| 授業名 | Data Science & AI for Leaders |
|---|---|
| Course Title | Data Science & AI for Leaders |
| 担当教員 Instructor Name | Ricardo Lim |
| 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 | 応用科目200系 / Applied |
| 学位 Degree | MSc in Management / Business Analytics & AI |
| 開講情報 Terms / Location | 2026 GSM Nagoya Spring |
| コード Course Code | GLP127_G26N |
授業の概要 Course Overview
Mission Statementとの関係性 / Connection to our Mission Statement
Being at the frontier means understanding and managing fast global change, intense competition, and quickly changing technology. NUCB managers therefore need resilience to handle faster cycles of problem-solving, data collection, insight generation, and decision-making. One way to build that resilience is to use data science and AI more effectively.
授業の目的（意義） / Importance of this course
Before GenAI, most MBA programs focused on teaching knowledge across fields like marketing, operations, and finance. That is no longer enough. AI can now gather and organize knowledge faster than people. As a result, knowledge alone is no longer the main advantage of an MBA. What matters now is knowing how to use AI and data science well—and combining them with sound judgment and empathy.
学修到達目標 / Achievement Goal
By the end of the course, students will be able to use AI as a creative partner, data analyst, and research assistant. More importantly, they will learn to define business problems clearly and translate business needs into data science and AI terms. This will help them work effectively with specialists such as data analysts and data scientists to achieve business goals. They will also learn to question AI outputs critically and manage its limits and risks.
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 Innovative Leadership (MBA)
LG7 Global Perspective (GLP)
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Innovative Leadership (MBA)
LG7 Global Perspective (GLP)
受講後得られる具体的スキルや知識 Learning Outcomes
By the end of the course, participants will be able to:
1. Build a personal toolkit of GenAI prompts and workflow notebooks for managerial work.
2. Know when and how to use core statistics, including hypothesis testing, regression, classification, and clustering, and interpret the results.
3. Explain how statistics support prediction and decision-making in AI-enabled systems.
4. Demonstrate a conceptual understanding of the core mechanisms of data science sufficient to communicate business requirements to technical experts.
5. Assess how AI can enhance personal productivity, redesign business processes, and inform business strategy.
6. Develop skills to judge AI quality and know when humans develop overdependence, and when humans should intervene.
1. Build a personal toolkit of GenAI prompts and workflow notebooks for managerial work.
2. Know when and how to use core statistics, including hypothesis testing, regression, classification, and clustering, and interpret the results.
3. Explain how statistics support prediction and decision-making in AI-enabled systems.
4. Demonstrate a conceptual understanding of the core mechanisms of data science sufficient to communicate business requirements to technical experts.
5. Assess how AI can enhance personal productivity, redesign business processes, and inform business strategy.
6. Develop skills to judge AI quality and know when humans develop overdependence, and when humans should intervene.
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 | 0 % | |
| 参加者中心型 Participant-Centered Learning | ケースメソッド Case Method | 100 % |
| 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
Pre-course preparation
AI (GenAI / LLM) use policy:
1. This course uses AI—GenAI and LLMs--often, but carefully. Use it critically while building judgment, ethical reasoning, empathy, and active listening.
2. You may use AI during designated class exercises such as brainstorming, data analysis, research support, editing, and presentation development.
3. Turn off AI during general case discussions.
General learning approach:
1. The course has no exams. Instead, you will complete a group project within a week after the course ends. You will also submit a peer evaluation naming the two strongest contributors on your team, excluding yourself. Each of those students will receive a 5% grade bonus.
2. I do not need submissions of case analysis, reports, etc during the course.
3. The course has 14 sessions. Some combine discussion with hands-on exercises.
4. Do not submit cases for Sessions 9, 11, 13, and 14 to AI. CCJ and HBS prohibit this. Read the four cases in your PDF packet before class.
5. Sessions 1–8, 10, and 12 are not Harvard or CCJ, so you you can feed them to AI.
5.a All the exercises here can be done with free versions of AI. But take care to not burn all your tokens too fast.
5.b Suggest you use NotebookLM to organize your work, which is part of your NUCB Google account. You should mix in ChatGPT, Claude, Copilot, Gamma, Suno, and others to augment your notebooks,
5.c Google says NotebookLM data stays private and is not used for public training. You can also use Slack or Claude Projects in lieu of NotebookLM.
5.d You may upload public documents and links, as well as your own notes, photos, and data, to NotebookLM.
5.e Keep notebooks small and simple. Use separate notebooks for each task or subproject. Tell NotebookLM not to generate slides or infographics unless needed.
AI (GenAI / LLM) use policy:
1. This course uses AI—GenAI and LLMs--often, but carefully. Use it critically while building judgment, ethical reasoning, empathy, and active listening.
2. You may use AI during designated class exercises such as brainstorming, data analysis, research support, editing, and presentation development.
3. Turn off AI during general case discussions.
General learning approach:
1. The course has no exams. Instead, you will complete a group project within a week after the course ends. You will also submit a peer evaluation naming the two strongest contributors on your team, excluding yourself. Each of those students will receive a 5% grade bonus.
2. I do not need submissions of case analysis, reports, etc during the course.
3. The course has 14 sessions. Some combine discussion with hands-on exercises.
4. Do not submit cases for Sessions 9, 11, 13, and 14 to AI. CCJ and HBS prohibit this. Read the four cases in your PDF packet before class.
5. Sessions 1–8, 10, and 12 are not Harvard or CCJ, so you you can feed them to AI.
5.a All the exercises here can be done with free versions of AI. But take care to not burn all your tokens too fast.
