WMK01500 Practice of Data Analysis for Education and Social Data Science Programs II

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 WMK01500 Subject Classification Specialized Education

Subject Name 多分野データ解析実践演習 II

Subject Name
（Katakana）タブンヤデータカイセキジッセンエンシュウ２

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

Instructor WAKUDA YUKI,KAJIKAWA HIROAKI,HARADA YUSUKE,SUZUKI YOSHIHISA

Instructor
(Katakana) ワクダ　ユウキ,カジカワ　ヒロアキ,ハラダ　ユウスケ,スズキ　ヨシヒサ

Campus Higashi-Senda Semester/Term 2nd-Year, First Semester, First Semester

Days, Periods, and Classrooms (1st) Mon11-12

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-Based Learning / Problem-Based Learning),Problem Discovery / Issue Identification,Innovation Process,Soft Skills,Creation and Management of Data Science Projects,Social Implementation / Real-world Implementation,Startups,Business Administration / Management Studies,Business Creation / Business Development

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
Through this 15-session course, students will practice the process of "Social Implementation" (business development and real-world application) to deliver Data Science (DS) results to society. Building on the problem-solving DS skills acquired in "Practice of Data Analysis for Education and Social Data Science Programs I", students will learn the procedures for identifying social and business issues, setting problems and constructing hypotheses based on data, and structuring those results as social value (Sessions 1-4). Subsequently, students will proceed with actual data analysis and evaluation, leading to an intermediate presentation (Sessions 5-9). In the latter half of the course, students will experience business innovation procedures using Lean Startup methodologies (CPF, PSF, and PMF), connecting these to evaluation design using KPI Management/BSC and Strategy Maps, and culminating in a final pitch presentation (Sessions 10–15).
This course focuses not only on data analysis and performance evaluation but also on how to bridge those results with society and translate them into actionable business steps to solve problems across various fields.

Learning Goals
- Understand and practice the process of problem discovery and innovation through DS, including core elements of the startup ecosystem.
- Identify social or business challenges independently and perform data-driven problem definition and hypothesis construction.
- Structure the social value of DS outcomes and formulate/communicate actionable business strategies.

Class Schedule Session 1: Course Guidance
- Course orientation and learning goals
- Shifting from problem solving to problem discovery
- The role of Data Science (DS) and its social impact [Lecture]
- Introduction to Business Administration [Lecture]
Session 2: Understanding Methodology
- Overview of DS and the innovation process [Lecture]
- Theme exploration: selecting a project (using your own data or choosing a provided scenario)
- Fundamentals of DS and the startup ecosystem
Session 3: Problem Discovery and DS Design (I)
- DS Process: business understanding and data understanding
- Social Implementation Process (I): organizing ideas
- Business Model Canvas (BMC) for social implementation
- Value Proposition Canvas (VPC) for social implementation
Session 4: Problem Discovery and DS Design (II)
- Social Implementation Process (II): empathizing with customer pain points (Customer-Problem Fit)
- DS design and project planning [Lecture + Workshop]
- Problem Interview techniques (optional)
Session 5: DS in Practice (I): Data Procurement and Preparation
- DS Process: data pre-processing (1) [Lecture + Hands-on]
- Initiation of data preparation
- Team building and team management [Lecture]
- [Soft Skills] Agile project management [Lecture]
Session 6: DS in Practice (II): Data Pre-processing
- Data cleansing techniques
- Generative AI-assisted programming [Lecture + Hands-on]
- [Soft Skills] Theory of management and team leadership [Lecture]
Session 7: DS in Practice (III): Data Analysis and Evaluation
- DS Process: data analysis and modeling [Lecture + Hands-on]
- Selection and execution of analytical methods
- KPI management / Balanced Scorecard (BSC) and strategy maps [Lecture]
Session 8: DS in Practice (IV): Discussion of Results
- DS Process: model evaluation and interpretation [Lecture + Hands-on]
- Stages of evaluation and the logic model (Input → Output → Outcome)
Session 9: Mid-term Presentation
- Presentation of analytical progress
- Review and peer evaluation by external industry experts
- Identification of areas for improvement
Session 10: Social Implementation in Practice (I)
- Incorporating feedback: pivoting and re-analysis
- Social Implementation Process (III): validating the solution (Problem-Solution Fit / PSF)
- [Soft Skills] Innovation theory [Lecture]
Session 11: Social Implementation in Practice (II)
- Social Implementation Process (IV): product validation (Product-Market Fit / PMF)
- Integration of analytical results
- [Soft Skills] Diffusion of innovation strategies [Lecture]
- [Soft Skills] The science of startups [Lecture]
Session 12: Social Implementation in Practice (III)
- Introduction to pitches and pitch decks [Lecture]
- Preparation of presentation materials (pitch deck creation)
- Finalizing the Business Model Canvas (BMC) and Value Proposition Canvas (VPC)
- Overview of fundraising and financing [Lecture]
Session 13: Social Implementation in Practice (IV)
- Pitch rehearsal
- Final refinement
Session 14: Final Presentation
- Final presentation
- Review and peer evaluation by external industry experts/practitioners
Session 15: Wrap-up and Discussion
- Reflection on the entire course
- Connecting course outcomes to the master’s thesis
- Exploration of future career paths

