シラバス情報

授業科目名
Special Topics I
(英語名)
Special Topics I
科目区分
Core Specialized Courses
対象学生
社会科学研究科
学年
学年指定なし
ナンバリングコード
KCWMS5MCA1
単位数
2単位
ナンバリングコードは授業科目を管理する部局、学科、教養専門の別を表します。詳細は右上の?から別途マニュアルをダウンロードしてご確認ください。
授業の形態
講義 (Lecture)
開講時期
2026年度後期
(Fall semester)
担当教員
Jean-Baptiste M.B. SANFO
所属
Graduate School of Social Sciences
授業での使用言語
英語
関連するSDGs目標
目標9
オフィスアワー・場所
Before or after class, in the classroom

Office: Research Building I Room A 204
連絡先
sanfo@em.u-hyogo.ac.jp

対応するディプロマ・ポリシー(DP)・教職課程の学修目標
二重丸は最も関連するDP番号を、丸は関連するDPを示します。
学部DP
研究科DP
全学DP
教職課程の学修目標

講義目的・到達目標
【Course Objectives】

This course focuses on Data Science and Machine Learning for Business Research. It aims to:

Expose students to modern data-driven methods used in business research
Guide students to develop the ability to critically evaluate machine learning applications in academic and applied business contexts
Bridge traditional econometrics and modern machine learning approaches
Prepare students to conduct independent, data-driven research projects using


【Learning Outcome】

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

  1. Demonstrate understanding of artificial intelligence applications in business
  2. Apply selected machine learning methods to business-related datasets
  3. Evaluate model performance and limitations
  4. Interpret and explain machine learning results for research and decision-making
  5. Design and present an independent research project using data science tools


授業のサブタイトル・キーワード
AI, machine learning, business analytics, predictive analytics
講義内容・授業計画
【Course Overview】

This course introduces artificial intelligence (AI) in business, with a practical focus on data science and machine learning for real-world business research and decision-making. Students will learn when and how machine learning tools can be used to analyze complex business data, alongside traditional econometric methods. Rather than focusing on programming, the course emphasizes designing good research questions, understanding results, and critically evaluating AI-based insights for managerial and policy decisions.


対面・遠隔の別
ハイブリッド(対面)
実施方法及び遠隔上限適用対象の別
The course combines lectures, guided discussions of academic papers, hands-on data analysis, and student-led presentations. Emphasis is placed on understanding intuition, interpreting results, and connecting methods to real business research questions.
生成AIの利用
利用する場面を限定し許可
生成AI注意点
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.
教科書
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost & Tom Fawcett

ISBN-10: 1449361323
ISBN-13: 978-1449361327
参考文献
事前・事後学習(予習・復習)の内容・時間の目安
【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.

アクティブ・ラーニングの内容
Students will be asked to share their understanding of given concepts or asked to report orally what they discussed in groups.


成績評価の基準・方法
Engagement in classroom activities (20%)
Assignments (20%)  
Mid-term Evaluation (30%)
Final evaluation (30%)

課題・試験結果の開示方法
In the classroom
履修上の注意・履修要件
Regular attendance is required.

実践的教育
N/A
備考
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!
英語版と日本語版との間に内容の相違が生じた場合は、日本語版を優先するものとします。