| 授業名 | Big Data and Analytics |
|---|---|
| Course Title | Big Data and Analytics |
| 担当教員 Instructor Name | Minjeong Ham |
| コード Couse Code | NUC407_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 | 専門教育科目 / Specialized Subject |
| 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
This course aims to equip students with a fundamental understanding of big data analytics, focusing on its applications across industries such as finance, marketing, retail, entertainment, and healthcare. By engaging with real-world case studies, students will develop critical thinking, analytical skills, and decision-making abilities necessary to leverage big data for strategic business insights.
授業の目的（意義） / Importance of this course
With the rapid expansion of digital technologies, businesses generate and process vast amounts of data daily. Organizations that effectively utilize big data analytics gain a significant competitive advantage by improving decision-making, optimizing operations, and predicting consumer behavior. This course provides students with the knowledge and practical exposure to data-driven decision-making, enabling them to adapt to the evolving business landscape and become valuable assets in data-centric roles.
到達目標 / Achievement Goal
By the end of this course, students will be able to analyze, interpret, and apply big data techniques in business contexts. They will learn to assess case studies critically, formulate data-driven strategies, and understand the ethical and managerial implications of big data applications in different industries.
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
Upon successful completion of this course, students will be able to:
- Understand the fundamental concepts and principles of big data analytics.
- Apply big data techniques in financial modeling, marketing, retail, entertainment, and healthcare decision-making.
- Analyze case studies to evaluate the impact of data-driven strategies on business performance.
- Develop data-driven business recommendations based on industry-specific challenges.
- Assess ethical considerations and privacy concerns related to big data applications.
- Communicate data-driven insights effectively through written and verbal analysis.
- Understand the fundamental concepts and principles of big data analytics.
- Apply big data techniques in financial modeling, marketing, retail, entertainment, and healthcare decision-making.
- Analyze case studies to evaluate the impact of data-driven strategies on business performance.
- Develop data-driven business recommendations based on industry-specific challenges.
- Assess ethical considerations and privacy concerns related to big data applications.
- Communicate data-driven insights effectively through written and verbal analysis.
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 Big Data & Fundamentals of Analytics●使用するケース
The Weather Company – Creating Consumer Apps That Leverage Big Data第2日（Day2）
Social Network Analysis & Fraud Detection●使用するケース
RBC – Social Network Analysis第3日（Day3）
Credit Risk & Financial Analytics●使用するケース
Credit Risk Modeling Using Nontraditional Data – The Experience of Ping An OneConnect Bank第4日（Day4）
Location-Based Analytics & Consumer Behavior●使用するケース
Movvo – Marketing Location-Based Data第5日（Day5）
Retail & Marketing Analytics●使用するケース
- MarcPoint – Strategizing With Big Data- Predicting Consumer Tastes with Big Data at Gap
第6日（Day6）
Big Data in the Entertainment Industry●使用するケース
Trinity Earth – Big Data Creating Value for China’s Film and Television Industry第7日（Day7）
Big Data in Healthcare & Future Trends●使用するケース
LinkDoc – Commercial Exploration of Healthcare Big Data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 | 10 % |
| 授業内での挙手発言 Class Contribution | 60 % |
| クラス貢献度合計 Class Contribution Total | 70 % |
| 予習レポート Preparation Report | 10 % |
| 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