8E550301 Pathway to becoming a Data Scientist

Hiroshima University Syllabus

Japanese

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
（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 SHIOZAKI JUNICHI,MISU TOSHIYUKI

Instructor
(Katakana) 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
Program
Criterion referenced
Evaluation

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.

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

PC or AV used in
Class,etc. 　

(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
Review 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.

Message

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.

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 （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	SHIOZAKI JUNICHI,MISU TOSHIYUKI
Instructor (Katakana)	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 Program
Criterion referenced Evaluation
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.
Text/Reference Books,etc.	To be distributed in class
PC or AV used in Class,etc.
(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 Review	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.
Message
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.

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