| 授業名 | Data Visualization |
|---|---|
| Course Title | Data Visualization |
| 担当教員 Instructor Name | Minjeong Ham |
| 授業形態 Class Type | 講義 Regular course |
| 授業形式 Class Format | On Campus |
| 単位 Credits | 2 |
| 言語 Language | EN |
| 科目区分 Course Category | 専門教育科目 / Specialized Subject |
| 学位 Degree | BBA |
| 開講情報 Terms / Location | 2025 UG Nisshin Term4 |
| コード Couse Code | NUC420_N25B |
授業の概要 Course Overview
Mission Statementとの関係性 / Connection to our Mission Statement
This course aligns with NUCB's mission of educating innovative and ethical leaders with "Frontier Spirits" by facilitating students to understand the core concepts of data visualization and gain insights from practices in regional and global business contexts.
授業の目的（意義） / Importance of this course
Data visualization is the presentation of data in pictorial or graphical form. In today’s data-driven business world, data visualization is an essential skill required for managers. Good data visualization can communicate ideas effectively, help people to make sense of big data, and enable data-driven decisions. This course aims to introduce the basics of data visualization and enable students to turn messy data and boring information into smart and effective visualizations that powerfully convey ideas. Students will learn from case studies in various business contexts with hands-on practice using various data visualization tools.
学修到達目標 / Achievement Goal
By studying this course, students will get familiarize with the fundamental concepts of data visualization and be able to apply these concepts in real-world business situations.
本授業の該当ラーニングゴール Learning Goals
*本学の教育ミッションを具現化する形で設定されています。
LG1 Critical Thinking
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Managerial Perspectives (BBA)
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Managerial Perspectives (BBA)
受講後得られる具体的スキルや知識 Learning Outcomes
Upon successful completion of the course, students should be able to
1. Understand the fundamental concepts of data visualization.
2. Appreciate various data visualization designs, tools and techniques.
3. Critically evaluate the effectiveness and persuasiveness of various data visualizations.
4. Design and use persuasive data visualizations for effective communication.
1. Understand the fundamental concepts of data visualization.
2. Appreciate various data visualization designs, tools and techniques.
3. Critically evaluate the effectiveness and persuasiveness of various data visualizations.
4. Design and use persuasive data visualizations for effective communication.
SDGsとの関連性 Relevance to Sustainable Development Goals
Goal 4 質の高い教育をみんなに（Quality Education）
教育手法 Teaching Method
| 教育手法 Teaching Method | % of Course Time | |
|---|---|---|
| インプット型 Traditional | 10 % | |
| 参加者中心型 Participant-Centered Learning | ケースメソッド Case Method | 50 % |
| フィールドメソッド Field Method | 40 % | 合計 Total | 100 % |
事前学修と事後学修の内容、レポート、課題に対するフィードバック方法 Pre- and Post-Course Learning, Report, Feedback methods
The course contents will include lecture, class presentations, case studies/hands-on practices and other discussion materials provided beforehand or brought into the class by the instructor. The format of this course will follow lectures, discussions, in-class exercises and hands-on practices with an emphasis on design, evaluation and application of data visualizations in various organizational contexts.
A computer with internet connection is required for in-class exercises and hands-on practices during the class.
Readings (lecture materials, relevant articles or cases) are provided beforehand and assigned for each class. Students are required to prepare for at least 2-3 hours per class in this course. The emphasis will be on student responsibility for learning through active participation in various in-class activities.
Constructive feedback will be given to students for their in-class activities as and when appropriate.
A computer with internet connection is required for in-class exercises and hands-on practices during the class.
Readings (lecture materials, relevant articles or cases) are provided beforehand and assigned for each class. Students are required to prepare for at least 2-3 hours per class in this course. The emphasis will be on student responsibility for learning through active participation in various in-class activities.
Constructive feedback will be given to students for their in-class activities as and when appropriate.
