Materials Data Analysis Course: Learn Collection, Analysis & Interpretation with R
Course Details
| Exam Registration | 19 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Metallurgy and Material science & Mining Engineering, Minor in Metallurgy, Minor in Materials Science |
| Credit Points | 3 |
| Level | Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 24 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Mastering the Science of Materials Data: A Comprehensive 12-Week Journey
In the evolving field of materials science and engineering, data is the new microscope. The ability to collect, analyze, and interpret materials data effectively is no longer a niche skill but a fundamental requirement for innovation and discovery. Recognizing this critical need, a new postgraduate course, "Dealing with Materials Data: Collection, Analysis and Interpretation," offers a rigorous 12-week immersion into the world of materials informatics.
Led by distinguished experts Prof. M.P. Gururajan from IIT Bombay and Prof. Hina Gokhale from the Defence Metallurgical Research Laboratory (DMRL), this course bridges the gap between traditional materials knowledge and modern data science techniques. It is designed to equip the next generation of scientists and engineers with the tools to extract meaningful insights from complex experimental and computational data.
Course Instructors: A Blend of Computational Expertise and Industrial Experience
The course benefits from a unique instructional team that combines academic depth with extensive practical experience.
Prof. M.P. Gururajan (IIT Bombay) brings expertise in computational materials science, with research interests in modelling microstructural evolution. His teaching covers physical metallurgy, phase transformations, and the application of mathematical and computational techniques to solve real-world materials problems. A proponent of open-source software, he focuses on making powerful tools accessible to practicing metallurgists and materials scientists.
Prof. Hina Gokhale (DMRL, Hyderabad) is a statistician with 25 years of experience analyzing materials data for critical applications like certification, research, and life estimation. Her hands-on experience with statistical tools—from Design of Experiments and Regression Analysis to Neural Networks and Monte Carlo simulation—ensures the course content is grounded in industrial relevance and practical problem-solving.
Who Should Take This Course?
This course is tailored for a diverse audience seeking to strengthen their data-driven materials research capabilities:
- Primary Audience: Postgraduate students in Materials Science, Materials Engineering, Metallurgy, Ceramics Engineering, and Polymers.
- Extended Audience: Students from Physics, Chemistry, Mechanical Engineering, and related disciplines with an interest in materials data.
- Prerequisites: A basic understanding of engineering or first-year mathematics is preferred but not mandatory, making the course accessible to motivated learners from various backgrounds.
A Detailed 12-Week Roadmap to Materials Data Proficiency
The course is meticulously structured to build competency from foundational concepts to advanced applications, with a strong emphasis on hands-on practice using the R programming language.
| Week | Topic | Focus Area |
|---|---|---|
| 1-2 | Introduction & Foundations | Basic probability, statistics, and an introduction to the R programming environment. |
| 3-4 | Data Presentation & Description | Understanding inaccuracies, error propagation, and using R for descriptive data analysis. |
| 5-7 | Probability & Data Processing | Exploring key probability distributions and applying R to process experimental data. |
| 8-9 | Modeling & Visualization | Fitting functions via regression analysis, testing significance, and advanced graphical handling in R. |
| 10-12 | Advanced Methods & Application | Basics of Design of Experiments (DoE), Bayesian inference, and culminating in practical case studies using R. |
Why This Course is Essential for Modern Materials Professionals
The integration of data science into materials research—often called Materials Informatics—is accelerating the discovery of new alloys, polymers, ceramics, and composites. This course addresses the core pillars of this integration:
- From Theory to Practice: Every statistical concept is taught in the context of real materials data, ensuring immediate relevance.
- Hands-On Skill Development: Extensive labs using R, a powerful open-source tool for statistical computing and graphics, provide practical, transferable skills.
- End-to-End Understanding: The curriculum covers the entire data pipeline—from responsible collection and error analysis to sophisticated interpretation and modeling.
- Industry-Relevant Techniques: Coverage of Design of Experiments (DoE), regression analysis, and Bayesian inference prepares students for both academic research and industrial R&D challenges.
Learning Outcomes and Career Impact
Upon completion, participants will be able to:
- Design effective data collection strategies for materials experiments.
- Perform robust statistical analysis and error estimation on materials datasets.
- Use R confidently for data visualization, regression modeling, and simulation.
- Apply Design of Experiments principles to optimize research and testing protocols.
- Interpret complex data to draw scientifically valid and impactful conclusions about material behavior and performance.
This course is more than just a class; it's an investment in a fundamental skill set that is becoming indispensable in materials science, metallurgy, and related engineering fields. By demystifying data analysis and providing powerful tools for interpretation, it empowers researchers to push the boundaries of what's possible in materials discovery and development.
Core Textbook: A Student’s Guide to Data and Error Analysis by Herman J.C. Berendsen (Cambridge University Press, 2011), supplemented by recent literature.
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