| 学部・研究科Faculty/Graduate school | GISDepartment of Global and Interdisciplinary Studies |
| 添付ファイル名Attached documents | |
| 年度Year | 2023 |
| 授業コードClass code | A6241 |
| 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2/Thu.2 |
| 科目種別Class Type | |
| キャンパスCampus | 市ヶ谷 / Ichigaya |
| 教室名称Classroom name | 市外濠‐S601 |
| 配当年次Grade | 2～4 |
| 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 | |
| 旧科目との重複履修Duplicate Subjects Taken Under Previous Class Title | |
| カテゴリー（2024年度以降入学者）Category (commenced 2024 onwards) | |
| カテゴリー（2020～2023年度入学者）Category (commenced 2020-2023) | 200-level Intermediate Courses（中級） |
| カテゴリー（2016～2019年度入学者）Category (commenced 2016-2019) | 200-level Intermediate Courses Culture and Society |
【授業の概要と目的（何を学ぶか） / Outline and objectives】
This class aims at building a strong understanding of big data and analytics in terms of its concept and real-world cases. Also, this class includes practical learning of big data and analytics at a basic level, so that students can experience data analysis on their own.
【到達目標 / Goal】
Students will understand "what big data and analytics is," "how big data and analytics are utilized within organizations," and "what challenges modern companies have to embed big data analytics into their organizational fabric." These topics are timely and emerging issues to grasp an overall picture of business landscape in a digital society. Also, by practically learning how to analyze data, students will have practical learning experiences in data analysis.
【この授業を履修することで学部等のディプロマポリシーに示されたどの能力を習得することができるか（該当授業科目と学位授与方針に明示された学習成果との関連） / Which item of the diploma policy will be obtained by taking this class?】
Will be able to gain “DP 1”, “DP 2”, “DP 3”, and “DP 4”.
【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. ）
This course will proceed with lectures, readings, group presentations, and practical learning. Students are required to read each chapter of references distributed by an instructor in advance and submit a summary of it.
For the first half of the semester, the class starts with partial lectures from an instructor, and students will participate in group presentations and subsequent discussions.
Also, for another half of the semester, the class starts with lectures from an instructor about programming, and students will do practical learning on their own. During practical learning sessions, an instructor will lead Q&A for each group or try to give individual instructions if possible.
At the beginning of class, feedback for the previous class is given using some comments from submitted weekly papers.
【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】
授業形態 / methods of teaching：対面/face to face
※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。
| 回 / No. | 各回の授業形態予定 / methods of teaching | テーマ / Theme | 内容 / Contents |
|---|---|---|---|
| 1 | オンライン/online | Introduction of Course | Introduction of Course |
| 2 | 対面/face to face | Chapter 1: Big Data and Analytics | This class covers the definition of big data (in terms of data structure, volume, velocity) and studies tools that can make data into assets. |
| 3 | 対面/face to face | Chapter 2: Big Data in Business | This class covers how modern enterprise turns big data into business value. |
| 4 | 対面/face to face | Chapter 3: Big Data in Practice (Amazon & Etsy) | This class is focused on understanding real-world examples of modern companies that successfully utilize big data including Amazon and Etsy. |
| 5 | 対面/face to face | Chapter 4: Big Data in Practice (Ralph Lauren & Apixio) | This class is focused on understanding real-world examples of modern companies that successfully utilize big data including Ralph Lauren and Apixio. |
| 6 | 対面/face to face | Chapter 5: Big Data in Practice (Uber & Transport for London) | This class is focused on understanding real-world examples of modern companies that successfully utilize big data including Uber and Transport for London. |
| 7 | 対面/face to face | Chapter 6: Python Programming_Class & Object | This class covers basic programming concept (Class & Object) and practice learning. |
| 8 | 対面/face to face | Chapter 7: Python Programming_Crawling | This class covers crawling code patterns and practice. |
| 9 | 対面/face to face | Chapter 8: Understanding Web Structure | This class helps students learn Web structure & HTML. |
| 10 | 対面/face to face | Chapter 9: Web Crawling | This class reviews web structures and practically learns web crawling based on the understanding of web structure. |
| 11 | 対面/face to face | Chapter 10: Understanding Web Structure & CSS | This class aims at understanding web structure & CSS and practically learning web crawling based on the understanding of HTML & CSS. |
| 12 | 対面/face to face | Chapter 10: Web Crawling_Intermediate Practices | This class provides some technical tips regarding web crawling and practically learns web crawling using CSS selector |
| 13 | 対面/face to face | Chapter 11: Practice Web Crawling_Real World Cases | This class practically learns web crawling using real-world cases like a shopping mall site & a portal web site. |
| 14 | 対面/face to face | Wrap-up & Final Exam | This class reviews what we have covered throughout the semester and takes the final exam. |
【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.)】
Preparatory study and review time for this class are 2 hours each.
【テキスト（教科書） / Textbooks】
Handouts and reading materials will be provided by lecturer.
【参考書 / References】
1. Rogers, D. (2016). The digital transformation playbook. Columbia University Press.
2. Marr, B. (2016). Big data in practice: how 45 successful companies used big data analytics to deliver extraordinary results. John Wiley & Sons.
3. Python Basics: A Practical Introduction to Python 3 (English Edition)David Amos, Dan Bader, Joanna Jablonski, Fletcher Heisler, Real Python (2022/1/24),
【成績評価の方法と基準 / Grading criteria】
Participation (20%); Weekly assignment (20%);
Group presentation (30%); Final exam (30%).
【学生の意見等からの気づき / Changes following student comments】
Not applicable
【学生が準備すべき機器他 / Equipment student needs to prepare】
1.A notebook, the references provided by an instructor
2.Laptop (*downloaded with 'anaconda' and 'jupyter notebook')
【その他の重要事項 / Others】
It is highly recommended to take "Introduction to Programming" first before taking this class.
【Prerequisite】
None.