Hiroshima University Syllabus

Back to syllabus main page
Academic Year 2022Year 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
Subject Name in
Pathway to becoming a Data Scientist
シオザキ ジュンイチ,ミス トシユキ
Campus Higashi-Hiroshima Semester/Term 1st-Year,  First Semester,  Intensive
Days, Periods, and Classrooms (Int) Inte:IAS K102
Lesson Style Lecture Lesson Style
(More Details)
Problem based learning, based on real data
(Depending on the circumstances, online lessons may be undertaken instead.) 
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
Criterion referenced
Class Objectives
/Class Outline
In recent years, an increase in the interest shown in Big Data, AI, etc. has been observed. From an industrial perspective, "Data Scientists" are considered as driving human resources who can manage the enormous amount of data and support crucial decision making to improve production lines, marketing strategy, search for core materials, product innovation, and so forth. On the other hand, application of expertise and skills in data science is expected to trigger academic innovation or to create new values.
This class is designed to nurture practical data science skills by tackling project themes, with the use of statistical analysis and/or IT-related skills.
Students are expected to work on case studies based on the practical data offered by companies and present analysis and results of his/her project.
Class Schedule The following will be covered in the course.

<Class Schedule>

Lesson 1: Friday, May 13, 5th–6th Periods (12:50–14:20)
  Orientation / Briefing of the data to be offered

Lesson 2: Friday, May 20, 5th–6th Periods (12:50–14:20)
  1. Lecture on the data analysis based on the historical cases 
  2. Data distribution

Lesson 3: Friday, June 10, 5th–7th Periods (12:50–15:20)
  Presentation on the concept of the data analysis

Lesson 4: Friday, July 1, 5th–8th Periods (12:50–16:05)
  Mid-term Presentation

Lesson 5: Friday, July 29, 5th–8th Periods (12:50–16:05)
  Final Presentation

1. Data analysis and wrap-up in groups or individually (for 40 hours or so in total).
2. Final presentation in groups or individually. Detailed instructions for the Final Report Submission will be given during the class.
3. You are encouraged to apply for the "Marketing Analysis Contest 2022" hosted by NRI and create a thesis based on the final achievement. 
To be distributed in class 
PC or AV used in
(More Details) PC, DVD, etc.

Your own PC or designated PC should be used for data analysis. 
Learning techniques to be incorporated  
Suggestions on
Preparation and
You should review, in advance, basics of statistics as much as possible. It is also preferable to master any one of the Excel statistical analysis function, statistical software such as SPSS or STATA, etc., or programming languages such as R, Python, etc. 
Requirements Preferred to have basic knowledge on statistics and experience of statistical software use or programming. 
Grading Method To be evaluated based on final presentation, final report and final evaluation by proposing companies. 
Practical Experience Experienced  
Summary of Practical Experience and Class Contents based on it The active consultant who takes charge of drafting a marketing strategy lectures on how to advance it data science while utilizing actual data. 
Other Please contact Global Career Design Center (E-mail:wakateyousei@office.hiroshima-u.ac.jp) and Professor Misu (E-mail: maxmisu@hiroshima-u.ac.jp) for any queries. 
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. 
Back to syllabus main page