2023 Faculty Courses School of Computing Major courses
Practical Artificial Intelligence and Data Science C 1
- Academic unit or major
- Major courses
- Instructor(s)
- Asako Kanezaki / Tsuyoshi Murata / Norio Tomii / Isao Ono / Katsumi Nitta / Takao Kobayashi / Yoshihiro Miyake / Ryuji Sakata / Yoshiaki Oida / Takumi Arai / Kazuyuki Kimura / Yuji Iwasaki / Yasushi Hanatsuka / Tppei Mori / Yoshihisa Kiyota / Motofumi Fukui
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Fri
- Class
- 1
- Course Code
- XCO.T495
- Number of credits
- 100
- Course offered
- 2023
- Offered quarter
- 3Q
- Syllabus updated
- Jul 8, 2025
- Language
- Japanese
Syllabus
Course overview and goals
The purpose of this course is to understand the current status of social implementation of AI and data science technologies and cutting-edge technologies, and to examine the applicability and challenges of these technologies. Trends and issues in technology and product development in the fields of Pharmaceutical, Machine Learning, Data Utilization, New Business Development, etc. will be explained in each class as shown in the course schedule.
Course description and aims
The goal of this course is for students to acquire knowledge of AI and data science technologies in various fields, and to gain a broader perspective that will enable them to play an active role in the real world by discussing social applications and explaining new ideas in assignment reports.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
This course will be taught by lecturers from (Class 1) Bridgestone, NGK, Panasonic, Sumitomo Heavy Industries, Fujitsu, and Mitsubishi UFJ Bank, and (Class 2) Idemitsu Kosan, Nippon Steel, Nissan Motor, Sumitomo Corporation, Toyo Engineering, Resonac and DIC, based on their practical experience.
Keywords
Data Science, Artificial Intelligence, FinTech, Manufacturing, Construction, Machine Learning, Data Utilization, New Business Development
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Class1－Class7： Lectures
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Bridgestone's data utilization and time series analysis using IoT data | Experience developing algorithms to convert tire data into useful information |
| Class 2 | Bridgestone's data utilization and time series analysis using IoT data | Experience developing algorithms to convert tire data into useful information |
| Class 3 | Data science and its real-world applications in manufacturing companies | In addition to the extraction of firing conditions for ceramics in materials R&D, we will introduce the current status of data-driven corporate activities based on the use of digital technologies, including data science and AI |
| Class 4 | Kaggle and Practical Applications of Data Science | Learn the necessary knowledge to make use of data science and machine learning technology in the real world. |
| Class 5 | Information technology for heavy machinery. | Relationships and issues between heavy machinery, people, and information technology |
| Class 6 | Design and Execution of AI Implementation Projects | This course introduces multiple real-world examples of practical AI implementation projects and provides an overview of key success factors of the project management. |
| Class 7 | Application of Data Science in Financial Market | This session provides overview of applications of data science in foreign exchange market, especially from commercial bank perspective. |
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.
Materials will be provided on T2SCHOLA in advance and shared in the Zoom lecture
Evaluation methods and criteria
Mainly short report required in each class will be considered
Related courses
- XCO.T487 ： Fundamentals of data science
- XCO.T488 ： Exercises in fundamentals of data science
- XCO.T489 ： Fundamentals of artificial intelligence
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
- XCO.T483 ： Advanced Artificial Intelligence and Data Science A
- XCO.T484 ： Advanced Artificial Intelligence and Data Science B
- XCO.T485 ： Advanced Artificial Intelligence and Data Science C
- XCO.T486 ： Advanced Artificial Intelligence and Data Science D
Prerequisites
Both credits of Practical Artificial Intelligence and Data Science C-1 and C-2 cannot be obtained. Priority may be given to students enrolled in the Progressive Graduate Minor in Data Science and Artificial Intelligence.
Other
Slide distribution and report acceptance will be done by T2SCHOLA.