data science and AI roles, which heavily test these core areas, and for understanding advanced concepts throughout the program.
Develop Proficiency in Data Science Programming & Tools- (Semester 1)
Become highly proficient in Python for data science, including libraries like Pandas, NumPy, Matplotlib, Scikit-learn (DS503), and basic SQL for data interaction. Actively engage in DS504 lab sessions, translating theoretical knowledge into practical code.
Tools & Resources
Kaggle notebooks, DataCamp, Coursera specializations, Official documentation for Python libraries, GitHub for project version control
Career Connection
Hands-on coding skills are non-negotiable for most data scientist and ML engineer positions, enabling practical implementation, data manipulation, and problem-solving in real-world scenarios.
Initiate and Explore Research Areas (DS505)- (Semester 1)
Engage deeply with Research Project - I. Identify areas of interest early, read relevant research papers, and actively discuss ideas with faculty mentors to formulate a robust problem statement and begin preliminary investigations.
Tools & Resources
Research papers on arXiv, IEEE Xplore, Google Scholar, Zotero for citation management, Departmental faculty for guidance
Career Connection
This early exposure to research problem formulation is vital for building a strong M.Tech project, which is a key differentiator in placements, demonstrating independent thinking and problem-solving abilities.
Intermediate Stage
Advanced Stage
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in Computer Science & Engineering/Computer Engineering/Information Technology or equivalent; or M.Sc. in Computer Science/Information Technology/Mathematics/Statistics or MCA. Candidates must have a valid GATE score in CS (or relevant discipline as per the common admission criteria for M.Tech programs).
Duration: 4 semesters / 2 years
Credits: 72 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS501 | Mathematical Foundations of Data Science | Core | 3 | Linear Algebra for Data Science, Probability Theory and Distributions, Statistical Inference and Hypothesis Testing, Optimization Techniques, Calculus for Machine Learning |
| DS502 | Data Structures and Algorithms for Data Science | Core | 3 | Advanced Sorting and Searching Algorithms, Graph Algorithms and their Applications, Dynamic Programming Principles, Hashing and Hash Tables, Tree Structures and Traversal |
| DS503 | Machine Learning | Core | 3 | Supervised Learning Algorithms, Unsupervised Learning Techniques, Reinforcement Learning Basics, Model Evaluation and Validation, Ensemble Methods and Boosting |
| CS503 | Advanced Computer Networks | Core | 3 | |
| DS504 | Data Science Lab - I | Lab | 2 | Python Programming for Data Science, Data Manipulation with Pandas, Data Visualization with Matplotlib/Seaborn, SQL for Data Analytics, Jupyter Notebooks and Reproducible Research |
| DS505 | Research Project - I | Project | 6 | Literature Review and Survey, Problem Formulation and Definition, Research Methodology Design, Proposal Writing and Presentation, Initial Implementation and Experimentation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS506 | Deep Learning | Core | 3 | Fundamentals of Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and LSTMs, Autoencoders and Generative Adversarial Networks (GANs), Deep Learning Architectures and Training |
| DS507 | Big Data Analytics | Core | 3 | Hadoop Ecosystem and HDFS, Apache Spark Framework, Distributed Data Processing, NoSQL Databases (Cassandra, MongoDB), Stream Processing with Kafka/Spark Streaming |
| DS508 | Natural Language Processing | Core | 3 | Text Preprocessing and Tokenization, Language Models and N-grams, Word Embeddings (Word2Vec, GloVe), Sequence Models (HMM, CRF), Deep Learning for NLP (Transformers) |
| DS509 | Computer Vision | Core | 3 | Image Processing Fundamentals, Feature Detection and Extraction, Object Detection and Recognition, Image Segmentation Techniques, Deep Learning for Computer Vision |
| DS510 | Data Science Lab - II | Lab | 2 | Deep Learning Frameworks (TensorFlow, PyTorch), Big Data Tools (Spark, Hive), NLP Libraries (NLTK, SpaCy, Hugging Face), Computer Vision Libraries (OpenCV), Model Deployment and MLOps Concepts |
| DS511 | Research Project - II | Project | 6 | Advanced Experimentation and Data Collection, Statistical Analysis of Results, Interpretation of Findings and Conclusions, Technical Report Writing, Presentation and Communication Skills |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS512 | M.Tech Project Part-I | Project | 2 | Project Problem Refinement, Detailed Literature Review, Methodology Development, Initial System Design/Prototype, Progress Report and Presentation |
| DSXXX | Elective I | Elective | 3 | Chosen from a pool including: Advanced Machine Learning, Reinforcement Learning, Deep Learning for NLP, Big Data Security, Time Series Analysis, Advanced Computer Vision, Data Visualization, Graph Neural Networks. |
| DSXXX | Elective II | Elective | 3 | Chosen from a pool including: Advanced Machine Learning, Reinforcement Learning, Deep Learning for NLP, Big Data Security, Time Series Analysis, Advanced Computer Vision, Data Visualization, Graph Neural Networks. |
| DSXXX | Elective Lab I | Elective Lab | 2 | |
| DS513 | DSAI Practical | Practical/Project | 6 | Advanced Data Science Project Development, Real-world Problem Solving and Implementation, Integration of Data Science Tools and Platforms, Model Deployment and Productionization, Performance Optimization and Evaluation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS514 | M.Tech Project Part-II | Project | 16 | Comprehensive Research and Development, Experimental Validation and Analysis, Thesis Writing and Documentation, Technical Presentation and Defense Preparation, Contribution to Knowledge in DSAI |
