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M-TECH in Computational Data Sciences at Indian Institute of Science
Bengaluru, Karnataka
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About the Specialization
What is Computational & Data Sciences at Indian Institute of Science Bengaluru?
This Computational & Data Sciences M.Tech. program at IISc Bengaluru focuses on foundational and applied aspects of data science, machine learning, and artificial intelligence. It addresses the significant demand for skilled professionals in India''''s burgeoning data-driven industries, distinguishing itself with a strong research-oriented curriculum and cutting-edge topics.
Who Should Apply?
This program is ideal for engineering or science graduates with strong quantitative skills and an interest in advanced computing. It suits fresh graduates aiming for impactful roles in AI/ML, and working professionals seeking to transition into or deepen their expertise in data-intensive fields, requiring a strong foundation in mathematics and programming.
Why Choose This Course?
Graduates can expect high-impact careers as Data Scientists, Machine Learning Engineers, AI Researchers, or Data Analysts across Indian and global tech giants, startups, and R&D divisions. Typical starting salaries can range from INR 10-25 LPA, with rapid growth. The program''''s rigorous training prepares students for leadership and innovation.
Student Success Practices
Foundation Stage
Master Core Concepts with Practical Application- (Semester 1-2)
Focus on building a robust understanding of mathematical foundations, algorithms, and core machine learning principles taught in the first two semesters. Utilize online platforms like GeeksforGeeks and LeetCode for coding practice, and Kaggle for dataset challenges. This solidifies the analytical and programming skills critical for subsequent specialized courses and industry roles.
Tools & Resources
GeeksforGeeks, LeetCode, Kaggle, Jupyter Notebooks
Career Connection
Strong foundational skills are directly tested in technical interviews for Data Scientist and Machine Learning Engineer roles.
Engage in Active Learning and Peer Collaboration- (Semester 1-2)
Form study groups to discuss complex topics, solve problems, and prepare for exams. Actively participate in class discussions and leverage faculty office hours for deeper insights into challenging concepts. This fosters a collaborative learning environment, crucial for navigating IISc''''s challenging curriculum and developing teamwork skills.
Tools & Resources
Study groups, Faculty office hours, Online forums like Stack Overflow
Career Connection
Enhances problem-solving abilities, communication skills, and prepares for collaborative project environments in industry.
Explore Departmental Research Opportunities- (Semester 1-2)
Attend research seminars and workshops organized by the CDS department. Reach out to faculty whose research areas pique your interest for potential short-term projects or reading assignments. Early exposure to research helps in identifying potential dissertation topics and understanding academic rigor.
Tools & Resources
CDS Department website, IISc Research portals, Faculty profiles
Career Connection
Builds research acumen, helps secure research-focused internships, and provides a strong foundation for advanced R&D roles or PhD pursuits.
Intermediate Stage
Strategic Elective Choice and Deep Skill Specialization- (Semester 3-4)
Carefully select electives that align with your career aspirations, whether in AI/ML, NLP, Computer Vision, or Data Engineering. Go beyond course requirements by delving into cutting-edge research papers and open-source projects in your chosen area. This builds a specialized skill set highly valued by top Indian tech companies.
Tools & Resources
arXiv, GitHub, Popular ML/DL frameworks (TensorFlow, PyTorch)
Career Connection
Directly enhances expertise for specialized roles, making candidates more competitive for niche job markets.
Focus on High-Impact Dissertation Research- (Semester 3-4)
Dedicate significant effort to your 32-credit M.Tech dissertation (E0 299). Aim for a research problem with real-world relevance, contributing to academic knowledge or practical solutions. Leverage IISc''''s advanced computing infrastructure and faculty expertise to achieve publishable results.
Tools & Resources
IISc computing clusters, Research labs, Academic databases
Career Connection
A strong dissertation with potential publications significantly boosts credibility for research roles and highly competitive industry positions.
Proactive Industry Engagement and Placement Preparation- (Semester 3-4)
Actively participate in campus placement drives, mock interviews, and resume workshops. Network with alumni and industry professionals through seminars and conferences. Secure internships or capstone projects with companies to gain practical experience, enhancing your readiness for roles as Data Scientists or AI Engineers.
Tools & Resources
IISc Placement Cell, LinkedIn, Industry conferences (e.g., Data Science Congress)
Career Connection
Directly leads to job placements, strengthens professional networks, and provides crucial industry experience for career launch.
Advanced Stage
Program Structure and Curriculum
Eligibility:
- Candidates must possess a Bachelor''''s degree in Engineering/Technology or a Master''''s degree in Science (e.g., MCA, M.Sc. in Computer Science/Mathematics/Statistics/Physics) or an equivalent qualification. A valid GATE score in a relevant discipline (CS, EC, EE, ME, AE, XE, MA, ST) is generally required. Additional eligibility criteria related to undergraduate performance may apply as per IISc admission guidelines.
Duration: 4 semesters / 2 years
Credits: 64 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
Semester 2
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| E0 299 | Dissertation Part 1 | Project | 16 | Research Problem Formulation, Literature Review, Methodology Design, Data Collection and Analysis, Initial Results and Discussion |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| E0 299 | Dissertation Part 2 | Project | 16 | Advanced Research Implementation, Experimental Validation, Result Interpretation, Thesis Writing, Oral Defense |
