8E550301 Pathway to becoming a Data Scientist

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School Common Graduate Courses (Doctoral Course)

Lecture Code 8E550301 Subject Classification Common Graduate Courses

Subject Name データサイエンティスト養成

Subject Name
（Katakana）データサイエンティストヨウセイ

Subject Name in
English Pathway to becoming a Data Scientist

Instructor TAMURA HAJIME

Instructor
(Katakana) タムラ　ハジメ

Campus Higashi-Hiroshima Semester/Term 1st-Year, First Semester, Intensive

Days, Periods, and Classrooms (Int) Inte

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, Online (simultaneous interactive)

Problem based learning, based on real data
( Lessons 1 and 4 are face-to-face only. For lessons 2 and 3, you can choose between face-to-face or Zoom.)

Credits 1.0 Class Hours/Week 　 Language of Instruction J : Japanese

Course Level 7 : Graduate Special Studies

Course Area（Area） 21 : Fundamental Competencies for Working Persons

Course Area（Discipline） 03 : Career Education

Eligible Students Doctoral students

Keywords PBL, Data Science, Data Analysis, Marketing Analysis

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) This course is one of the elective subjects in the category of "Career Development and Data Literacy Courses" for Common Graduate Courses. This category of courses aims to provide opportunities for students to learn about the development of the current social systems, to gain knowledge needed for the future, to concretely tackle the challenges facing modern society, and to acquire the ability to utilize knowledge and skills.
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline Today, data scientists play a crucial role in supporting decision-making across all industries by finding valuable insights from vast amounts of data. In this course, you will build basic analysis models using real-world real estate market data. You will learn versatile processes required in any field, such as data visualization, cleansing, and building machine learning models. Using an educational Kaggle environment, you will evaluate the accuracy of your models in real-time and learn how to improve them through trial and error. The goal is to gain the ability to connect data science methods with your own field of expertise to create new academic discoveries and value.

Class Schedule Instructor: Hajime Tamura (Nomura Research Institute, Ltd.)

Analyze real estate market data and build models to calculate prices.
Estimate and evaluate model accuracy (scores) using the Kaggle environment.
Give a presentation and submit a final report based on your results.

Schedule: 4 Intensive Lessons
Lesson 1: May 15 (Fri), Periods 5-8 (12:50-16:05) – Face-to-Face

Orientation, data briefing, and analysis tips.
Basics of data analysis (regression, decision tree, machine learning, etc.).
Explanation of analysis methods for the exercise.

Lesson 2: May 29 (Fri), Periods 5-8 (12:50-16:05) – Choice of Face-to-Face or Zoom

How to use the data analysis environment.
Building and sharing a baseline model.
Explanation of methods to improve your model.

Lesson 3: June 19 (Fri), Periods 5-8 (12:50-16:05) – Choice of Face-to-Face or Zoom

Mid-term presentation (analysis results and model).
Evaluation of model accuracy (Kaggle score).

Lesson 4: July 10 (Fri), Periods 5-8 (12:50-16:05) – Face-to-Face

Final presentation (results, model, and business/research proposal).
Final evaluation of model accuracy (Kaggle score).

Text/Reference
Books,etc. To be distributed in class

PC or AV used in
Class,etc. Visual Materials, Zoom, Other (see [More Details]), moodle

(More Details) A laptop with Wi-Fi / Internet connectivity.

Learning techniques to be incorporated Discussions, PBL (Problem-based Learning)/ TBL (Team-based Learning), Post-class Report

Suggestions on
Preparation and
Review Review basic statistics and get used to handling data in Excel to help your understanding.
Reference Book: "Data Scientist Basic Skills 84" (Nikkei Bunko Visual, written by NRI Data Science Lab).

Requirements Basic knowledge of statistics or programming (Python, etc.) is helpful, but beginners are welcome. We will explain how to use the basic tools in class. Students from all academic fields are encouraged to join.

Grading Method Final (midterm) examination, Report, Quiz, Oral presentation, Assignment
Attitude toward the class, etc.

Practical Experience Experienced

Summary of Practical Experience and Class Contents based on it An active data scientist working on marketing and AI projects will teach practical model-building using real-world data.

Message Data science is a powerful tool for any field, not just for specialists. In this course, you will experience the fun and challenges of building models with real data. Use these skills to create new value in your own area of expertise.

