| 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 | NUC402_N23A |
| 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 | 2023 UG Nisshin Term1 |
授業の概要 Course Overview
Misson Statementとの関係性 / Connection to our Mission Statement
This course teaches various business analytics 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 business analytics 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 the ability to quantitatively analyze system performance using data, 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
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Managerial Perspectives (BBA)
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Managerial Perspectives (BBA)
受講後得られる具体的スキルや知識 Learning Outcomes
Learning Outcomes: Upon successful completion of this course, students will understand the key concepts of business analytics on the topics such as
1. Python programming
2. Classification
3. Clustering
4. Association analysis
5. Deep learning
6. AI (Artificial Intelligence)
1. Python programming
2. Classification
3. Clustering
4. Association analysis
5. Deep learning
6. AI (Artificial Intelligence)
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 Business AnalyticsIntroduction to AI tools and Python Programming
●使用するケース
In-class exercises on AI tools第2日（Day2）
Python programming●使用するケース
In-class exercises on Python programming第3日（Day3）
Classification●使用するケース
Original case based on the provided sample code(We use breast cancer dataset)
第4日（Day4）
Clustering●使用するケース
Original case based on the provided sample code(We use wholesale customers dataset)
第5日（Day5）
Association analysis●使用するケース
Original case based on the provided sample code(We use online sales dataset)
第6日（Day6）
Introduction to Deep learning●使用するケース
Original case based on the provided sample code第7日（Day7）
AI (Artificial Intelligence)●使用するケース
AI (Artificial Intelligence) and Human Resources Analytics: GROW: Using Artificial Intelligence to Screen Human Intelligence (418020-PDF-ENG)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。
Preparation report (10% weight of the final grade): Submission of preparation reports to Google Classroom is required.
Mini in-class test (30% weight of the final grade): Students should take two non-cumulative in-class tests to confirm their understanding of the 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 | 60 % |
| クラス貢献度合計 Class Contribution Total | 60 % |
| 予習レポート Preparation Report | 10 % |
| 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
In-class discussion and excercises (60% weight of the final grade): Active participation in class discussions is required. Small excercises are given during class to confirm students' understanding of materials.Preparation report (10% weight of the final grade): Submission of preparation reports to Google Classroom is required.
Mini in-class test (30% weight of the final grade): Students should take two non-cumulative in-class tests to confirm their understanding of the concepts taught in class.
Late submission of tests/reports/excercises is not accepted or accepted with penalty.
教科書 Textbook
- 「not used」 （）
参考文献・資料 Additional Readings and Resource
No textbook will be used for this course. Additional reading materials will be provided through Google Classroom. Students are also encouraged to utilize the resources at the NUCB library.
授業調査に対するコメント Comment on Course Evaluation
Students found topics and cases interesting. Some students had hard time following the materials due to the number of topics covered. I cut down some materials and will slow down the pace to make sure everybody can follow the materials.
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
- (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
- (2021) Evaluating the Dynamic Impact of Theater Performances and Sports Events on Parking Demand in Downtown Pittsburgh. Smart Cities 4(4): 2624-6511
- (2021) Asymptotic Analysis for Systems with Deferred Abandonment. Mathematics 9(18): 2227-7390