8E550301 Pathway to becoming a Data Scientist

Hiroshima University Syllabus

Japanese

Academic Year 2024Year 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

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
(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 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.
This year, we will conduct exercises based on real estate data and present results such as planning marketing strategies.

Class Schedule The following will be covered in the course.

Exercises based on real estate market trend data
1. We perform data analysis and model construction using tabular data related to the real estate market.
2.Estimate and evaluate the accuracy of the model using the educational data analysis environment (Kaggle environment).
3.Based on the results of data analysis, we will present the results with the aim of proposing marketing strategies to companies.

<Class Schedule>

Lesson 1: Friday, May 10, 5th–6th Periods (12:50–14:20) TBD
  Orientation, briefing of the data to be offered and analysis points

Lesson 2: Friday, May 17, 5th–7th Periods (12:50–15:20) (Online)
   Fundamentals of data analysis (regression analysis, decision tree analysis, principal component analysis, machine learning, etc.)
  Explanation of analysis methods based on the data used within the exercises
  Distribution of data, explanation of how to operate the data analysis environment, construction and sharing of baseline analysis model

Lesson 3: Friday, June 7, 5th–7th Periods (12:50–15:20) TBD
  Announcement of basic aggregation results of data based on baseline model
  Introduction and explanation of data analysis methods based on analysis policy, introduction of past analysis cases

Lesson 4: Friday, July 28, 5th–8th Periods (12:50–16:05) TBD
  Mid-term Presentation
    Presentation of data analysis results and analysis model
    Evaluation of model accuracy (Kaggle score)

Lesson 5: Friday, July 19, 5th–8th Periods (12:50–16:05) TBD
  Final Presentation
    Analysis model and marketing strategy proposal presentation
    Evaluation of model accuracy (Kaggle score)

1. Data analysis and wrap-up in individually (for 40 hours or so in total).
2. Final presentation in individually. Detailed instructions for the Final Report Submission will be given during the class.

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, the basics of statistics as much as possible.
You should strive to be able to perform basic data analysis using Excel, etc.
also be able to perform basic data analysis by utilizing Excel’s statistical analysis functions, or libraries such as R, Python, etc.
[Reference book] “データサインティスト基礎スキル84” (Nikkei Bunko Visual, NRI Data Science Lab)

Requirements A basic knowledge of statistics, and experience in data analysis using Excel, Python, etc. is desirable, however, even beginners can take this class. The basic ways of usage will be introduced in the lectures.
The capacity of this class is 20 students.

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 and data science utilization project 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	2024Year	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
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 (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	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. This year, we will conduct exercises based on real estate data and present results such as planning marketing strategies.
Class Schedule	The following will be covered in the course. Exercises based on real estate market trend data 1. We perform data analysis and model construction using tabular data related to the real estate market. 2.Estimate and evaluate the accuracy of the model using the educational data analysis environment (Kaggle environment). 3.Based on the results of data analysis, we will present the results with the aim of proposing marketing strategies to companies. <Class Schedule> Lesson 1: Friday, May 10, 5th–6th Periods (12:50–14:20) TBD Orientation, briefing of the data to be offered and analysis points Lesson 2: Friday, May 17, 5th–7th Periods (12:50–15:20) (Online) Fundamentals of data analysis (regression analysis, decision tree analysis, principal component analysis, machine learning, etc.) Explanation of analysis methods based on the data used within the exercises Distribution of data, explanation of how to operate the data analysis environment, construction and sharing of baseline analysis model Lesson 3: Friday, June 7, 5th–7th Periods (12:50–15:20) TBD Announcement of basic aggregation results of data based on baseline model Introduction and explanation of data analysis methods based on analysis policy, introduction of past analysis cases Lesson 4: Friday, July 28, 5th–8th Periods (12:50–16:05) TBD Mid-term Presentation Presentation of data analysis results and analysis model Evaluation of model accuracy (Kaggle score) Lesson 5: Friday, July 19, 5th–8th Periods (12:50–16:05) TBD Final Presentation Analysis model and marketing strategy proposal presentation Evaluation of model accuracy (Kaggle score) 1. Data analysis and wrap-up in individually (for 40 hours or so in total). 2. Final presentation in individually. Detailed instructions for the Final Report Submission will be given during the class.
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, the basics of statistics as much as possible. You should strive to be able to perform basic data analysis using Excel, etc. also be able to perform basic data analysis by utilizing Excel’s statistical analysis functions, or libraries such as R, Python, etc. [Reference book] “データサインティスト基礎スキル84” (Nikkei Bunko Visual, NRI Data Science Lab)
Requirements	A basic knowledge of statistics, and experience in data analysis using Excel, Python, etc. is desirable, however, even beginners can take this class. The basic ways of usage will be introduced in the lectures. The capacity of this class is 20 students.
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 and data science utilization project 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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