2023 Practical Artificial Intelligence and Data Science C 2

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Academic unit or major
School of Computing
Kanezaki Asako  Tomii Norio  Murata Tsuyoshi  Ono Isao  Nitta Katsumi  Kobayashi Takao  Miyake Yoshihiro  Takeshima Shota  Tsunashima Koyori  Kosaka Junichi  Seo Noriaki  Iwashita Yoshinori  Takemoto Shimpei  Arai Ryosuke  Hamaguchi Shun  Hashimoto Shuki  Ueda Tetsuro  Nanri Takuya 
Class Format
Lecture    (Livestream)
Media-enhanced courses
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Offered quarter
Syllabus updated
Lecture notes updated
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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.


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 Applications of DS and AI technologies in Nippon Steel Corporation Digital transformation measures that Nippon Steel Corporation is promoting by utilizing DS and AI technologies
Class 2 Domain Knowledge informed Neural Networks ~Beyond Data Collection~ Learn the importance of understanding the principles behind the subject through the construction of domain knowledge informed neural networks
Class 3 What is DX required in corporate R&D? To learn the practical application and practice of data-driven science in corporate R&D
Class 4 DX Strategies for “Sogo Shosha”, General Trading Company, Learning from the Fields— Practical Examples of Data Analysis and AI Utilization. Understanding DX strategies and use cases of data science and AI in a general trading company.
Class 5 How to use data science in the development of chemical materials Introduction of Data Science Theory and Examples in Chemical Materials Development
Class 6 Data challenges and approaches for unique projects. The plant EPC business requires the handling of a wide variety of data, as each individual project is unique. However, the same data has different meanings and conditions depending on the project situation and background. These characteristics make data utilisation and analysis difficult. What approaches and case studies are available to address these challenges? Learn about specific approaches through case studies of data utilisation.
Class 7 Concept of information system strategy in a chemical industry, required skills and attitude of DS/AI personnel This lecture introduces the concept of information system strategy, required human resources and examples of the use of advanced technology.

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.



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.


Slide distribution and report acceptance will be done by T2SCHOLA.

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