| 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 | EST226_G21V |
| 授業形態 Class Type | 講義 Regular course |
| 授業形式 Class Format | Live Virtual |
| 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 | 2021 GSM ONLINE Fall |
授業の概要 Course Overview
Misson Statementとの関係性 / Connection to our Mission Statement
Analytics and big data are the new frontier, circa 2021. They will be for a long time. AI, machine learning---all these terms will be de-mystified in this course.
授業の目的（意義） / Importance of this course
What is the analytics and big data frenzy all about? Each time 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. We are awed and blinded by the science.
In this course students will learn about the foundations of data and analytics. We dive beneath the 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.
The class will get a brief refresher on the a few statistical concepts, and focus on two predictive tools, recommender systems and regression, the foundations for many big data applications. No worries, non-numeric people! We will simplify and get into the practical understanding. No, you will not be a data scientist from this course (do you really want to?), but you should be able to intelligently talk to, and even manage, data scientists.
The class will then appreciate how analytics and big data fit into business models. Where do these fit on platforms and two-sided models like AirBnb and Uber? More generally, how does digitization enable business models?
Finally, the class will study the limits to analytics and big data: of fake news, of information overload and bias, and information fatigue. (We will take recent incident like Gamestop and dissect what happened.)
In this course students will learn about the foundations of data and analytics. We dive beneath the 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.
The class will get a brief refresher on the a few statistical concepts, and focus on two predictive tools, recommender systems and regression, the foundations for many big data applications. No worries, non-numeric people! We will simplify and get into the practical understanding. No, you will not be a data scientist from this course (do you really want to?), but you should be able to intelligently talk to, and even manage, data scientists.
The class will then appreciate how analytics and big data fit into business models. Where do these fit on platforms and two-sided models like AirBnb and Uber? More generally, how does digitization enable business models?
Finally, the class will study the limits to analytics and big data: of fake news, of information overload and bias, and information fatigue. (We will take recent incident like Gamestop and dissect what happened.)
到達目標 / Achievement Goal
N/A
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)
LG7 Global Perspective (GLP)
LG3 Ethical Decision Making
LG4 Effective Communication
LG5 Executive Leadership (EMBA)
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, you will be able to:
1. Get beneath the jargon of Digital and Big data, and understand terms such as predictive vs prescriptive stats, AI, machine learning.
2. Understand the mechanics and interpretation of regression and recommender systems.
3. Link analytics and big data applications into business models.
4. Understand the limits and pitfalls of information.
1. Get beneath the jargon of Digital and Big data, and understand terms such as predictive vs prescriptive stats, AI, machine learning.
2. Understand the mechanics and interpretation of regression and recommender systems.
3. Link analytics and big data applications into business models.
4. Understand the limits and pitfalls of information.
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
The course is about class discussions. I will grade your participation in both class discussions and possibly learning team breakout rooms. How actively do you participate by sharing your ideas with others, collaborating with teams, and contributing insights to the group? The most rigorous students will read all assigned texts. Other students should at least browse alternative readings on the web (provided in each section) as background reading.
授業スケジュール Course Schedule
第1日（Day1）
What is...?In class Exercise: Data Analytics Simulation: Strategic Decision Making 7050-HTM-ENG
●使用するケース
In class Exercise: Data Analytics Simulation: Strategic Decision Making 7050-HTM-ENGRead: Business Analytics at a Glance BEP 498
第2日（Day2）
Stats refresher on Correlation and regression and Recommender Systems●使用するケース
Read: Collaborative Filtering (RA Lim reading)第3日（Day3）
Prepare: Telenor: Revolutionizing Retail Banking in Serbia IN1328●使用するケース
Case: Telenor: Revolutionizing Retail Banking in Serbia IN1328第4日（Day4）
Analyze and discuss the Gamestop – Robinhood incidentRead: https://firstmonday.org/ojs/index.php/fm/article/view/738/647
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 | 80 % |
| クラス貢献度合計 Class Contribution Total | 80 % |
| 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 | 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
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. I will assign a final term paper, 1000 words limit, to be submitted January 30, 2022. The topic will be assigned in class.教科書 Textbook
- N/A「N/A」N/A（N/A）
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
N/A
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