WMK01400 Practice of Data Analysis for Education and Social Data Science Programs I

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Social Data Science Program

Lecture Code WMK01400 Subject Classification Specialized Education

Subject Name 多分野データ解析実践演習 I

Subject Name
（Katakana）タブンヤデータカイセキジッセンエンシュウ１

Subject Name in
English Practice of Data Analysis for Education and Social Data Science Programs I

Instructor WAKUDA YUKI,KAJIKAWA HIROAKI,HARADA YUSUKE

Instructor
(Katakana) ワクダ　ユウキ,カジカワ　ヒロアキ,ハラダ　ユウスケ

Campus Higashi-Senda Semester/Term 1st-Year,  Second Semester,  Second Semester

Days, Periods, and Classrooms (2nd) Tues11-12

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

Exercise-centered, discussion, student presentations

Credits 1.0 Class Hours/Week 2 Language of Instruction J : Japanese

Course Level 7 : Graduate Special Studies

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 05 : Sociology

Eligible Students Students of Social Data Science program

Keywords Data Science (DS), Artificial Intelligence (AI), Social Sciences, Education, PBL (Project/Problem-Based Learning), Problem Solving, DS Process, Soft Skills, DS Project Management, Team Collaboration, Presentation

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline Course Overview
In this course, students will personally or collaboratively practice the end-to-end workflow of a Data Science (DS) project aimed at solving business and social challenges. Specifically, students will select themes (datasets and problem settings) from various fields such as Psychology, Economics, Social Sciences, and Local Administration. By executing these projects according to the standard DS process-Problem Definition → Data Understanding → Pre-processing → Analysis → Evaluation and Interpretation → Reporting-students will systematically learn the practical application of DS (Sessions 1-9). Following feedback from the mid-term presentation (Session 10), students will deepen their re-analysis and discussions, leading to the documentation of results and final presentations (Sessions 11-15).
Through PBL (Project/Problem-Based Learning) using real-world data and scenarios across diverse fields, students will also acquire the soft skills necessary for project advancement, including Business Understanding, Logical Thinking, Communication, and Facilitation. By employing a selective PBL task system and incorporating peer reviews during the mid-term and final presentations, students will learn from the DS practices of others in different fields. Please note that the mid-term and final presentations are designed as collaborative sessions between the Social Data Science and Educational Data Science programs.

Learning Goals
- Design DS-based solutions tailored to client needs, using existing business operations or social issues as subject matter.
- Execute the entire DS process within a team, ranging from data pre-processing to model construction, evaluation, and interpretation.
- Document and present DS outcomes effectively in a format that is clear and persuasive to third-party stakeholders.

