IGESSIGESS (Institute for Global Economics and Social Sciences)
ECN400CB（経済学 / Economics 400）SeminarⅣSeminarⅣ
ROBERT M SINCLAIRRobert Michael SINCLAIR
授業コードなどClass code etc
| 学部・研究科Faculty/Graduate school | IGESSIGESS (Institute for Global Economics and Social Sciences) |
| 添付ファイル名Attached documents | |
| 年度Year | 2021 |
| 授業コードClass code | K7063-402 |
| 旧授業コードPrevious Class code | |
| 旧科目名Previous Class title | |
| 開講時期Term | 秋学期授業/Fall |
| 曜日・時限Day/Period | 木5/Thu.5 |
| 科目種別Class Type | |
| キャンパスCampus | 多摩 |
| 教室名称Classroom name | |
| 配当年次Grade | |
| 単位数Credit(s) | 2 |
| 備考（履修条件等）Notes | |
| 他学部公開科目Open Program | |
| 他学部公開（履修条件等）Open Program (Notes) | |
| グローバル・オープン科目Global Open Program | |
| 成績優秀者の他学部科目履修制度対象Interdepartmental class taking system for Academic Achievers | |
| 成績優秀者の他学部科目履修（履修条件等）Interdepartmental class taking system for Academic Achievers (Notes) | |
| 実務経験のある教員による授業科目Class taught by instructors with practical experience | |
| SDGsCPSDGs CP | |
| アーバンデザインCPUrban Design CP | |
| ダイバーシティCPDiversity CP | |
| 未来教室CPLearning for the Future CP | |
| カーボンニュートラルCPCarbon Neutral CP | |
| 千代田コンソ単位互換提供（他大学向け）Chiyoda Campus Consortium | |
| カテゴリーCategory |
Advanced Courses／専門科目 Disciplinary Courses／IGESS科目 Ⅶ. Seminar |
| 科目主催学部Faculty Sponsored Department |
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授業の概要と目的（何を学ぶか）Outline and objectives
Big Data is a rapidly developing field which is changing our world in ways which are still difficult to predict. Our first objective will be to gain an understanding of current trends, both positive and negative, from detailed studies of current news reports. Technical background will only be discussed where it is absolutely necessary for understanding the impact of Big Data on society and business. Our main objective is for each student to study those aspects of Big Data which can be expected to impact on the field in which they intend to work. For this reason, each student will be encouraged to make presentations and write reports, including a thesis, so that they can communicate what they are learning, and also so that students can learn from each other.
到達目標Goal
The goal is to help all students to reach a level of understanding of Big Data (including AI) that will be useful in their job hunting and future careers. This course is held in the belief that an understanding of Big Data and AI can directly translate into future salary raises and promotions, even for those whose work is not directly related to the development of Big Data related technology.
授業で使用する言語Default language used in class
英語 / English
授業の進め方と方法Method(s)（学期の途中で変更になる場合には、別途提示します。 /If the Method(s) is changed, we will announce the details of any changes. ）
Active learning is central to this course. We will be constantly in discussion. Flexibility of thought and association (working in groups or individually) will be encouraged where meaningful, but always with the goal of helping each individual student to achieve their best. We will also regularly cover the international news regarding Big Data and AI, and all students will be encouraged to report on news stories, particularly from their own country.
アクティブラーニング（グループディスカッション、ディベート等）の実施Active learning in class (Group discussion, Debate.etc.)
あり / Yes
フィールドワーク（学外での実習等）の実施Fieldwork in class
なし / No
授業計画Schedule
※各回の授業形態は予定です。教員の指示に従ってください。
1：AI
Introductory lecture, introducing the need for AI in the analysis of Big Data.
2：AI
Discussion of ethical issues raised by AI.
3：AI
Discussion of possible limits of AI.
4：AI
Students should investigate the impact of AI on business in Japan and other countries.
5：AI
Student presentations (group or individual).
6：Data Security
Discussion concerning the data security challenges associated with the combination of Big Data and AI.
7：Prediction
Students should work in groups or individually, studying the impact on their own field of interest.
8：Prediction
Students should attempt to predict something of interest to them, using publicly available data.
9：Prediction
Student presentations (group or individual). Of particular relevance are the difficulties that were encountered.
10：Project work
Students must decide whether to work individually or in groups, and also decide on a topic.
11：Project work
All groups must describe their topic to the class.
12：Project work
All groups must make a detailed presentation of their project’s current status.
13：Project work
Writing of reports.
14：Project work
Completion and submission of final reports.
授業時間外の学習（準備学習・復習・宿題等）Work to be done outside of class (preparation, etc.)
All students should be checking the international news daily, looking for reports relating to Big Data and/or AI. If the report is specific to a language other than English, students should be prepared to give a summary of the news in class in English. This will require two hours of work outside of class per week.
テキスト（教科書）Textbooks
No textbook will be required.
参考書References
The field of Big Data is changing so rapidly that we will not have any fixed references.
成績評価の方法と基準Grading criteria
There will be no examination.
Reports written for project work (every student must write their own report) will count for 50%.
Presentations made in class will count for 20%.
Homework assignments will count for 20%.
Class participation in discussions will count for 10%.
学生の意見等からの気づきChanges following student comments
This course is for the benefit of the students, and student feedback will be expressly encouraged. Since the field of Big Data and AI is changing very quickly, it will be important to have the flexibility to make changes to the course, even on a weekly basis when appropriate. Student comments will be a vital consideration in making changes, since any changes must be motivated by the goal of helping students in their future careers. Those students who wish to learn about technical aspects, such as advanced usage of Excel or database software, will be encouraged to do so.
学生が準備すべき機器他Equipment student needs to prepare
Students must always bring notepads and pens/pencils. It would be useful if students could bring their own laptop computers to class.