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- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Kanezaki Asako Murata Tsuyoshi Tomii Norio Ono Isao Nitta Katsumi Kobayashi Takao Miyake Yoshihiro Sakata Ryuji Oida Yoshiaki Arai Takumi Kimura Kazuyuki Iwasaki Yuji Hanatsuka Yasushi Mori Tppei Kiyota Yoshihisa Fukui Motofumi
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Fri7-8()
- Group
- 1
- Course number
- XCO.T495
- Credits
- 1
- Academic year
- 2023
- Offered quarter
- 3Q
- Syllabus updated
- 2023/9/27
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
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.
Student learning outcomes
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.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| 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 that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Class1－Class7： Lectures
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| 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. |
Out-of-Class Study Time (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
Assessment criteria and methods
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 (i.e., required knowledge, skills, courses, etc.)
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.