Class Schedule Session 1: Course Guidance
- Introduction to Multidisciplinary Data Science (DS)
- Course orientation (PBL may be conducted individually or in teams)
- Definitions of key terms and concepts [Lecture]
- Essential skills for Data Science
- [Soft Skills] Personal attributes required for team-based DS practice
Session 2: DS Practice Methodologies (I)
- Overview of the Data Science Process [Lecture]
- Practical applications of DS [Lecture]
- Software execution environment: Google Colaboratory (Colab)
- Introduction to Markdown notation (Supplemental materials provided)
Session 3: DS Practice Methodologies (II)
- DS Process
  - Business Understanding and Data Understanding
- Methods for DS project design [Lecture + Workshop]
- Utilization of Open Data
- Handling of confidential and personal information in DS practice
Session 4: PBL Problem Definition
- Selection of project themes and data (choose from approx. four fields):
  - Psychology
   - Statistical testing of experimental results
     (tests of differences / modeling for positive groups)
  - Economics
   - Analysis of economic indicator trends
     (panel data analysis using ML / future forecasting)
  - Social Sciences
   - Survey design and result analysis
     (Principal Component Analysis (PCA), Cluster Analysis, tips for organizing survey data)
  - Local Administration
   - EBPM (Evidence-Based Policy Making) in administrative practice
  - Other
   - Students may use their own research topics and data
- [Soft Skills] Project Management for DS (equivalent to PMBOK 6th Edition)
- [Soft Skills] Meeting Management and Facilitation
Session 5: DS in Practice (I): Data Understanding
- Data cleansing and data preparation [Lecture]
- Introduction to data understanding and data quality assessment [Lecture + Hands-on]
- [Soft Skills] Communication for Data Science
Session 6: DS in Practice (II): Data Pre-processing
- DS Process
  - Data Pre-processing (1) [Lecture + Hands-on]
- Utilization of BI (Business Intelligence) and generative AI-assisted programming [Lecture + Hands-on]
- [Soft Skills] Fundamentals of logical thinking
Session 7: DS in Practice (III): Data Pre-processing (Continued)
- DS Process
  - Data Pre-processing (2) [Lecture + Hands-on]
- Data integration and transformation
Session 8: DS in Practice (IV): Data Analysis
- DS Process
  - Data analysis and modeling [Lecture + Hands-on]
- Selection and execution of analytical methods
Session 9: DS in Practice (V): Evaluation and Interpretation
- DS Process
  - Model evaluation and interpretation [Lecture + Hands-on]
- Verification of consistency with hypotheses
- Review of statistical testing fundamentals
- Application of statistical testing (t-test, Mann-Whitney U test)
Session 10: Mid-term Presentation
- Presentation of DS project design and analytical progress
- Review and peer evaluation by external industry experts/practitioners
- Identification of areas for improvement
Session 11: DS in Practice (VI): Feedback
- Incorporation of feedback and insights
- Supplemental data acquisition and further analysis
- Fundamentals of intellectual property and contracts (patents, NDA, copyright)
Session 12: DS in Practice (VII): Re-analysis
- Discussion and organization of analytical results
- Preparation of presentation materials
- [Soft Skills] Documentation of DS outcomes and presentation skills
Session 13: DS in Practice (VIII): Discussion and Documentation
- Presentation rehearsal
- Final adjustment of narrative and storyline
- [Soft Skills] Critical thinking for Data Science
Session 14: Final Presentation
- Final presentation by teams
- Review and peer evaluation by external industry experts/practitioners
Session 15: Wrap-up
- Reflection on the entire course
- Social applications of Data Science
- Transition to "Applied Multidisciplinary Data Analysis II"

Reports may be assigned in each class, in addition to the mid-term and final presentations.

Text/Reference
Books,etc. Introduce useful materials for exercises as appropriate.

PC or AV used in
Class,etc. Handouts, Microsoft Teams, Zoom, moodle

(More Details) Ovice

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

Suggestions on
Preparation and
Review Preparation (Before Class):
- Take a look at the topics for the next class in advance, and come prepared with your own thoughts and questions.
- If there is any homework from the previous class, try to make some progress on it before the next session.
Review (After Class):
- Continue working on anything from previous classes that you did not finish during class time.
- If there were things you did not understand, don’t leave them unclear-make a note of them and bring them to the next class.
- Use the post-class report to organize the day’s discussion and analysis in your own words.

Requirements "Practice of Data Analysis for Education and Social Data Science Programs I" is a common subject for the Social Data Science Program and the Education Data Science Program. This course (Lecture Code: WMK01400) is aimed at students in Social Data Science Program. Students in Education Data Science Program should take "Practice of Data Analysis for Education and Social Data Science Programs I" for Education Data Science Program (Lecture Code: WNF10050).

Grading Method Your final grade will be determined based on your active class participation, the quality of your presentations (mid-term and final), and your assignment reports.
- Class Participation (Attendance and Active Involvement in Lectures/Workshops): 40%
- Assignment Reports: 30%
- Presentations (Mid-term and Final): 30%

Practical Experience Experienced

Summary of Practical Experience and Class Contents based on it Instructor’s Professional Background and Practical Application
Leveraging professional experience in the commercialization of machine learning and collaborative research with private companies, this course focuses on machine learning methodologies that are highly effective and applicable in real-world business environments.

Message The role of a Data Scientist extends far beyond mere data analysis. It requires the ability to clearly define goals and strategically construct analytical processes to achieve them. Furthermore, it is essential to solve business and social challenges using data to create new value, while communicating results in a way that is easily understood by the general public.
To excel in this profession, you need more than just "hard skills" in data processing and analysis; you must possess the ability to correctly understand the current situation, identify core issues, and design actionable tasks. In addition, "soft skills"-such as team collaboration to drive projects forward and the ability to communicate outcomes clearly-are indispensable.
In this course, you will experience the entire journey of a Data Science (DS) project-from initial design to final reporting-using real-world data and social issues. Through PBL (Project-Based Learning), you will engage with practical data science across multiple disciplines. By immersing yourselves in diverse DS practices, you will aim to acquire the ready-to-work competencies needed to master Data Science in the real world.

