| 授業名 | Competing on Business Analytics and Big Data |
|---|---|
| Course Title | Competing on Business Analytics and Big Data |
| 担当教員 Instructor Name | Ricardo Lim |
| コード Couse Code | EST101_G24N |
| 授業形態 Class Type | 講義 Regular course |
| 授業形式 Class Format | On Campus |
| 単位 Credits | 1 |
| 言語 Language | EN |
| 科目区分 Course Category | 入門科目0系 / Pre |
| 学位 Degree | Exed |
| 開講情報 Terms / Location | 2024 GSM Nagoya Spring |
授業の概要 Course Overview
Misson Statementとの関係性 / Connection to our Mission Statement
The 21st century frontier of management includes data analysis: big, vast, complex. Without analytical tools, managers have only crude, blunt "20th century" decision making capabilities. Analytics allows sharp iinsighting and action about business opportunities.
授業の目的（意義） / Importance of this course
What are the analytics and big data frenzies all about? When we watch Netflix or a Tiktok, or merely walk around the mall with our smartphone (location: on), we are being big-data-dissected and “predicted,” i.e., led to what we need to buy or watch next, whether we like it or not. The business press has added to the frenzy with jargon like “Digitization,” “AI,” “algorithms,” etc.
到達目標 / Achievement Goal
In this course students will learn about the foundations of data and analytics. We dive beneath jargon into the how-tos and applications of analytics. We do practical in-class exercises on data analytics. We should no longer feel the intimidation.
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
LG4 Effective Communication
LG7 Global Perspective (GLP)
LG4 Effective Communication
LG7 Global Perspective (GLP)
受講後得られる具体的スキルや知識 Learning Outcomes
Students will get a refresher on the a few statistical concepts, and focus on predictive tools such as regression and recommender systems, the foundations for many big data applications.
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
Non-numeric people should not worry: we will simplify and get practical understanding. You will not be a data scientist from this course, but you should be able to intelligently talk to, and even manage, data scientists.
授業スケジュール Course Schedule
第1日（Day1）
1. Introduction: What is analytics? What is big data? Learning: Introduction and framework of course. What are analytics and big data? Why do we do it?Read/watch any one of these useful websites on business analytics (Watch out for sites that are disguised marketing tools for programs and software!)
1. https://www.techtarget.com/searchbusinessanalytics/definition/business-analytics-BA
2. https://online.hbs.edu/blog/post/importance-of-business-analytics
3. https://www.talend.com/resources/business-analytics-vs-data-analytics/
4. https://www.investopedia.com/terms/b/big-data.asp
'
earning: to understand the basic math and foundation of analytics tools. Students should learn just enough analytics principles to understand big picture analysis goals and to pose insighting questions to data scientists and analysts
Instructions:
1. We will spend this day on basic and advanced statistical tools for analytics. Do not worry if your statistics are rusty. We will do the material together with exercises and demonstrations
2. Please be ready with Excel. Make sure you have enable the statistical tool pack. See https://www.youtube.com/watch?v=1R_aJ_Fli2w for Macs and https://www.youtube.com/watch?v=V60-IFnih3Q for Windows
Read case and notes from Prof. Ricky Lim:
1. Customer Lifetime Value
2. Correlation and Regression
3. Recommender systems (Collaborative Filtering)
4. Logistic Regression
●使用するケース
(From Prof. Ricky Lim)1. Customer Lifetime Value
2. Correlation and Regression
3. Recommender systems (Collaborative Filtering)
4. Logistic Regression
第2日（Day2）
1. Read: Why the Lean Start-up Changes Everything R1305C-PDF-ENGRead https://steveblank.com/2010/11/15/creating-startup-success-customer-development-business-model-design/
There is a 102-slide embedded slideshare deck. It is a good, actually, easy-to-view PPT on the business model canvas
See also: http://theleanstartup.com/ and https://steveblank.com/slides/
2, Read: Lemonade: Delighting Insurance Customers with AI and Behavioural Economics – IN1673
●使用するケース
Why the Lean Start-up Changes Everything R1305C-PDF-ENGLemonade: Delighting Insurance Customers with AI and Behavioural Economics – IN1673
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
I will reward in-class contributions during case discussions. These include starting the case, summarizing others’ viewpoints, creating debate with others, concluding. I like good questions and how you clarify difficult concepts. I like active group participation, but participation must be positive, building, and collaborative—not attacking or criticizing.教科書 Textbook
- 配布資料
参考文献・資料 Additional Readings and Resource
N/A
授業調査に対するコメント Comment on Course Evaluation
Course evaluations were positive, with ratings > 4,5
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