授業スケジュール Course Schedule
第1日（Day1）
Theme: Introduction to Data Visualization●使用するケース
Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations (HBS) *第2日（Day2）
Theme: Visualization Design and Tools●使用するケース
Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations (HBS) *第3日（Day3）
Theme: Understanding and Exploring Data●使用するケース
Iuiga’s Challenge: Is Omni-Channel Worth It (HBS) *第4日（Day4）
Theme: Infographics●使用するケース
Data Visualization Case Scenario I (Original Case) *第5日（Day5）
Theme: Dashboard●使用するケース
Data Visualization Case Scenario II (Original Case) *第6日（Day6）
Theme: Storytelling with Data●使用するケース
Data Visualization Case Scenario III (Original Case) *第7日（Day7）
Theme: Project Presentations●使用するケース
Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations (HBS) *成績評価方法 Evaluation Criteria
*成績は下記該当項目を基に決定されます。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
Class Contribution includes but is not limited to:
discussion participation, in-class exercises, hand-on practices, and other in-class activities.
• Active participation is required and expected.
• Further information about the case assignments and final report will be given on Google Classroom.
• Selected case assignments must be submitted before case discussions as per given deadlines.
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
| 講師用内規準拠 Method of Assessment | Weights |
|---|---|
| コールドコール Cold Call | 0 % |
| 授業内での挙手発言 Class Contribution | 70 % |
| クラス貢献度合計 Class Contribution Total | 70 % |
| 予習レポート Preparation Report | 0 % |
| 小テスト Quizzes / Tests | 0 % |
| シミュレーション成績 Simulation | 0 % |
| ケース試験 Case Exam | 0 % |
| 最終レポート Final Report | 30 % |
| 期末試験 Final Exam | 0 % |
| 参加者による相互評価 Peer Assessment | 0 % |
| 合計 Total | 100 % |
評価の留意事項 Notes on Evaluation Criteria
Quizzes / Tests = Project PresentationClass Contribution includes but is not limited to:
discussion participation, in-class exercises, hand-on practices, and other in-class activities.
• Active participation is required and expected.
• Further information about the case assignments and final report will be given on Google Classroom.
• Selected case assignments must be submitted before case discussions as per given deadlines.
教科書 Textbook
- 「 」 （）
参考文献・資料 Additional Readings and Resource
Scott Berinato (2016). Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations. Harvard Business Review Press.
Kristen Sosulski. (2019). Data Visualization Made Simple: Insights into Becoming Visual. Routledge.
Scott Berinato (2016). Visualizations That Really Work. Harvard Business Review.
Nussbaumer Knaflic (2015). Storytelling with Data. Wiley.
Additional relevant readings will be provided as and when appropriate.
Kristen Sosulski. (2019). Data Visualization Made Simple: Insights into Becoming Visual. Routledge.
Scott Berinato (2016). Visualizations That Really Work. Harvard Business Review.
Nussbaumer Knaflic (2015). Storytelling with Data. Wiley.
Additional relevant readings will be provided as and when appropriate.
授業調査に対するコメント Comment on Course Evaluation
担当教員のプロフィール About the Instructor
Minjeong Ham is an Assistant Professor at NUCB. She received her Ph.D. in Information Systems from Yonsei University, Seoul, South Korea. She was a postdoctoral fellow at Korea University before joining NUCB. Her research interests include Information Systems adoption and usage in digital business, especially in the creative industry. A significant aspect of her research centers on privacy concerns in personalized advertising, examining the delicate balance between user data protection and effective ad targeting.
Refereed Articles
- (2026) Antidote for the personalization-privacy paradox: does algorithm transparency trigger more ad click-through intention than algorithm literacy?. Internet Research forthcoming
- (2026) Rethinking targeting strategies for SMEs: How artificial intelligence and audience breadth drive advertising performance. Computers in Human Behavior forthcoming
- (2025) Content Strategies to Improve the Performance of Audio Streaming Services: Focusing on Content Genre and Update Features. Sage Open 15(1):
- (2025) Personal data strategies in digital advertising: Can first-party data outshine third-party data?. International Journal of Information Management 80 0268-4012
- (2024) How does OTT social viewing relieve pandemic-related depressive symptoms? Investigating the moderated mediation model of social connectedness and network types. The Communication Review 10714421