5.b Suggest you use NotebookLM to organize your work, which is part of your NUCB Google account. You should mix in ChatGPT, Claude, Copilot, Gamma, Suno, and others to augment your notebooks,
5.c Google says NotebookLM data stays private and is not used for public training. You can also use Slack or Claude Projects in lieu of NotebookLM.
5.d You may upload public documents and links, as well as your own notes, photos, and data, to NotebookLM.
5.e Keep notebooks small and simple. Use separate notebooks for each task or subproject. Tell NotebookLM not to generate slides or infographics unless needed.
授業スケジュール Course Schedule
第1日（Day1）
Day 1S01 - Understanding data science v analytics v AI
S02-4 - AI as creative partner
S05 – AI as Data Analyst: crosstabs, correlation, ANOVA
●使用するケース
S01 case: noneS02-S04 AI as creative partner (reading not in packet. Look in Google Classroom)
S05 case: AI as Data Analyst: crosstabs, correlation, ANOVA (reading not in packet. Look in Google Classroom)
第2日（Day2）
Day 2S06-7 - AI as Research Assistant
S08 – AI as Data Analyst: regression
S09 - AI as judge
S10 - AI as Data Analyst: LOGITS
●使用するケース
Day 3S06-7 - AI as Research Assistant (reading not in packet. Look in Google Classroom)
S08 - AI as Data Analyst: regression (reading not in packet. Look in Google Classroom)
S09 - GROW: Using AI to Screen Human Intelligence 9-418-020
S10 AI as Data Analyst: LOGITS (reading not in packet. Look in Google Classroom)
第3日（Day3）
Day 3 (Subject to change as of May 29, 2026. Please check soon for updates)S11 - GenAI on “steroids”: Agentic AI
S12 – AI as Data Analyst: Cluster, recommender
S13 - AI ethics and critical thinking
S14 - AI and strategy: implementing and scaling corporate GenAI
●使用するケース
S11 - Walmart’s SPARKY: Agentic AI and the Future of Shopping KE1436 S11-12 GenAI as data analyst, continued.S12 - AI as Data Analyst: recommender systems and cluster analysis (reading not in packet. Look in Google Classroom)
S13 - The Clueless: Navigating an Ethical AI Marketing Dilemma W39696
S14 - Moderna 9-625-070
第4日（Day4）
N/A●使用するケース
N/A第5日（Day5）
N/A●使用するケース
N/A第6日（Day6）
N/A●使用するケース
N/A第7日（Day7）
N/A●使用するケース
N/A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 | 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
教科書 Textbook
- 配布資料
参考文献・資料 Additional Readings and Resource
To be announced.
授業調査に対するコメント Comment on Course Evaluation
For past course evaluations, please see various course evaluations.
https://docs.google.com/spreadsheets/d/1bmWo77TeHNgayBaU3TlOprS-9mK58-mL/edit?usp=sharing&ouid=103967654037341470318&rtpof=true&sd=true
https://docs.google.com/spreadsheets/d/1bqH3sXynAEyf32ItAq97muKRVmTwdX3J/edit?usp=sharing&ouid=103967654037341470318&rtpof=true&sd=true
https://docs.google.com/spreadsheets/d/1uVN4AMzY7wE7E95Ucpe4J6aTF-YZ_j_-/edit?usp=sharing&ouid=103967654037341470318&rtpof=true&sd=true
https://docs.google.com/spreadsheets/d/1bmWo77TeHNgayBaU3TlOprS-9mK58-mL/edit?usp=sharing&ouid=103967654037341470318&rtpof=true&sd=true
https://docs.google.com/spreadsheets/d/1bqH3sXynAEyf32ItAq97muKRVmTwdX3J/edit?usp=sharing&ouid=103967654037341470318&rtpof=true&sd=true
https://docs.google.com/spreadsheets/d/1uVN4AMzY7wE7E95Ucpe4J6aTF-YZ_j_-/edit?usp=sharing&ouid=103967654037341470318&rtpof=true&sd=true
担当教員のプロフィール About the Instructor
Ricardo A. Lim, Ph.D. is a professor at the NUCB Business School and visiting professor at Ritsumeikan APU, Beppu, Japan. He was a former Dean of AIM, former President of the Association of Asia Pacific Business Schools (a consortium of 80 Asian B-schools), founding member of the Global Network to Advance Management at Yale Business School, and Asia-Pacirfic Advisory Council of AACSB. He teaches information systems, statistics, analytics, and design thinking x lean x agile concepts. He has published in the MIS Quarterly and the Journal of Management Information Systems, and serves as Associate Editor for the International Journal of Business and Economics, Taiwan. He currently consults for education and financial services sectors. Before joining academe he was a senior consultant for the Computer Sciences Corporation in Boston and Siemens Computing in Manila. He has a Ph.D. from the U. of Southern California, an MBA from the U. of Virginia, and a B.Com. from McGill University.
Refereed Articles
- (2023) Determinants of Conspicuous Consumption in Smartphones. Asia Pacific Journal of Information Systems 33 (3): 2288-5404
- (2023) A Study of Satisfaction and Loyalty for Continuance Intention of Mobile Wallet in India. International Journal of E-Adoption (IJEA) 15 (1): 1937-9633
- (2021) Developing and Testing a Smartphone Dependency Scale Assessing Addiction Risk. International Journal of Risk and Contingency Management 10 (4): 2160-9624
- (2021) Business Model Innovation: A Study of Empowering Leadership. Creativity and Innovation Management 1467-8691
- (2021) The Effect of Reciprocity on Mobile Wallet Intention: A Study of Filipino Consumers. International Journal of Asian Business and Information Management 12 (2): 1947-9638