About the Website
- ✓ Image processing using Python libraries
- ✓ A step-by-step guide to materials data analysis via machine learning and deep learning
- ✓ Review of recent developments in the field of materials informatics
- I will share the codes developed by me and also provide tutorials on how to use publicly available open source codes related to multiscale materials design and informatics.
The Significance of Learning Data Analysis for Materials
Generally, most researchers (experimentalists) conduct innumerable experiments to reach the final target. In this process, we generate tons of data that involves enormous consumption of energy, resources, and, most importantly, time. The whole activity has a direct impact on our climate, too. Therefore, designing and performing relevant experiments by analyzing pre-existing data to predict new or improved materials via materials informatics and analytics is the need of the hour.
Click on blogs and tutorials to begin your adventure with materials data.
Latest Posts
- How to Read Confusion Matrix for Multi-Class Classification
- How to Apply Ordinal Logistic Regression for prediction of Materials Properties
- How to Generate Hypothetical Dataset for Machine Learning
- How to Apply Binary Logistic Regression Analysis for Prediction of Materials Properties
- Assessing Evaluation Metrics for Regression Analysis with a Single Input Variable (Part I)
- Assessing Evaluation Metrics for Regression Analysis with Multiple Input Variables (Part II)
April 2024
March 2024
February 2024
About me
I am Joyita. I am a materials scientist, a microscopist by training to be specific, and a self-taught materials data analyst. My goal for this website is to help those interested in using data analytics tools to navigate through materials data and discover hidden correlations among processes, microstructures, properties, and performances. Below is the link to my LinkedIn profile.
