Course title
S24120001
Data Science Literacy

 KUROKAWA Yasuhiro
Middle-level Diploma Policy (mDP)
Program / Major mDP Goals
Department of Architecture 3. 自然科学や人文社会科学に関する知識を援用して、建築にかかわるさまざまな事象を論理的に説明することができる
Department of Architecture 3. 自然科学や人文社会科学に関する知識を援用して、建築にかかわるさまざまな事象を論理的に説明することができる
Department of Architecture 3. 自然科学や人文社会科学に関する知識を援用して、建築にかかわるさまざまな事象を論理的に説明することができる
Department of Architecture 5. 豊富な教養と専門知識を統合、駆使して、種々の制約条件や解決するべき課題を整理・分析し、合理的な方法によって建築をデザインすることができる
Department of Architecture 5. 豊富な教養と専門知識を統合、駆使して、種々の制約条件や解決するべき課題を整理・分析し、合理的な方法によって建築をデザインすることができる
Department of Architecture 5. 豊富な教養と専門知識を統合、駆使して、種々の制約条件や解決するべき課題を整理・分析し、合理的な方法によって建築をデザインすることができる
Purpose of class
In almost all engineering research, the handling of data is essential. Furthermore, data science has become inseparable from current and future engineering practices. The objective of this course is to provide students with the necessary literacy for their graduation research by exploring the history and current state of data science as a foundation for engineering studies.
Course description
Students will learn about the history and current state of data science, as well as its applications across various social and research fields. Additionally, the course covers the ethics and regulations required when handling data.
Goals and objectives
  1. Understand data utilization in society and the changes resulting from it.
  2. Learn about the technical domains and technologies for utilizing data and AI.
  3. Understand the current landscape of data and AI utilization.
  4. Learn the rules and regulations for handling data and AI.
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Quizzes Total.
1. 30% 30%
2. 30% 30%
3. 25% 25%
4. 15% 15%
Total. 100% -
Evaluation method and criteria
A quiz, equivalent in difficulty to the exercises in the textbook, will be administered in every class. A total score of 60/100 or higher is required to pass.
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Changes Occurring in Society Read Consortium materials for review 90minutes
Review and complete assignments 60minutes
2. Data Utilized in Society Read Consortium materials for review 90minutes
Review and complete assignments 60minutes
3. Domains of Data/AI Utilization Read Consortium materials for review 90minutes
Review and complete assignments 60minutes
4. Tech for Data/AI Utilization Read Consortium materials for review 90minutes
Review and complete assignments 60minutes
5. Real-world Sites of Data/AI Read Consortium materials for review 90minutes
Review and complete assignments 60minutes
6. Latest Trends in Data/AI Read Consortium materials for review 90minutes
Review and complete assignments 60minutes
7. Ethics and Law in Data/AI Read Consortium materials for review 90minutes
Review and complete assignments 335minutes
Total. - - 1325minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback outside of the class (ScombZ, mail, etc.)
Textbooks and reference materials
Textbook: Data Science as Liberal Arts by Seiichi Uchida et al., Kodansha.

References: Consortium teaching materials.
Prerequisites
None
Office hours and How to contact professors for questions
  • Please consult with the respective instructors.
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
More than one class is interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Wed Apr 29 10:53:59 JST 2026