2022 Faculty Courses School of Environment and Society Undergraduate major in Transdisciplinary Science and Engineering
Applied programming and numerical analysis
- Academic unit or major
- Undergraduate major in Transdisciplinary Science and Engineering
- Instructor(s)
- Daisuke Akita / Kunio Takahashi
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-4 Wed (学術国際情報センター３F 第１実習室)
- Class
- -
- Course Code
- TSE.A324
- Number of credits
- 110
- Course offered
- 2022
- Offered quarter
- 4Q
- Syllabus updated
- Jul 10, 2025
- Language
- English
Syllabus
Course overview and goals
The purposes of the first half of this class are to learn a basic programming technique of Python and to be able to make programs for numerical analysis by succeeding "Programming and numerical analysis". Students learn from basic grammar of Python to data structures such as list and array. And students learn algorithms of statistical processing and signal and image processing, and practice programmings.
The purpose of the second half of this class is to develop practical abilities such as applied numerical simulation methods, visualization of results by succeeding "Programming and numerical analysis". In the group work, students will experience not only numerical simulation or programming but also problem setting, modeling, and evaluation of results.
Course description and aims
Students can obtain the following abilities by this lecture
(1) Basic grammar of Python
(2) Algorithms of statistical processing and signal and image processing
(3) Basic programming
Keywords
Programming,numerical analysis, algorithm, Python, statistical processing, signal and image processing, modeling, visualization
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
In the beginning part of each class, students learn about grammar of a programming language and algorithms. After that, make programs based on them.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Basic grammar and control of Python | Be able to make simple programs of Python with branch and loops. |
| Class 2 | Basic data structure and sort | Be able to make programs of Python with list and array to sort them. |
| Class 3 | Statistical processing | Be able to make programs of Python to calculate average and variance from data and perform a statistical test. |
| Class 4 | Visualization of results and 3D modeling using Paraview | Be able to visualize results using Paraview |
| Class 5 | Newtonian flow simulation | Be able to perform Newtonian flow simulation |
| Class 6 | Review of simultaneous equation and solving method of elliptic partial differential equation | Be able to solve elliptic partial differential equation numerically. |
| Class 7 | Group work | Prepare for the group presentation |
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)
None
Reference books, course materials, etc.
John V. Guttag, "Introduction to Computation and Programming Using Python," MIT Press, 2013.
Evaluation methods and criteria
Learn programming and be able to make programs by using algorithms of numerical analysis.
Practices and reports (100%)
Related courses
- TSE.A307 ： Programming and numerical analysis
Prerequisites
None