Aligning with the NUCB's mission statement, this course aims to nurture innovative, ethical leaders who possess a ‘Frontier Spirit’ in conducting data science in response to the contemporary challenges faced by organizations in the New Asian and global context.

Data science has become the new language of business. Many roles across the enterprise in finance, marketing, human resources, operations, innovation, and strategy now rely heavily on data science for critical decision-making input and implementation. Given the increasing ubiquity and need for analytics across organizations, it is imperative that master students learn key concepts, understand the opportunities and limitations of analytics, and develop a solid grasp of the tools that are currently being deployed.

The Data Science for Managers (DSM) course aims to provide the general manager with the necessary tools to develop a data science organization, evaluate data-driven insights, and productively collaborate with data analysts, engineers, and scientists. This course will help you appreciate the full benefits of data-driven decision making and teach you the business analytics tools and techniques to effectively understand, visualize, and analyze the data available to you.

Students completing the DSM course are expected to be able to combine their proficiency in data analytics with their managerial abilities to identify business opportunities, frame problems, shape solutions, and lead change in organizations.

By taking part in this course, students will deepen their understanding of the following areas in Data Science for Managers:
Data Science, Qualitative Data Collection, Quantitative Data Collection, Hypothesis Testing and Modelling, Data Visualization and Storytelling, and Ethics in Data Science

*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。

Upon completing this course, students are able to:
1. Develop a foundational understanding of data science tools, processes, and models.
2. Use business analytics and data science to build a framework that will support data-driven decisions leading to organizational success.
3. Design suitable methodologies and techniques in collecting, processing and analyzing qualitative and quantitative data for management purposes.
4. Critically evaluate key ethical issues in data science and their potential impacts to the organizations.

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 %

"Pre-class Preparation"
Students are expected to study each case and prepare their own answers to the questions in the assignment files. Participants should allow at least 3 hours of preparation time per case.
A laptop computer is required for electronic distribution of handouts on the day of the class.

"Class Discussion"
The class is based on open discussions and students are facilitated to exchange their opinions with their classmates. During the open discussions, students are expected to think flexibly and adjust their opinions. No specific comment may be given to each student's opinion, but students will know if their opinions are appreciated by listening to their classmates' feedback.

"Preparation report"
Cases used: (1) Agoda: People Analytics and Business Culture (A), (2) All Nutrition (A): Focus Group Research for Market Segmentation , (3) All Nutrition (B): Quantitative Research for Market Segmentation (CU277-PDF-ENG, Columbia Business School), and (4) Innovation at Uber: The Launch of Express POOL.
Assignment outline: Please identify and discuss the common factor(s) of successful data-driven decision making found across four cases.
Due date: Before the class starts on the first day of class.
Submission method: Please submit to Google Classroom.
Feedback: The common factor(s) will be discussed in class.

"Final Examination"
Case : will be provided at the end of Day 3
Examination questions: will be distributed in class on Day 4
Duration : 3 hours
Submission method : submit to Google Classroom
Feedback: Marked assessment rubrics will be returned to participants 

*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	20 %
授業内での挙手発言 Class Contribution	50 %
クラス貢献度合計 Class Contribution Total	70 %
予習レポート Preparation Report	10 %
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	0 %
期末試験 Final Exam	20 %
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

• 配布資料

There is no set textbook for this course, but students can refer to an open-source textbook by the following weblink:

Social Science Research: Principles, Methods, and Practices
https://open.umn.edu/opentextbooks/textbooks/social-science-research-principles-methods-and-practices

Introductory Statistics
https://open.umn.edu/opentextbooks/textbooks/introductory-statistics

Introduction to Data Science Using Python
https://open.umn.edu/opentextbooks/textbooks/introduction-to-data-science-using-python

Additional readings and videos will be distributed and shown in class.

This is a new course.

Refereed Articles

• (2025) Personal data strategies in digital advertising: Can first-party data outshine third-party data?. International Journal of Information Management 80 0268-4012
• (2024) How does OTT social viewing relieve pandemic-related depressive symptoms? Investigating the moderated mediation model of social connectedness and network types. The Communication Review 10714421
• (2023) Personalization, Privacy and Algorithms in Online Advertising. Yonsei University
• (2021) The effects of internet proliferation on search engine and over-the-top service markets. Telecommunications Policy 45(8): 03085961
• (2021) Empirical study on video clip consumption: focusing on viewing habits and use motives. International Journal of Mobile Communications 19(2): 1741-5217