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Program Overview

The program is a customized, in-house practice program designed to address the challenges of applying data science and AI in real-world business settings. It is based on the educational program provided by Nagoya University, which has been successfully implemented with over 300 participants and a satisfaction rate of 95% or higher.

Key Features

• Customized Curriculum: The program is tailored to the company's specific challenges and data, ensuring that the learning is directly applicable to the business.
• Practical Experience: Participants work in teams to analyze and propose solutions to real-world problems, developing practical skills and thinking.
• Support System: The program includes support from Nagoya University teachers and QTA (Certified Teaching Assistants), providing guidance on analysis methods, tool operation, and presentation skills.
• Certificate of Completion: Participants receive a certificate of completion, which can be used as evidence of their education and training.

Program Structure

The program consists of two phases: the learning phase and the practice phase.

Learning Phase

• Foundational Subjects: Data science basics, including statistics, probability, and data literacy.
• Tool Utilization: Practical use of Python, NumPy, Pandas, and other tools for data processing and visualization.
• Machine Learning and AI: Understanding and applying machine learning algorithms and AI models.
• Practical Knowledge: Applying data science to real-world problems, including data ethics and legal considerations.
• Project Management: Teamwork, communication, and project management skills for data science projects.

Practice Phase

• Problem Setting: Identifying and defining real-world problems within the company.
• Data Understanding and Exploration: Analyzing and exploring company data to understand trends and patterns.
• Analysis and Modeling: Applying machine learning and statistical models to solve defined problems.
• Proposal and Presentation: Developing and presenting proposals based on analysis results.
• Result Presentation: Final presentation of results and discussion of implementation possibilities.

Support System

• Advisor Teachers: Provide guidance on analysis methods and project direction.
• QTA (Certified Teaching Assistants): Offer technical support on tool operation and data analysis.
• Company Representatives: Provide background information on company challenges and support the project's progress.

Implementation Format

• Implementation Period: Typically 3 months, with weekly sessions.
• Team Composition: 3-6 members per team, with the possibility of multiple teams.
• Implementation Style: Online, face-to-face, or hybrid, depending on the company's preferences.

Program Outcomes

• Practical Reports and Proposals: Teams produce reports and proposals that can be directly applied to business challenges.
• Teamwork and Presentation Skills: Participants develop teamwork, analysis, and presentation skills.
• Potential for Business Improvement: Results can lead to actual business improvements or new project initiatives.

Schedule and Fees

• Basic Fee: Includes lecture and practice fees.
• Customization Fee: For tailoring the program to the company's specific challenges.
• Option Fees: For additional services such as team expansion or extension of the implementation period.

FAQ

• Suitable Industries: Any industry with real data can participate, including manufacturing, logistics, and healthcare.
• Data Analysis Skills: Not required, as the program includes lectures and support for beginners.
• Schedule Flexibility: Implementation schedules can be adjusted according to the company's situation.
• Data Quantity: Even small datasets can be used for the program, focusing on analysis and proposal development.
• Confidentiality: Company data is kept confidential and used only within the company.

Contact and Further Information

For detailed information, including fees and implementation schedules, please consult the provided contact information or visit the official website.