New Age, New Frontier of Asia should know about Big Data and Analytics. However one must get past the initial awe and intimidation, and learn about the basic foundation of data and its glorious possibilities, and also severe limitations. For the non-data scientific manager, this course help unravel the mysteries of big data and analytics into their fundamental functions.

This is a full two-day course. We will try to demystify Data analytics: 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 participants will learn about

1. 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.

2. Basic 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. 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.

3. 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?

*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。

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.

教育手法 Teaching Method		% of Course Time
インプット型 Traditional		25 %
参加者中心型 Participant-Centered Learning	ケースメソッド Case Method	50 %
	フィールドメソッド Field Method	25 %
合計 Total		100 %

There is a portion of this course where we refresh students’ memories of math and statistics. Basic, not expert skills are needed for this portion. Then the course migrates into class discussions. I will grade 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.

*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。

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. I will assign a final term paper, 1000 words limit, to be submitted one week after completion of the course. The topic will be assigned in class.

• 配布資料

N/A

I executed this course in 2021. It got overall ratings of 4.58, with a high of 4.8 Here were some student comments:

“It is useful for students not so firm in statistics and data analysis.”
“The course materials were very clear and well organized, and the explanation of complex topics made it very understandable. The teacher was really encouraging us to speak out our ideas and built up on them which made the conversation very dynamic.”
“I really enjoyed the practical exercises with data and regression.”
“Sharing of clear explanations, clarifications of concept, real life examples, references for future interest”

There were some negative comments as well”

“The pace was too slow for me because I was already very familiar with most of the content.”

To answer the negative point, experience statisticians/analytics students may find the course too rudimentary. We designed the course with the non-data scientific manager in mind. This course is not necessarily for beginners, but for people who are intimidated about the details of analytics.

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