This course provides an accessible and practical introduction to Python programming for absolute beginners. Students will learn the fundamental concepts of coding, from basic syntax and data types to essential control structures and functions. The curriculum is built around hands-on exercises and straightforward projects, enabling students to gain practical problem-solving skills and a strong foundation for a future in technology.

Our mission is to empower business professionals with the analytical skills and mindset to thrive in a data-driven world. We aim to cultivate innovative and ethical leaders who can leverage Python and its powerful libraries to transform data into strategic insights. Through a curriculum focused on practical application, our students will develop a frontier spirit, using data to solve complex business problems and contribute to the growth of their organizations and the global business community.

Taking the Python Programming course is crucial for anyone looking to build a career in technology or data-driven fields. Python's versatility and clear syntax make it an ideal language for beginners and a powerful tool for professionals. By learning Python, students can automate tedious tasks, analyze complex datasets, and build everything from web applications to machine learning models. The skills acquired in a Python course are not only in high demand but also serve as a foundational building block for future learning in areas like data science, artificial intelligence, and software development.

*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 successful completion of this course, you will be able to:
● Understand the Data Analytics Lifecycle: From problem formulation to data communication.
● Master Foundational Python Skills: Write and execute Python code, work with data structures, and understand basic programming logic.
● Utilize Key Python Libraries: Become proficient with Pandas for data manipulation, NumPy for numerical operations, and Matplotlib & Seaborn for data visualization.
● Perform Exploratory Data Analysis (EDA): Summarize key data characteristics, identify patterns, and find hidden insights.
● Apply Statistical Analysis: Conduct hypothesis testing and regression analysis to understand relationships between variables.
● Communicate Insights Effectively: Create clear, compelling visualizations and presentations to share your findings with stakeholders.

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 %

● Please read a short article ( https://online.hbs.edu/blog/post/types-of-data-analysis ) for a quick reference on different types of Data Analysis
● Please allocate at least 1-2 hours of preparatory study for each case indicated in the schedule, all cases will be distributed 2 weeks before the class via Google Classroom or Google Drive.
● A laptop is required as the course materials will be distributed electronically on the day of the class.


Case to be used: TBA
Assignment:：Write a Written Analysis of Case (WAC) based on the case provided, guide questions will be provided.
Submission Deadline: At the end of the last class
Submission Method: Submit via Google Classroom or email (within min. of 5 pages, 11 font size, Arial, A4 page)

*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	30 %
授業内での挙手発言 Class Contribution	50 %
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

Class Contribution (80%) - This course evaluates a student's active and meaningful contribution to class discussions - the relevance, depth, and frequency of comments or sharing in the class. This also measures active listening and the ability to build on peers' ideas, demonstrating engagement beyond just speaking.

Final Report (20%) - A final business case will be shared to the class, with guide questions, each student will write a short written analysis of the identified case. Evaluation will be based on how the students will apply the data analytics principle in the said business case.

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HBR Guide to Data Analytics Basics for Managers: This book is a practical guide for managers and leaders who aren't data experts. It covers the fundamentals of the data analysis process, from identifying key metrics and asking the right questions to interpreting results and communicating findings. It helps bridge the gap between business strategy and technical analytics.

Strategic Analytics: The Insights You Need from Harvard Business Review: This book provides a collection of HBR articles on the strategic applications of data analytics. It focuses on how to adopt analytics throughout an organization, build a data-driven culture, and identify the key talents needed to succeed. It's designed to help leaders stay on top of the latest developments in the field.

Competing on Analytics: The New Science of Winning: Considered a classic in the field, this book by Thomas H. Davenport and Jeanne G. Harris outlines the roadmap for becoming an analytical competitor. It provides a framework for how companies can use sophisticated analytics to create new business strategies and gain a competitive edge. This book is more focused on the organizational and strategic aspects of analytics.

Python for Data Analysis: This is often considered the definitive guide for using the pandas library, created by the author himself. It's a hands-on, practical book that shows you how to effectively clean, manipulate, and process data using Python. It's an invaluable resource for anyone who will be handling real-world, messy datasets, and it’s especially useful for the early stages of a data analytics project

Data Mining for Business Analytics: This book provides a comprehensive overview of data mining concepts, techniques, and applications. The Python version of the book uses code examples to demonstrate methods like classification, clustering, and predictive modeling, all within a business context. It's a solid resource for students looking to understand the mechanics of advanced analytics and machine learning.

First-year course instructor

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