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2026Year	School/Graduate School	Common Graduate Courses (Doctoral Course)
Lecture Code	8E550301	Subject Classification	Common Graduate Courses
Subject Name	データサイエンティスト養成
Subject Name （Katakana）	データサイエンティストヨウセイ
Subject Name in English	Pathway to becoming a Data Scientist
Instructor	TAMURA HAJIME
Instructor (Katakana)	タムラ　ハジメ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, First Semester, Intensive
Days, Periods, and Classrooms	(Int) Inte
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive)
Problem based learning, based on real data ( Lessons 1 and 4 are face-to-face only. For lessons 2 and 3, you can choose between face-to-face or Zoom.)
Credits	1.0	Class Hours/Week		Language of Instruction	J : Japanese
Course Level	7 : Graduate Special Studies
Course Area（Area）	21 : Fundamental Competencies for Working Persons
Course Area（Discipline）	03 : Career Education
Eligible Students	Doctoral students
Keywords	PBL, Data Science, Data Analysis, Marketing Analysis
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	This course is one of the elective subjects in the category of "Career Development and Data Literacy Courses" for Common Graduate Courses. This category of courses aims to provide opportunities for students to learn about the development of the current social systems, to gain knowledge needed for the future, to concretely tackle the challenges facing modern society, and to acquire the ability to utilize knowledge and skills.
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	Today, data scientists play a crucial role in supporting decision-making across all industries by finding valuable insights from vast amounts of data. In this course, you will build basic analysis models using real-world real estate market data. You will learn versatile processes required in any field, such as data visualization, cleansing, and building machine learning models. Using an educational Kaggle environment, you will evaluate the accuracy of your models in real-time and learn how to improve them through trial and error. The goal is to gain the ability to connect data science methods with your own field of expertise to create new academic discoveries and value.
Class Schedule	Instructor: Hajime Tamura (Nomura Research Institute, Ltd.) Analyze real estate market data and build models to calculate prices. Estimate and evaluate model accuracy (scores) using the Kaggle environment. Give a presentation and submit a final report based on your results. Schedule: 4 Intensive Lessons Lesson 1: May 15 (Fri), Periods 5-8 (12:50-16:05) – Face-to-Face Orientation, data briefing, and analysis tips. Basics of data analysis (regression, decision tree, machine learning, etc.). Explanation of analysis methods for the exercise. Lesson 2: May 29 (Fri), Periods 5-8 (12:50-16:05) – Choice of Face-to-Face or Zoom How to use the data analysis environment. Building and sharing a baseline model. Explanation of methods to improve your model. Lesson 3: June 19 (Fri), Periods 5-8 (12:50-16:05) – Choice of Face-to-Face or Zoom Mid-term presentation (analysis results and model). Evaluation of model accuracy (Kaggle score). Lesson 4: July 10 (Fri), Periods 5-8 (12:50-16:05) – Face-to-Face Final presentation (results, model, and business/research proposal). Final evaluation of model accuracy (Kaggle score).
Text/Reference Books,etc.	To be distributed in class
PC or AV used in Class,etc.	Visual Materials, Zoom, Other (see [More Details]), moodle
(More Details)	A laptop with Wi-Fi / Internet connectivity.
Learning techniques to be incorporated	Discussions, PBL (Problem-based Learning)/ TBL (Team-based Learning), Post-class Report
Suggestions on Preparation and Review	Review basic statistics and get used to handling data in Excel to help your understanding. Reference Book: "Data Scientist Basic Skills 84" (Nikkei Bunko Visual, written by NRI Data Science Lab).
Requirements	Basic knowledge of statistics or programming (Python, etc.) is helpful, but beginners are welcome. We will explain how to use the basic tools in class. Students from all academic fields are encouraged to join.
Grading Method	Final (midterm) examination, Report, Quiz, Oral presentation, Assignment Attitude toward the class, etc.
Practical Experience	Experienced
Summary of Practical Experience and Class Contents based on it	An active data scientist working on marketing and AI projects will teach practical model-building using real-world data.
Message	Data science is a powerful tool for any field, not just for specialists. In this course, you will experience the fun and challenges of building models with real data. Use these skills to create new value in your own area of expertise.
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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