| 授業名 | Introduction to Business Analysis |
|---|---|
| Course Title | Introduction to Business Analysis |
| 担当教員 Instructor Name | Minjeong Ham |
| コード Couse Code | NUC419_N25A |
| 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 | 教養教育科目 / Liberal Arts |
| 学位 Degree | BBA |
| 開講情報 Terms / Location | 2025 UG Nisshin Term1 |
授業の概要 Course Overview
Misson Statementとの関係性 / Connection to our Mission Statement
The mission of the Introduction to Business Analytics course at Nagoya University of Commerce & Business (NUCB) is to empower students with the knowledge, skills, and mindset needed to leverage data for strategic decision-making. Through case-based discussions, stakeholder role-playing, and hands-on applications, students will develop a deep understanding of how businesses use data analytics to drive competitive advantage, improve operational efficiency, and enhance customer engagement. This course aligns with NUCB’s "Frontier Spirit" by encouraging critical thinking, problem-solving, and data-driven decision-making, preparing students to become future business leaders in an increasingly digital economy.
授業の目的（意義） / Importance of this course
The business landscape is rapidly evolving, making data analytics an essential skill for future managers and leaders. This course bridges theory with real-world applications through case studies, equipping students with the ability to interpret data, extract insights, and make informed business decisions. As AI, big data, and machine learning reshape industries, professionals must navigate ethical considerations and data privacy. Companies actively seek talent with analytical expertise, and this course enhances employability by developing high-demand skills. Additionally, students gain a global perspective by analyzing data-driven strategies across industries, preparing them for careers in international business and consulting.
到達目標 / Achievement Goal
By the end of the course, students will be able to:
- Understand the role of business analytics in decision-making.
- Use data-driven insights to improve business strategies.
- Learn marketing, customer segmentation, and pricing strategies using analytics.
- Interpret social media and digital marketing analytics.
- Discuss the impact of AI and big data on business.
- Understand the role of business analytics in decision-making.
- Use data-driven insights to improve business strategies.
- Learn marketing, customer segmentation, and pricing strategies using analytics.
- Interpret social media and digital marketing analytics.
- Discuss the impact of AI and big data on business.
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
LG5 Business Perspectives (BSc)
LG6 Managerial Perspectives (BBA)
LG7 International Perspectives (BA)
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG5 Business Perspectives (BSc)
LG6 Managerial Perspectives (BBA)
LG7 International Perspectives (BA)
受講後得られる具体的スキルや知識 Learning Outcomes
By the end of this course, students will be able to understand the role of business analytics in decision-making, apply analytical techniques to solve business problems, and interpret data to generate actionable insights. Through case studies and stakeholder discussions, students will enhance their critical thinking, problem-solving, and decision-making skills. Additionally, they will develop a global perspective on business analytics, preparing them for careers in international business, consulting, and data-driven industries.
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 | 20 % | |
| 参加者中心型 Participant-Centered Learning | ケースメソッド Case Method | 80 % |
| フィールドメソッド Field Method | 0 % | 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
授業スケジュール Course Schedule
第1日（Day1）
Introduction to Business Analytics & Data-Driven Decision Making●使用するケース
Giving Data a Voice: The Rise of TalkingData第2日（Day2）
Market Segmentation & Customer Insights●使用するケース
Wahoo Fitness: Segmentation and Data Insights第3日（Day3）
Predictive Analytics & Customer Relationship Management (CRM)●使用するケース
Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare第4日（Day4）
Pricing Strategies & Markdown Optimization●使用するケース
Markdown Optimization for an Indian Apparel Retailer第5日（Day5）
Social Media Analytics & Customer Engagement●使用するケース
Xoxoday.com: Customer Engagement through Social Media第6日（Day6）
Digital Marketing & Direct-to-Consumer (DTC) Business Models●使用するケース
Hubble Contact Lenses: Data-Driven Direct-to-Consumer Marketing第7日（Day7）
AI and Business Analytics Applications in Industry●使用するケース
Relevance of Healthcare Analytics in Singapore during COVID19 and beyond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。
*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 | 20 % |
| 授業内での挙手発言 Class Contribution | 60 % |
| クラス貢献度合計 Class Contribution Total | 80 % |
| 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 | 20 % |
| 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
教科書 Textbook
- 「 」 （）
参考文献・資料 Additional Readings and Resource
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
- (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
- (2023) Personalization, Privacy and Algorithms in Online Advertising. Yonsei University
- (2021) The effects of internet proliferation on search engine and over-the-top service markets. Telecommunications Policy 45(8): 03085961
- (2021) Empirical study on video clip consumption: focusing on viewing habits and use motives. International Journal of Mobile Communications 19(2): 1741-5217