Learn how to structure diverse data—including students’ grades, school records, and areas of interest—using the AI-SPARS tool, and leverage an LLM to design everything from major exploration and college/admissions strategies to personalized roadmaps. Rather than simple Q&A, you can immediately apply in practice a systematic AX-style career and college counseling process that progresses from Profile-Interview-Major-College-Admissions Track-Gap-Roadmap-Smart Report-Portfolio.

Middle school students will use the AI-SPARS tool to systematically analyze their interests and strengths, and explore customized careers, majors, and elective courses. The program is practice-oriented, ranging from AI in-depth interviews to career roadmap design, ultimately resulting in the completion of a personalized career portfolio.

This is a practical guide to AI-based career exploration for children in grades 4–6. Using the AI-SPARS algorithm, it precisely analyzes a child's interests, aptitudes, and values, teaching how to complete a customized career and academic portfolio prepared systematically from elementary school. Parents and teachers can directly implement a data-driven career design process that suggests a direction rather than a single fixed answer.

Design and create your own student-customized AI Tutors and AI Avatars using generative AI such as ChatGPT and Gemini along with Google Colab. From prompt engineering and data analysis to building an AI Career Exploration Platform for career and academic counseling, you will complete practical projects that can be immediately applied in the classroom.

Build a fully functional smart farm platform that integrates sensor data collection, MQTT communication, web dashboards, and AI-driven environmental analysis. Experience the entire process of agricultural digital transformation (DX) through a hands-on project, covering everything from IoT sensor integration and FastAPI/Spring-based API servers to React/Vue dashboard development and cloud deployment.

This is an advanced course where you will directly implement and interpret practical statistical techniques such as sampling distributions, hypothesis testing, regression analysis, and logistic regression using R. By utilizing AI tutors like ChatGPT and Gemini, you will learn everything from code generation to interpreting analysis results and writing reports, allowing you to take your data analysis capabilities to the next level.

Feeling overwhelmed by where to start with complex data analysis? I will clearly guide you through how to utilize R and AI Tutors using the practical know-how I've built up.

This course is structured to provide systematic learning, from basic database concepts to practical SQL applications, based on the Oracle Database 11g environment. Through step-by-step hands-on practice, you will master core content essential for database practice, including SELECT statements for data retrieval, DML for data manipulation, DDL for data definition, and user privilege management. Furthermore, this course goes beyond simple video lectures by providing an LLM-based AI Tutor, designed to allow learners to study by asking questions and receiving answers on their own. By utilizing the AI Tutor, the following types of learning are possible: - SQL syntax explanations and example generation - Analysis and correction of SQL error causes - Automatic generation of practice problems - Q&A on database concepts - Customized review based on learning progress - Generation of expected questions for certifications and job interviews Therefore, this course is suitable not only for beginners new to databases but also for professionals and job seekers looking to strengthen their practical SQL skills.

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.

Practical Hadoop & Hive Big Data with LLM Hadoop Ecosystem with AI Tutor This course focuses on understanding the Hadoop Ecosystem, a core technology of the Big Data era, and provides hands-on learning on how to process large-scale data using Distributed Storage (HDFS), Distributed Processing (MapReduce), and Data Warehousing (Hive). The curriculum is designed to help you master the core technologies of big data processing step-by-step, from Hadoop's basic structure and operating principles to HDFS, YARN, MapReduce, and Hive. In particular, you will build practical skills applicable in the field by performing tasks ranging from setting up a virtual machine-based practice environment to actual data processing and analysis. Furthermore, this course goes beyond simple video lectures by providing a self-directed learning environment utilizing an LLM-based AI Tutor. Students can maximize their learning efficiency through the AI Tutor, which provides Q&A for Hadoop and Hive concepts, error troubleshooting, practice problem generation, HiveQL writing support, and project learning guides.