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	Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Social Data Science Program
Lecture Code	WMK01400	Subject Classification	Specialized Education
Subject Name	多分野データ解析実践演習 I
Subject Name （Katakana）	タブンヤデータカイセキジッセンエンシュウ１
Subject Name in English	Practice of Data Analysis for Education and Social Data Science Programs I
Instructor	WAKUDA YUKI,KAJIKAWA HIROAKI,HARADA YUSUKE
Instructor (Katakana)	ワクダ　ユウキ,カジカワ　ヒロアキ,ハラダ　ユウスケ
Campus	Higashi-Senda	Semester/Term	1st-Year, Second Semester, Second Semester
Days, Periods, and Classrooms	(2nd) Tues11-12
Lesson Style	Seminar	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive)
Exercise-centered, discussion, student presentations
Credits	1.0	Class Hours/Week	2	Language of Instruction	J : Japanese
Course Level	7 : Graduate Special Studies
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	05 : Sociology
Eligible Students	Students of Social Data Science program
Keywords	Data Science (DS), Artificial Intelligence (AI), Social Sciences, Education, PBL (Project/Problem-Based Learning), Problem Solving, DS Process, Soft Skills, DS Project Management, Team Collaboration, Presentation
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	Course Overview In this course, students will personally or collaboratively practice the end-to-end workflow of a Data Science (DS) project aimed at solving business and social challenges. Specifically, students will select themes (datasets and problem settings) from various fields such as Psychology, Economics, Social Sciences, and Local Administration. By executing these projects according to the standard DS process-Problem Definition → Data Understanding → Pre-processing → Analysis → Evaluation and Interpretation → Reporting-students will systematically learn the practical application of DS (Sessions 1-9). Following feedback from the mid-term presentation (Session 10), students will deepen their re-analysis and discussions, leading to the documentation of results and final presentations (Sessions 11-15). Through PBL (Project/Problem-Based Learning) using real-world data and scenarios across diverse fields, students will also acquire the soft skills necessary for project advancement, including Business Understanding, Logical Thinking, Communication, and Facilitation. By employing a selective PBL task system and incorporating peer reviews during the mid-term and final presentations, students will learn from the DS practices of others in different fields. Please note that the mid-term and final presentations are designed as collaborative sessions between the Social Data Science and Educational Data Science programs. Learning Goals - Design DS-based solutions tailored to client needs, using existing business operations or social issues as subject matter. - Execute the entire DS process within a team, ranging from data pre-processing to model construction, evaluation, and interpretation. - Document and present DS outcomes effectively in a format that is clear and persuasive to third-party stakeholders.
Class Schedule	Session 1: Course Guidance - Introduction to Multidisciplinary Data Science (DS) - Course orientation (PBL may be conducted individually or in teams) - Definitions of key terms and concepts [Lecture] - Essential skills for Data Science - [Soft Skills] Personal attributes required for team-based DS practice Session 2: DS Practice Methodologies (I) - Overview of the Data Science Process [Lecture] - Practical applications of DS [Lecture] - Software execution environment: Google Colaboratory (Colab) - Introduction to Markdown notation (Supplemental materials provided) Session 3: DS Practice Methodologies (II) - DS Process - Business Understanding and Data Understanding - Methods for DS project design [Lecture + Workshop] - Utilization of Open Data - Handling of confidential and personal information in DS practice Session 4: PBL Problem Definition - Selection of project themes and data (choose from approx. four fields): - Psychology - Statistical testing of experimental results (tests of differences / modeling for positive groups) - Economics - Analysis of economic indicator trends (panel data analysis using ML / future forecasting) - Social Sciences - Survey design and result analysis (Principal Component Analysis (PCA), Cluster Analysis, tips for organizing survey data) - Local Administration - EBPM (Evidence-Based Policy Making) in administrative practice - Other - Students may use their own research topics and data - [Soft Skills] Project Management for DS (equivalent to PMBOK 6th Edition) - [Soft Skills] Meeting Management and Facilitation Session 5: DS in Practice (I): Data Understanding - Data cleansing and data preparation [Lecture] - Introduction to data understanding and data quality assessment [Lecture + Hands-on] - [Soft Skills] Communication for Data Science Session 6: DS in Practice (II): Data Pre-processing - DS Process - Data Pre-processing (1) [Lecture + Hands-on] - Utilization of BI (Business Intelligence) and generative AI-assisted programming [Lecture + Hands-on] - [Soft Skills] Fundamentals of logical thinking Session 7: DS in Practice (III): Data