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

Text/Reference
Books,etc. No specific textbook is required for this course. Supplemental materials and lecture notes will be distributed during each session.

PC or AV used in
Class,etc. Handouts, Microsoft Teams, Zoom, Other (see [More Details]), moodle

(More Details)

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 Applied Multidisciplinary Data Analysis II is a common core subject for both the Social Data Science Program and the Educational Data Science Program.
However, this specific course section is designated for students in the Social Data Science Program. Students enrolled in the Educational Data Science Program must register for the section of "Applied Multidisciplinary Data Analysis II" specifically offered for their own program.

Grading Method Grading Policy
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 Pitch): 30%

Practical Experience Experienced

Summary of Practical Experience and Class Contents based on it 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 true value derived from Data Science (DS) is realized only when it is appropriately delivered to society, creating tangible change as innovation.
In this course, you will experience the entire journey-from discovering challenges on your own to designing data-driven solutions and communicating those outcomes within social and business contexts.
Over the next five to ten years, the convergence of fields-such as "AI x [Your Field]" or "Data Science x [Your Field]"-will undoubtedly become the critical key to business success.
Through this course, I expect you to acquire the practical skills necessary to realize new value and innovation in your areas of interest. My goal is for you to develop the practical competence to challenge yourself with data-driven business innovation whenever you encounter diverse problems in your future research or professional careers after graduation.

