2025 (Current Year) Faculty Courses School of Materials and Chemical Technology Undergraduate major in Materials Science and Engineering
Exercise on Information Processing
- Academic unit or major
- Undergraduate major in Materials Science and Engineering
- Instructor(s)
- Takuya Hoshina / Keiichi Kuboyama / Xiao-Wen Lei / Akira Takahashi
- Class Format
- Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-8 Mon (情報ネットワーク演習室 第２演習室)
- Class
- -
- Course Code
- MAT.A205
- Number of credits
- 020
- Course offered
- 2025
- Offered quarter
- 1Q
- Syllabus updated
- Mar 28, 2025
- Language
- Japanese
Syllabus
Course overview and goals
With the recent advancements in data science, materials informatics and process informatics—which utilize information science techniques in materials research, development, and manufacturing—have garnered significant attention. Python is extensively used as a primary tool in these fields due to its powerful libraries for data processing and analysis. It is also a language widely adopted beyond data science across various disciplines. In this course, students will learn the basic syntax of Python to ensure that even beginners can effectively use it. Additionally, students will explore methods for data processing using several data processing libraries, enabling them to utilize Python in their laboratory experiments and other practical applications.
Course description and aims
(1) Learning program components (Python)
(2) Learning program structure
(3) Learning experimental data processing methods
Keywords
programming, data processing, python
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The course will be exercise-based.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Guidance and interpretation of system used in this course. Basics and preparation for starting programming in Python. |
Learn about the environment required to use Python |
| Class 2 | Data/variable types and operations |
Learn about Python data and variable types and be able to perform operations on them |
| Class 3 | How to use Python's built-in functions and libraries |
Understand and be able to use built-in functions and libraries |
| Class 4 | Lists, tuples, sets, dictionaries |
Understand lists, tuples, sets, and dictionaries |
| Class 5 | Conditional branching |
Understand and be able to use conditional branching |
| Class 6 | iterative processing |
Understand and be able to use repetitive processes |
| Class 7 | Creating Custom Functions |
Understand how to create your own functions and be able to create them on your own |
| Class 8 | Objects and Classes |
Understand object and class concepts |
| Class 9 | File Input and Output |
Understand and be able to use file input/output methods |
| Class 10 | Plotting Graphs and Peak Detection |
Be able to draw graphs using the external library matplotlib |
| Class 11 | Curve Fitting and Peak Fitting |
Be able to perform curve fitting and peak fitting using external libraries such as SciPy |
| Class 12 | Data Processing with Pandas |
Be able to perform simple data processing using the external library Pandas |
| Class 13 | Trigonometric Functions and Fourier Transform |
Understand trigonometric functions and Fourier transforms |
| Class 14 | Utilizing Materials Libraries and Mathematical Processing |
Understand how to use material system libraries and how to process mathematical equations |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Electronic distribution of proprietary texts
Reference books, course materials, etc.
None
Evaluation methods and criteria
Learning achievement is evaluated by general exercises and exercises about each topic.
Related courses
- MAT.A250 ： Materials Science Laboratory I
- MAT.A251 ： Materials Science Laboratory II
- MAT.A252 ： Materials Science Laboratory III
Prerequisites
None.