2026Year

Second Semester

DX solutions

Lecture

DX solutions

Z0400017 DX solutions

GUNJI Shigeki

2.0Credits

Mon.4Period

Shinjuku Campus

B-0753DX実践ラボ

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%

1. Visionary Design: Business Architecture Engineering
　・Strategic Roadmap Formulation: Ability to deconstruct existing business processes and independently formulate comprehensive DX roadmaps.
　・Executive Communication: Capability to articulate technical selection rationales to management based on quantitative ROI (Return on Investment) projections.

2. Change Agent: Leading Agile Organizational Culture
　・Dual-Track Leadership: Mastery of the team leader role, effectively balancing the two worlds of engineering and project management.
　・Change Management: Ability to propose and implement continuous improvement cycles facilitated by container-based platforms.

3. Implementation Mastery: Building Scalable Infrastructure
　・Container Professionals: Practical skills in standardized development environments for portability and reproducibility using Docker/Podman.
　・Resilient Systems Design: Ability to design and construct robust infrastructure based on the principles of microservices and Kubernetes.

4. Analytical Integration: Data Governance and AI Synergy
　・E2E Data Architecture: Design end-to-end data pipelines that guarantee data quality (preventing GIGO) and robust security.
　・System Orchestration: Build business automation frameworks by integrating AI agents and LLMs with existing enterprise systems through RAG and function calling.

5. Execution & Impact: Social Implementation and Validation
　・Hypothesis Validation: Ability to execute iterative validation cycles to test your original ideas against real-world operational challenges.
　・Strategic Presentation: Pitching "2030 Solutions" to engage diverse stakeholders and achieve professional consensus.

1. Linear Algebra basics
2. Python basics
3. Linux basics
4. Highest self-motivation

Project Based Learning／Discussion Debate／Group Work／Presentation／Practice Fieldwork

Format Overview:
　Lectures: hybrid (f2f & online)
　Workshop, Hands-Ons & Presentations: f2f
　Fieldwork: details to be announced in class.

　[!IMPORTANT]
　Agile Course Management: To ensure seamless coordination with external industry partners, this course adopts an "Agile" management approach. Please note that the schedule is subject to flexible adjustments.

Introduction: Updating Your Perspective
　01(hybrid): Overview of DX Solutions
　02(tba): Business Frontline Experience I - Identifying field-specific pain points and understanding business design.

Part I: Container Infrastructure & Innovation
　03(hybrid): Agile Culture, Change Management, and Technical Debt Elimination.
　04(f2f): Agile (Scrum) Workshop
　05(f2f): Container Hands-On using Docker/Podman.
　06(f2f): Advanced Container Hands-On with Kubernetes.

Part II: Data Infrastructure & AI
　07(hybrid): Evolution of Data Platforms
　08(f2f): Data Engineering Hands-On
　09(hybrid): Structural Understanding of Deep Learning
　10(f2f): Business Applications of LLMs Hands-On
　11(f2f): AI Agents & LLMOps Hands-On

Part III: Practicing Your DX Leadership Skills
　12(on demand): Business Frontline Experience II - Validating hypotheses for your ongoing solution plan.
　13(hybrid): Summary and Commentary - Connect the dots.
　14(f2f): DX Solution Pitch I - Presenting 2030 solutions (Peer & Faculty evaluation).
　15(f2f): DX Solution Pitch II - Guest Judges, Awards, and Networking.

Grading criteria evaluated by attendance point, assignments (results/reports from exercises and a fieldwork) and pitches 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.

Will be done through comments made during the final pitch. In addition, evaluation comments from external parties (overall critiques only) will be fed back to the students.

Not specified.

David L. Rogers: The Digital Transformation Roadmap: Rebuild Your Organization for Continuous Change, Columbia Business School Pub

Questions will be accepted in the classroom during and after the class, and via email (shige.gunji@cc.kogakuin.ac.jp).

This course is not about the passive "input of knowledge" within the safety of a classroom. It is an experimental lab—a place where you define your own questions, get your hands dirty with real-world data, and implement solutions for the complex challenges of our digital society.

To succeed here, we ask you to embrace three core principles:

1. Own Your Growth: "You get out what you put in"
We provide the arena—hands-on workshops, fieldwork, and high-stakes pitches to industry judges. In this environment, a passive attitude will leave you empty-handed. However, if you dive in with initiative, the experience will transform into a lifelong competitive edge.

2. Innovation through Collision: "The power of mixing"
Digital Transformation (DX) is never a solo mission. You are expected to collaborate with peers whose backgrounds, expertise, and opinions differ from your own. Do not shy away from friction; it is through the process of clashing and complementing each other that you will find the keys to updating your own perspective.

3. Claim Your Stake in 2030: "Be the driver, not the passenger"
As traditional boundaries dissolve, what kind of world do you want to build? We want students who are deeply curious about social issues and possess the hunger to rewrite the future with their own hands.

It’s time to transform yourself and join us in repainting the scenery of 2030.

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).

Informatics Program