Syllabus data

Academic year
2025Year
Term
Second Semester
Course title
DX solutions
Class type
Lecture
Course title (ENG)
DX solutions
Class code・Class name・Teaching forms
Z0400017 DX solutions
Instructor
GUNJI Shigeki
Credits
2.0Credits
Day and Time
Mon.4Period
Campus
Shinjuku Campus
Location
B-0753DX実践ラボ

Relationship between diploma policies and this course
A) A high degree of specialized expertise 50%
B) The skills to use science and technology 50%
C) The ability to conduct research independently, knowledge pertaining to society and occupations, and sense of ethics required of engineers and researchers 0%
D) Creative skills in specific areas of specialization 0%
Goals and objectives
This class provides a practical blueprint, skills, and mindset for digital transformation (DX). Its goal is not just to understand digital technology, but to become a change-agent both in organization and business.
Prerequisites
1. Mathematics (linear algebra, calculus, statistics)
2. Python basics
3. Linux basics
4. Slack (used to communicate with multiple external parties)
5. Highly self-motivated
Method Using AL・ICT
Project Based Learning/Discussion Debate/Group Work/Presentation/Practice Fieldwork

Class schedule
Through lectures and exercises, students will efficiently learn on platforms, data and innovation, which are essential elements for digital transformation. This class will also provide an opportunity to connect with leading business persons active in a wide range of industries. Students will utilize their time outside of class to work in collaboration with the business experts, in order to improve their skills to discover and solve problems. The overall class schedule is below, but the plan may be changed to avoid the external experts' conflicts.

Introduction
1 (F2F): DX Solution Overview
2 (to be announced in class): Experiencing the Business Front #1 - Business Plan Creation

Part I: Container Platform and Innovation
3 (F2F): Team to Drive Innovation
4 (F2F): Agile Mindset Exercise
5 (F2F): Platform Technologies for Hybrid Multi-Cloud
6 (F2F): Using a Container Platform

Part II: Data Platform and AI
7 (F2F): Data Platform Evolution and its Design
8 (F2F): The Data Analytics Cycle
9 (F2F): Unstructured Data and Deep Learning
10 (F2F): Using a Data Platform
11 (F2F): Generative AI Hands-On

Part III: Putting DX Skills into Practice
12 (on demand): Experiencing the Business Front #2 - Business Plan Presentation
13 (F2F): Making the Business Plan Concrete
14 (F2F): DX Solution Presentations #1
15 (F2F): DX Solution Presentations #2

Evaluation
Based on assignments (exercises and fieldwork results and reports) and a presentation in the final 2 classes, students will be graded as A+, A, B, C, D, or F, with a grade of D or above being a pass. Evaluation comments from multiple external parties will also be taken into consideration when grading.
Feedback for students
Will be done through comments made during the final presentation. In addition, evaluation comments from external parties (overall critiques only) will be fed back to the students.

Textbooks
Not specified.
Reference materials
David L. Rogers: The Digital Transformation Roadmap: Rebuild Your Organization for Continuous Change, Columbia Business School Pub

Office hours and How to contact teachers for questions
Questions will be accepted in the classroom during and after the class, and via email.
Message for students
This class places high priority on practical experience, so what you gain will depend greatly on yourself. Expecting you to be interested in social issues surrounding the digital society, participate proactively and enthusiastically, and work with people with different positions and opinions to update yourself.

Course by professor with work experience
Applicable
Work experience and relevance to the course content if applicable
Utilizing his technical background spanning from scientific computation to data science, he has experience in creating grand designs for enterprise AI and data analytics. Examples of social implementation include a demonstration experiment (2015-2016) in which machine learning and simulation were used in combination to control traffic lights and reduce traffic congestion, and a demonstration experiment (2016) in which car models were automatically identified using deep learning to display optimal outdoor advertisements. An example of a service being put into service is a major non-life insurance company's new car insurance product with a special clause for dashcams (2016-2017).

Teaching profession course
Informatics Program