Course Name: Dealing with materials data : collection, analysis and interpretation

Course abstract

This course is an introductory course with hands on sessions in R on some basic aspects of materials data. The course will cover all aspects, namely, data collection, analysis and interpretation. All the concepts will be covered with materials data and the hands-on sessions will be conducted using R programming language.


Course Instructor

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Prof. M P Gururajan

My research interests are in modelling microstructural evolution. My teaching interests, among other things, include physical metallurgy, phase transformations, computational methods, simulation and optimization, modelling, mathematical methods and data analysis and interpretation. I am also interested in using mathematical and computational techniques to solve materials problems of academic interest and industrial importance as well as introducing open source software to materials scientists / metallurgists.
More info
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Prof. Hina Gokhale

I am statistician by education and have 25 years of experience of working in Defence Metallurgical Research laboratory, Hyderabad. My experience ranges from analysis of materials data for certification, research and life estimation technologies to training metallurgists for variety of statistical tools such as Design of Experiments and Analysis, Regression Analysis, Analysis of Variance, Neural Networks and Genetic Algorithm, Statistical Modeling and Monte Carlo simulation.

Teaching Assistant(s)

No teaching assistant data available for this course yet
 Course Duration : Jan-Apr 2022

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 Enrollment : 14-Nov-2021 to 31-Jan-2022

 Exam registration : 13-Dec-2021 to 18-Mar-2022

 Exam Date : 24-Apr-2022

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Certificate Eligible

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Certified Category Count

Gold

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Silver

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Elite

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Successfully completed

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Participation

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Success

Elite

Gold





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Final Score Calculation Logic

Enrollment Statistics

Total Enrollment: 390

Assignment Statistics




Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.