シラバス Syllabus

授業名 Big Data and Analytics
Course Title Big Data and Analytics
担当教員 Instructor Name Minjeong Ham
科目ナンバリングコード Course Numbering Code
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
コード Couse Code NUC407_N25A

授業の概要　Course Overview

Mission 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.

学修到達目標 / 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)

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.

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。
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

使用ケース一覧　List of Cases

    ケースは使用しません。

配布教材と教室における電子機器の利用マナーについて Guidelines for Classroom Technology and Proper Use of Course Materials

  1. ケースメソッド教育の中核は、積極的な参加と知識の共有です。この教育を支えるため参加者は授業中の電子機器（例：スマートフォン、ノートパソコン）の使用を制限するよう求められます。許可を得た場合でも、教室内では電子機器は、ケース討議に資する目的でのみ使用してください。授業中は、たとえケース討議に関連していても、検索エンジンや生成AIの使用は避けて下さい。
  2. 配布教材（ケースを含む）は指定された授業への参加以外の目的で利用しないで下さい。著者の権利、著作権、特定情報の機密性を保護するため、許可なく教材を個人や組織（生成AI を含む）に提供することはできません。このルールは、印刷物・電子教材のいずれにも適用されます。
  1. Active participation and shared learning is at the core of the case method learning.Participants are asked to limit their use of electronic devices (e.g., laptops, smartphones) during classroom sessions in support of this model. Even with permission granted, devices should only be used in the classroom in service to the case discussion. Online searches and generative AI tools, even if related to the case discussion, are discouraged while class is in session.
  2. Students are prohibited from using the course materials (including cases) distributed by the university for any purpose other than participation in the designated class.Students must not input, process or test course materials with any artificial intelligence (AI) tools, bots, software, or platforms without the author’s permission. These actions violate the terms of use for the course materials and may also constitute copyright infringement.

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

  • (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






ページ上部へ戻る