2023 Applied Artificial Intelligence and Data Science B

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Academic unit or major
School of Computing
Kanezaki Asako  Tomii Norio  Miyazaki Kei  Okumura Keiji  Sakuma Jun  Ono Isao  Miyake Yoshihiro  Nitta Katsumi  Kobayashi Takao  Akashika Hideki  Kaji Yusuke  Hirate Yu  Nishizawa Yusuke  Iwakabe Koichi  Okada Koichi  Kawano Yukihiro 
Class Format
Lecture    (Livestream)
Media-enhanced courses
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Course description and aims

The purpose of this course is to understand the current status and state-of-the-art of social implementation of AI and data science technologies, and to examine the applicability and challenges of these technologies. In each class session as indicated in the lesson plan, trends and issues in technology and product development in the fields of IT, telecommunications, architecture, chemical industry, heavy industry, etc. will be explained.

Student learning outcomes

This course aims to provide students with a broad perspective on AI and data science technologies in a variety of fields, and to help them gain a broad view of the real world by explaining new ideas and considering social applications through assignment reports.

Course taught by instructors with work experience

Applicable How instructors' work experience benefits the course
The purpose of this lecture is to introduce the practical experience of engineers from companies (Rakuten Group, Inc., Kajima Corporation, Mitsui Chemicals, Inc., Nippon Telegraph and Telephone East Corporation, and IHI Corporation) who are implementing data science and artificial intelligence technologies in society.


Data science, artificial intelligence, deep learning, machine learning, IT companies, telecommunications, construction, chemical industry, heavy industry

Competencies that will be developed

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

Class flow

This course requires students to take an active role in their own learning. It is required to submit a summary report after each class.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Notes and development examples for building large-scale web services The lecture will introduce the contents to be considered and matters to be noted when building large-scale Web services, based on case studies, as well as examples of development in payments.
Class 2 Career development in the data science and ML field Lecture on how project experience in a company can help you to become a data scientist or ML engineer.
Class 3 Examples of AI-related R&D at Rakuten Group Using examples of AI-related R&D at Rakuten Group, the presentation will show how corporate R&D organizations contribute to business.
Class 4 Data Science and AI Applications in Architectural Firms Presenting a case study of the use of data science and AI in an architectural firm
Class 5 Using Data Science in R&D for an Integrated Chemical Company With the prosperity of data science, companies are also making efforts to utilize data science in their business. In this lecture, we will introduce the approaches and applications of "digital science," including computational chemistry and CAE, in the research and development of materials. A report summarizing students' impressions of the lecture content will be assigned as an assignment for this lecture.
Class 6 Applying a challenge of data utilization hackathon to real business Hackathons are not just competitions to realize ideas through coding, but good opportunities to improve skills and chances to create your work. And we can apply them to real business. The lecturer of this course, a successive three-year first-prize winner of an international hackathon "Asia Opendata Challenge," introduces a case of how he applied his work for the business of NTT East, his workplace. And you can learn actual cases of business analytics in this lecture.
Class 7 Application of AI/Data Analysis Technology in Heavy Industries Understand how ai/data analysis is used in the manufacturing industry and examples. As main contents, abnormality diagnosis technology, text analysis technology, and deterioration diagnosis technology are taken up.

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 course material.


None required

Reference books, course materials, etc.

Materials will be provided on T2SCHOLA in advance.

Assessment criteria and methods

Mainly assignment reports required in each class will be considered

Related courses

  • XCO.T483 : Applied Artificial Intelligence and Data Science A
  • XCO.T485 : Applied Artificial Intelligence and Data Science C
  • XCO.T486 : Applied Artificial Intelligence and Data Science D

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students of doctoral course are required to register for Progressive Applied Artificial Intelligence and Data Science B(XCO.T688) instead of Advanced Artificial Intelligence and Data Science B (XCO.T484)

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