(FS)

(FS)

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Global Business

Graduate School of Social Sciences

2Year

KCWMS6MCA3

4.00Credits

演習 (Seminar)

Fall semester 2026

(Fall semester)

Jean-Baptiste SANFO

School of Economics and Management

English

2／9

Before or after class, in the classroom

sanfo@em.u-hyogo.ac.jp

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2◎／3◎／4◎

N/a

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Course Objectives

This seminar introduces students to quantitative research methods in econometrics and artificial intelligence (machine learning). The course focuses on developing the ability to design empirical research, apply econometric and machine learning models, and critically evaluate quantitative results. Through hands-on exercises and project-based work, students will develop the skills necessary to conduct independent quantitative analysis and communicate findings effectively in academic and professional settings.

Learning Outcomes

By the end of this course, students will be able to:

1. Apply econometric and artificial intelligence methods to data preparation, visualization, and quantitative analysis using appropriate statistical and computational tools.
2. Critically interpret, validate, and clearly communicate quantitative results for academic, policy, and applied audiences.
3. Design and carry out an independent quantitative research project, from research question formulation to final presentation.

econometrics, machine learning, Python, R

Course Overview

This seminar provides an introduction to quantitative research using econometric and artificial intelligence (machine learning) methods. Students learn how to design empirical research, analyze data using modern quantitative tools, and critically evaluate results. Through hands-on exercises and an independent research project, the course emphasizes interpretation, rigor, and clear communication of quantitative findings for academic and applied contexts.

Hybrid (In-person)

Consultation

Limited permission for use

Students are required to comply with the University of Hyogo's policy regarding the use of generative AI tools. Generative AI may be used as a supplementary aid for learning activities such as drafting reports or conducting preliminary research, provided that students critically assess the content produced. Students are responsible for verifying the accuracy of all information, properly acknowledging sources and references, and ensuring that submitted work reflects their own understanding and independent effort. Assignments generated primarily by generative AI, or submitted without appropriate revision and original contribution, are not permitted.

If inappropriate use of generative AI is identified, the assignment may receive no credit or other academic measures may be taken in accordance with university regulations.

None

Reference will be shared at the class. 

【Pre-Learning】Students will be provided with lecture notes and handouts beforehand. Please read them before the class.

【Post- Learning】Lecture notes and handouts will contain exercises or questions. Please review them after the class.

Students will be asked to share their understanding of given concepts or asked to report orally what they discussed in groups.

Models will be estimated in class using real-world data. We will discuss the estimation results together.

Students will estimate models and give presentations on their findings.

Engagement in classroom activities (40%)

Research project and research presentation (60%)

In the classroom

N/A

Remember that making mistakes is a natural part of the learning process. Don’t fear mistakes; instead, view them as valuable opportunities for growth. Be proactive in your learning by seeking help, asking questions, and sharing your thoughts. Learning is a collaborative effort, and I’m here to support you every step of the way. Just as you are learning, so am I; together, we can create a positive and enriching experience. Let’s engage, explore, and learn from each other!