2025 (Current Year) Faculty Courses School of Engineering Undergraduate major in Mechanical Engineering

Computational Mechanics and Data Science Project

Academic unit or major: Undergraduate major in Mechanical Engineering
Instructor(s): Teaching Staffs
Class Format: Experiment (Face-to-face)
Media-enhanced courses: -
Day of week/Period (Classrooms): 5-8 Mon (I3-301, 302, 303) / 1-4 Fri (I3-301, 302, 303)
Class: -
Course Code: MEC.Q321
Number of credits: 004
Course offered: 2025
Offered quarter: 3-4Q
Syllabus updated: Sep 26, 2025
Language: Japanese

Syllabus

Course overview and goals

[Overview]
Data obtained from experiments or previously measured data alone cannot enable us to understand phenomena, manipulate them, or make them controllable. The Computational Mechanics and Data Science Project aims to develop the ability to understand and control phenomena by applying computational models and statistical models to experimental results and data. In this course, teams of a few people will be formed and the work will proceed.

[Objectives]
1. Develop the ability to apply computational mechanics and data science techniques learned in previous courses by tackling challenges with engineering and societal significance.
2. Develop the ability to design projects that utilize computational mechanics and data science to address physical and social phenomena.
3. Develop the project execution capabilities by systematically advancing projects that encompass data exploration, generation, analysis, and discussion.

Course description and aims

By completing this course, students will be able to:
1. Utilize information and data unattainable through experimentation or observation by applying computational mechanics models or statistical models to physical and social phenomena,
2. Plan projects that utilize computational mechanics and data science to examine physical and social phenomena.
3. Execute projects where groups collaborate to consistently perform data exploration, generation, analysis, and examination.

Keywords

Computational Mechanics, Data Science, Simulation, Statistical Data Analysis

Competencies

Specialist skills
Intercultural skills
Communication skills
Critical thinking skills
Practical and/or problem-solving skills

Class flow

This course is primarily group-based. During the first three quarters (3Q), students with shared interests form groups to plan projects utilizing computational mechanics and data science. In the final quarter (4Q), each group designs, implements, and verifies their project based on their discussions. Students present their project outcomes and receive evaluations.

Course schedule/Objectives

	Course schedule	Objectives
Class 1	Introduction to Computational Mechanics and Data Science Project	Assignments will be given as appropriate.
Class 2	Computational Mechanics and Data Science Project Presentation	Assignments will be given as appropriate.
Class 3	Group Formation and Project Preparation	Assignments will be given as appropriate.
Class 4	Project Planning (Research on Related Technical Data, etc.)	Assignments will be given as appropriate.
Class 5	Project Planning (Research on Related Technical Data, etc.)	Assignments will be given as appropriate.
Class 6	Project Planning(Creating an overall concept for data exploration, generation, analysis, etc.)	Assignments will be given as appropriate.
Class 7	Project Planning(Creating an overall concept for data exploration, generation, analysis, etc.)	Assignments will be given as appropriate.
Class 8	Project Planning(Creating an overall concept for data exploration, generation, analysis, etc.)	Assignments will be given as appropriate.
Class 9	Project Planning(Creating an overall concept for data exploration, generation, analysis, etc.)	Assignments will be given as appropriate.
Class 10	Project Planning(Creating an overall concept for data exploration, generation, analysis, etc.)	Assignments will be given as appropriate.
Class 11	Project Planning(Creating an overall concept for data exploration, generation, analysis, etc.)	Assignments will be given as appropriate.
Class 12	Preparations for Project Plan Presentation	Assignments will be given as appropriate.
Class 13	Project Plan Presentation	Preparations for the Presentation
Class 14	Feedback on the Project Plan Presentation	Assignments will be given as appropriate.
Class 15	Project Implementation	Assignments will be given as appropriate.
Class 16	Project Implementation	Assignments will be given as appropriate.
Class 17	Project Implementation	Assignments will be given as appropriate.
Class 18	Project Implementation	Assignments will be given as appropriate.
Class 19	Project Implementation	Assignments will be given as appropriate.
Class 20	Project Verification (Methodology for Effectiveness Evaluation)	Assignments will be given as appropriate.
Class 21	Project Verification (Effectiveness Evaluation)	Assignments will be given as appropriate.
Class 22	Project Verification (Effectiveness Evaluation)	Assignments will be given as appropriate.
Class 23	Project Review (Effectiveness Evaluation Analysis)	Assignments will be given as appropriate.
Class 24	Project Review (Assessment and Improvements)	Assignments will be given as appropriate.
Class 25	Preparing for the project presentation (creating presentation materials)	Assignments will be given as appropriate.
Class 26	Preparing for the project presentation (creating presentation materials)	Preparing for the final presentation
Class 27	Project Presentation and Discussion	Submission of deliverables (products, videos, reports, etc.)
Class 28	Revisions based on the results of the project presentation	Submission of deliverables (products, videos, reports, etc.)

Study advice (preparation and review)

To enhance effective learning, students are encouraged to spend a certain length of time outside of class on preparation and review (including for assignments), as specified by the Tokyo Institute of Technology Rules on Undergraduate Learning (東京科学大学学修規程) and the Tokyo Institute of Technology Rules on Graduate Learning (東京科学大学大学院学修規程), for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

None

Reference books, course materials, etc.

Lecture materials will be distributed via the LMS as needed.

Evaluation methods and criteria

Grades will be based on a 50% report and 50% group work. Group work will be evaluated comprehensively based on the presentation of concepts, performance at the presentation session, and the level of contribution within the team.

Related courses

XEG.B101 ： Engineering Literacy I
XEG.B102 ： Engineering Literacy II
XEG.B103 ： Engineering Literacy III
XEG.B104 ： Engineering Literacy IV
MEC.A202 ： Mechanical engineering literacy
MEC.B201 ： Fundamentals of information and mathematical sciences
MEC.B221 ： Statistical data analysis
MEC.B222 ： Fundamentals of computational mechanics
MEC.K332 ： Finite Element Analysis
MEC.B334 ： Time Sequencial Data Analysis

Prerequisites

Limited to students enrolled in the Department of Mechanical Engineering. Additionally, students must have already earned credits for either “MEC.Q201: Mechanical Systems Engineering” or “MEC.A202: Mechanical Engineering Literacy.”