Pre-processing (Continued) - DS Process - Data Pre-processing (2) [Lecture + Hands-on] - Data integration and transformation Session 8: DS in Practice (IV): Data Analysis - DS Process - Data analysis and modeling [Lecture + Hands-on] - Selection and execution of analytical methods Session 9: DS in Practice (V): Evaluation and Interpretation - DS Process - Model evaluation and interpretation [Lecture + Hands-on] - Verification of consistency with hypotheses - Review of statistical testing fundamentals - Application of statistical testing (t-test, Mann-Whitney U test) Session 10: Mid-term Presentation - Presentation of DS project design and analytical progress - Review and peer evaluation by external industry experts/practitioners - Identification of areas for improvement Session 11: DS in Practice (VI): Feedback - Incorporation of feedback and insights - Supplemental data acquisition and further analysis - Fundamentals of intellectual property and contracts (patents, NDA, copyright) Session 12: DS in Practice (VII): Re-analysis - Discussion and organization of analytical results - Preparation of presentation materials - [Soft Skills] Documentation of DS outcomes and presentation skills Session 13: DS in Practice (VIII): Discussion and Documentation - Presentation rehearsal - Final adjustment of narrative and storyline - [Soft Skills] Critical thinking for Data Science Session 14: Final Presentation - Final presentation by teams - Review and peer evaluation by external industry experts/practitioners Session 15: Wrap-up - Reflection on the entire course - Social applications of Data Science - Transition to "Applied Multidisciplinary Data Analysis II" Reports may be assigned in each class, in addition to the mid-term and final presentations.
Text/Reference Books,etc.	Introduce useful materials for exercises as appropriate.
PC or AV used in Class,etc.	Handouts, Microsoft Teams, Zoom, moodle
(More Details)	Ovice
Learning techniques to be incorporated	Discussions, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning, Post-class Report
Suggestions on Preparation and Review	Preparation (Before Class): - Take a look at the topics for the next class in advance, and come prepared with your own thoughts and questions. - If there is any homework from the previous class, try to make some progress on it before the next session. Review (After Class): - Continue working on anything from previous classes that you did not finish during class time. - If there were things you did not understand, don’t leave them unclear-make a note of them and bring them to the next class. - Use the post-class report to organize the day’s discussion and analysis in your own words.
Requirements	"Practice of Data Analysis for Education and Social Data Science Programs I" is a common subject for the Social Data Science Program and the Education Data Science Program. This course (Lecture Code: WMK01400) is aimed at students in Social Data Science Program. Students in Education Data Science Program should take "Practice of Data Analysis for Education and Social Data Science Programs I" for Education Data Science Program (Lecture Code: WNF10050).
Grading Method	Your final grade will be determined based on your active class participation, the quality of your presentations (mid-term and final), and your assignment reports. - Class Participation (Attendance and Active Involvement in Lectures/Workshops): 40% - Assignment Reports: 30% - Presentations (Mid-term and Final): 30%
Practical Experience	Experienced
Summary of Practical Experience and Class Contents based on it	Instructor’s Professional Background and Practical Application Leveraging professional experience in the commercialization of machine learning and collaborative research with private companies, this course focuses on machine learning methodologies that are highly effective and applicable in real-world business environments.
Message	The role of a Data Scientist extends far beyond mere data analysis. It requires the ability to clearly define goals and strategically construct analytical processes to achieve them. Furthermore, it is essential to solve business and social challenges using data to create new value, while communicating results in a way that is easily understood by the general public. To excel in this profession, you need more than just "hard skills" in data processing and analysis; you must possess the ability to correctly understand the current situation, identify core issues, and design actionable tasks. In addition, "soft skills"-such as team collaboration to drive projects forward and the ability to communicate outcomes clearly-are indispensable. In this course, you will experience the entire journey of a Data Science (DS) project-from initial design to final reporting-using real-world data and social issues. Through PBL (Project-Based Learning), you will engage with practical data science across multiple disciplines. By immersing yourselves in diverse DS practices, you will aim to acquire the ready-to-work competencies needed to master Data Science in the real world.
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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