This workshop is an introduction to Materials Informatics – using data science concepts to support answering materials science questions. This will be a hands-on workshop where you will learn primarily by doing.
Prerequisites
- Some familiarity with a programming language
- Knowledge or interest in materials science
| Setup | Download files required for the lesson | |
| 00:00 | 1. Introduction |
What is this workshop about?
Why take a programmatic approach to materials informatics? How do I get the most out of this workshop? |
| 00:10 | 2. Introduction to Python and Jupyter |
What is python, and how do I use it?
What is jupyter, and how do I use it? |
| 01:10 | 3. Introduction to the Citrination API | What is an API, and how do I use it? |
| 02:10 | 4. Data Cleaning Workshop | How can I make my data ready for machine learning? |
| 03:10 | 5. Programmatic Data Operations | Why use programming to operate on data? |
| 04:10 | 6. Principles of Visualization |
How are numbers displayed visually?
How do we design effective visualizations? |
| 05:00 | 7. Visualizing in Python with matplotlib | How do we visualize data in Python? |
| 06:00 | 8. How To Not Get Fooled by Machine Learning |
What is the danger zone?
How do I train a machine learning model? Why should I learn some statistics to do machine learning? |
| 07:00 | 9. Introduction to Sequential Learning | What is sequential learning? |
| 08:00 | 10. Suggestions for Further Study | How do I learn more? |
| 08:00 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.