| 授業名 | Business Analytics |
|---|---|
| Course Title | Business Analytics |
| 担当教員 Instructor Name | 笹沼 克信（Katsunobu Sasanuma） |
| コード Couse Code | CLD209_G24N |
| 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 | 発展科目300系 / Advanced & Specialized |
| 学位 Degree | Exed |
| 開講情報 Terms / Location | 2024 GSM Nagoya Fall |
授業の概要 Course Overview
Misson Statementとの関係性 / Connection to our Mission Statement
This course teaches various analytic techniques, which will be a key tool to deal with uncertainty and big data. The course also aims to educate innovative leaders who can contribute to the creation of new business.
授業の目的（意義） / Importance of this course
The course emphasizes the understanding of the concepts of important analytical techniques, not the derivations of formulas. Through active learning teaching in this course, students will develop an ability to understand key concepts and working knowledge of various analytic techniques. Students also cooperate with their classmates and discuss their ideas in groups.
到達目標 / Achievement Goal
Students taking this course will acquire an ability to analyze performance of systems quantitatively, gain insights into performance characteristics, recognize various trade-offs in operational decisions, and apply concepts and analytical methods to firms/organizations to improve their operational performances.
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
LG3 Ethical Decision Making
LG4 Effective Communication
LG5 Executive Leadership (EMBA)
LG6 Innovative Leadership (MBA)
LG3 Ethical Decision Making
LG4 Effective Communication
LG5 Executive Leadership (EMBA)
LG6 Innovative Leadership (MBA)
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 understand the key concepts/tools of various analytic techniques on the topics such as
1. Python Programming
2. Machine Learning Algorithms such as Linear Regression, Decision Tree, Clustering, and PCA
1. Python Programming
2. Machine Learning Algorithms such as Linear Regression, Decision Tree, Clustering, and PCA
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 | 40 % | |
| 参加者中心型 Participant-Centered Learning | ケースメソッド Case Method | 60 % |
| 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
The goal of the course is to develop the ability to solve hard problems that may not have solutions. Do not memorize formulas. I do not want students to become a second-rate computer, which runs slow and can find answers only when formulas are available. When solving real-world problems, understanding the fundamental concept is much more important than memorizing formulas.
Cases and/or reading materials are provided before each class; students are expected to read them, submit assignments (if any), and prepare well for the discussions before coming to class. Students are expected to spend at least around three hours to prepare for each class.
Please bring a laptop with you; we will use Excel and Google Colab.
Feedback on students’ performances (e.g. in-class discussions, reports, etc.) will be provided during class and/or through Google Classroom.
Note: The case list is tentative and subject to change.
Cases and/or reading materials are provided before each class; students are expected to read them, submit assignments (if any), and prepare well for the discussions before coming to class. Students are expected to spend at least around three hours to prepare for each class.
Please bring a laptop with you; we will use Excel and Google Colab.
Feedback on students’ performances (e.g. in-class discussions, reports, etc.) will be provided during class and/or through Google Classroom.
Note: The case list is tentative and subject to change.
授業スケジュール Course Schedule
第1日（Day1）
Introduction to Python Programming第2日（Day2）
Introduction to Linear Regression●使用するケース
Force Energy: Growing the Brand (Ivey Case)第3日（Day3）
Introduction to Decision Tree●使用するケース
Nata Supermarkets: Customer Analytics (Ivey Case)第4日（Day4）
Introduction to Clustering●使用するケース
London Hydro Inc.: Evaluating Different Electricity Pricing Schemes (Ivey Case)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。
In-class discussion (40% weight for the final grade): Active participation in class discussion is required.
Mini in-class test (30% weight for the final grade): Students should take in-class mini-tests to confirm the understanding of basic concepts taught in class.
Late submission of tests/reports/excercises is not accepted or accepted with penalty.
*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 | 40 % |
| クラス貢献度合計 Class Contribution Total | 40 % |
| 予習レポート Preparation Report | 30 % |
| 小テスト Quizzes / Tests | 30 % |
| 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 | 0 % |
| 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
Case report (30% weight for the final grade): Submission of case report to Google Classroom is required.In-class discussion (40% weight for the final grade): Active participation in class discussion is required.
Mini in-class test (30% weight for the final grade): Students should take in-class mini-tests to confirm the understanding of basic concepts taught in class.
Late submission of tests/reports/excercises is not accepted or accepted with penalty.
教科書 Textbook
- 配布資料
参考文献・資料 Additional Readings and Resource
Please check references in a case assignment sheet; this sheet will be provided before the first class.
授業調査に対するコメント Comment on Course Evaluation
Material covered in class was too extensive last year. This year, I will focus on less material and try to cover them in detail.
担当教員のプロフィール About the Instructor
東京大学教養学部基礎科学科卒業後、東京大学大学院理学系研究科相関理化学修了（物性物理学専攻）。東芝研究開発センター（研究員）、アルメック（コンサルタント）を経てハーバード大学とマサチューセッツ工科大学において修士課程（公共政策及びオペレーションズリサーチ専攻）を修了した後、カーネギーメロン大学においてPh.D.取得（オペレーションズマネージメント専攻）。その後ニューヨーク州立大学ストーニーブルック校アシスタントプロフェッサーを経て、現職。現在、名古屋商科大学経営学部教授、及び東北大学大学院経済学研究科客員教授。専門は確率モデル、待ち行列理論、在庫管理、交通システム、データアナリティクス等。これまでオペレーションズリサーチ、オペレーションズマネージメント、ビジネスアナリティクス等の各授業を担当し、研究成果は国際学会及び国際ジャーナルに発表されている。
Katsunobu Sasanuma is a professor at NUCB, Nagoya University of Commerce and Business. He also holds a visiting professor position at the Graduate School of Economics and Management at Tohoku University. Prior to joining the faculty at NUCB, Dr. Sasanuma was an assistant professor at College of Business at Stony Brook University, State University of New York.
His research areas include stochastic modeling, queueing theory, inventory management, transportation systems, and data analytics. He possesses an interdisciplinary background with a decade of accumulated consulting, business, and engineering experience including an R&D work at Toshiba focusing on LEDs and laser diodes. He has taught classes in Operations Research/Operations Management/Business Analytics. His work has been presented at various conferences and has appeared in professional journals.
BA and MS, University of Tokyo (1990 and 1992, resp.)
MPA, Harvard University (2005)
MS in Operations Research and Technlogy&Policy, MIT (2009)
PhD in Public Policy and Management (Operations Management), Carnegie Mellon University (2015)
Refereed Articles
- (2024) Acquisition of Knowledge and Meta-Skills through the Case Method in Politics and Law Classrooms: New Empirical Insight from Japan. Journal of Political Science Education
- (2024) Evaluating the Effectiveness of Recommendation Engines on Customer Experience Across Product Categories. International Journal of Technology and Human Interaction (IJTHI) 1548-3908
- (2022) A marginal analysis framework to incorporate the externality effect of ordering perishables. Operations Research Perspectives (Elsevier) 9(100230): 2214-7160
- (2022) Controlling arrival and service rates to reduce sensitivity of queueing systems with customer abandonment. Results in Control and Optimization (Elsevier) 6(100089): 2666-7207
- (2022) An opaque selling scheme to reduce shortage and wastage in perishable inventory systems. Operations Research Perspectives (Elsevier) 9(100220): 2214-7160
Refereed Proceedings
- (2024). Analytical approach to solving continuous-time hidden Markov models. The 2024 Fall National Conference of Operations Research Society of Japan .Operations Research Society of Japan. 1. 2. Nanzan University