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	WMK01500	Subject Classification	Specialized Education
Subject Name	多分野データ解析実践演習 II
Subject Name （Katakana）	タブンヤデータカイセキジッセンエンシュウ２
Subject Name in English	Practice of Data Analysis for Education and Social Data Science Programs II
Instructor	WAKUDA YUKI,KAJIKAWA HIROAKI,HARADA YUSUKE,SUZUKI YOSHIHISA
Instructor (Katakana)	ワクダ　ユウキ,カジカワ　ヒロアキ,ハラダ　ユウスケ,スズキ　ヨシヒサ
Campus	Higashi-Senda	Semester/Term	2nd-Year, First Semester, First Semester
Days, Periods, and Classrooms	(1st) Mon11-12
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-Based Learning / Problem-Based Learning),Problem Discovery / Issue Identification,Innovation Process,Soft Skills,Creation and Management of Data Science Projects,Social Implementation / Real-world Implementation,Startups,Business Administration / Management Studies,Business Creation / Business Development
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 Through this 15-session course, students will practice the process of "Social Implementation" (business development and real-world application) to deliver Data Science (DS) results to society. Building on the problem-solving DS skills acquired in "Practice of Data Analysis for Education and Social Data Science Programs I", students will learn the procedures for identifying social and business issues, setting problems and constructing hypotheses based on data, and structuring those results as social value (Sessions 1-4). Subsequently, students will proceed with actual data analysis and evaluation, leading to an intermediate presentation (Sessions 5-9). In the latter half of the course, students will experience business innovation procedures using Lean Startup methodologies (CPF, PSF, and PMF), connecting these to evaluation design using KPI Management/BSC and Strategy Maps, and culminating in a final pitch presentation (Sessions 10–15). This course focuses not only on data analysis and performance evaluation but also on how to bridge those results with society and translate them into actionable business steps to solve problems across various fields. Learning Goals - Understand and practice the process of problem discovery and innovation through DS, including core elements of the startup ecosystem. - Identify social or business challenges independently and perform data-driven problem definition and hypothesis construction. - Structure the social value of DS outcomes and formulate/communicate actionable business strategies.
Class Schedule	Session 1: Course Guidance - Course orientation and learning goals - Shifting from problem solving to problem discovery - The role of Data Science (DS) and its social impact [Lecture] - Introduction to Business Administration [Lecture] Session 2: Understanding Methodology - Overview of DS and the innovation process [Lecture] - Theme exploration: selecting a project (using your own data or choosing a provided scenario) - Fundamentals of DS and the startup ecosystem Session 3: Problem Discovery and DS Design (I) - DS Process: business understanding and data understanding - Social Implementation Process (I): organizing ideas - Business Model Canvas (BMC) for social implementation - Value Proposition Canvas (VPC) for social implementation Session 4: Problem Discovery and DS Design (II) - Social Implementation Process (II): empathizing with customer pain points (Customer-Problem Fit) - DS design and project planning [Lecture + Workshop] - Problem Interview techniques (optional) Session 5: DS in Practice (I): Data Procurement and Preparation - DS Process: data pre-processing (1) [Lecture + Hands-on] - Initiation of data preparation - Team building and team management [Lecture] - [Soft Skills] Agile project management [Lecture] Session 6: DS in Practice (II): Data Pre-processing - Data cleansing techniques - Generative AI-assisted programming [Lecture + Hands-on] - [Soft Skills] Theory of management and team leadership [Lecture] Session 7: DS in Practice (III): Data Analysis and Evaluation - DS Process: data analysis and modeling [Lecture + Hands-on] - Selection and execution of analytical methods - KPI management / Balanced Scorecard (BSC) and strategy maps [Lecture] Session 8: DS in Practice (IV): Discussion of Results - DS Process: model evaluation and interpretation [Lecture + Hands-on] - Stages of evaluation and the logic model (Input → Output → Outcome) Session 9: Mid-term Presentation - Presentation of analytical progress - Review and peer evaluation by external industry experts - Identification of areas for improvement Session 10: Social Implementation in Practice (I) - Incorporating feedback: pivoting and re-analysis - Social Implementation Process (III): validating the solution (Problem-Solution Fit / PSF) - [Soft Skills] Innovation theory [Lecture] Session 11: Social Implementation in Practice (II) - Social Implementation Process (IV): product validation (Product-Market Fit / PMF) - Integration of analytical results - [Soft Skills] Diffusion of innovation strategies [Lecture] - [Soft Skills] The science of startups [Lecture] Session 12: Social Implementation in Practice (III) - Introduction to pitches and pitch decks [Lecture] - Preparation of presentation materials (pitch deck creation) - Finalizing the Business Model Canvas (BMC) and Value Proposition Canvas (VPC) - Overview of fundraising and financing [Lecture] Session 13: Social Implementation in Practice (IV) - Pitch rehearsal - Final refinement Session 14: Final Presentation - Final presentation - Review and peer evaluation by external industry experts/practitioners Session 15: Wrap-up and Discussion - Reflection on the entire course - Connecting course outcomes to the master’s thesis - Exploration of future career paths Reports may be assigned in each class session, in addition to the mid-term and final presentations.
Text/Reference Books,etc.	No specific textbook is required for this course. Supplemental materials and lecture notes will be distributed during each session.
PC or AV used in Class,etc.	Handouts, Microsoft Teams, Zoom, Other (see [More Details]), moodle
(More Details)
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	Applied Multidisciplinary Data Analysis II is a common core subject for both the Social Data Science Program and the Educational Data Science Program. However, this specific course section is designated for students in the Social Data Science Program. Students enrolled in the Educational Data Science Program must register for the section of "Applied Multidisciplinary Data Analysis II" specifically offered for their own program.
Grading Method	Grading Policy 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 Pitch): 30%
Practical Experience	Experienced
Summary of Practical Experience and Class Contents based on it	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 true value derived from Data Science (DS) is realized only when it is appropriately delivered to society, creating tangible change as innovation. In this course, you will experience the entire journey-from discovering challenges on your own to designing data-driven solutions and communicating those outcomes within social and business contexts. Over the next five to ten years, the convergence of fields-such as "AI x [Your Field]" or "Data Science x [Your Field]"-will undoubtedly become the critical key to business success. Through this course, I expect you to acquire the practical skills necessary to realize new value and innovation in your areas of interest. My goal is for you to develop the practical competence to challenge yourself with data-driven business innovation whenever you encounter diverse problems in your future research or professional careers after graduation.
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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