| Academic Year |
2025Year |
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 |
WMK00100 |
Subject Classification |
Specialized Education |
| Subject Name |
DXの概念と現状 |
Subject Name
（Katakana） |
ディーエックスノガイネントゲンジョウ |
Subject Name in
English |
Introduction to DX: Concept and Current Situation of DX |
| Instructor |
KAJIKAWA HIROAKI,HARADA YUSUKE,SUZUKI YOSHIHISA |
Instructor
(Katakana) |
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 |
1st-Year, First Semester, 1Term |
| Days, Periods, and Classrooms |
(1T) Weds11-14：Online,Higashi-Senda Seminar Rm 9 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Online (on-demand) |
| Lecture-based (video delivery on-demand) |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
J
:
Japanese |
| Course Level |
5
:
Graduate Basic
|
| Course Area（Area） |
24
:
Social Sciences |
| Course Area（Discipline） |
05
:
Sociology |
| Eligible Students |
Master’s Program (First Term) |
| Keywords |
DX (Digital Trans Formation), data-driven, digital technology, agile development, AI, IoT, business model transformation, cloud, big data, RPA, AR/VR, robotics, digitalization, digital rural city concept |
| 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 |
In this lecture, students will be introduced to the concept of digital transformation (DX) and the use of data science and digital technologies essential for DX. By examining the skills, technologies, and examples essential for driving DX, students will deepen their understanding of modern technological innovation and social transformation. The course also focuses on topics such as AI, data science and open data, with the aim of gaining practical knowledge. |
| Class Schedule |
1: Introduction: Overview of the lecture and learning objectives; introduction to the concept of DX .
2：The relationship between DX and data science.
3：Necessary skills for data scientists.
4：Practical examples of data science.
5：Current Status of DX.
6：Current Status of Data Science.
7：Integration of Humanities and Sciences.
8：Communication skills.
9: Advances in digital technology.
10：AI Technologies and Machine Learning.
11：Using Open Data.
12：The concept of collective intelligence.
Sessions 13 - 15 Future AI technologies and impacts: future developments in AI technologies and their impact on society through lecturer lecture videos and discussion. |
Text/Reference
Books,etc. |
DX no Kyokuyo (Textbook of DX), etc. |
PC or AV used in
Class,etc. |
Visual Materials, moodle |
| (More Details) |
|
| Learning techniques to be incorporated |
Discussions, Post-class Report |
Suggestions on
Preparation and
Review |
1–15:
(Preparation) Read assigned materials
(Review) Summarize the comments from discussions and related materials |
| Requirements |
|
| Grading Method |
(Requirements for Earning Credits)
Watch the on-demand videos on Moodle and submit all assigned tasks by the deadlines.
Submit the final report as instructed.
Even if all assignments and reports are submitted, credit may not be granted depending on the evaluation criteria below.
(Evaluation)
Evaluation is based on the reports submitted each time. |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
If you do not submit the required assignment, the score for that session will be zero. Please pay close attention to each deadline and the submission method. |
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. |