[Renewed] First-Time SQL and Database (MySQL) Bootcamp [From Beginner to Practical Applications]

This course teaches the fundamental skills needed to learn the entire Python data analysis process for beginners in data analysis. As an e-commerce planner and developer who actively uses data analysis techniques in the field, I designed it to help you easily learn the full Python data analysis process and apply it right away.

This course is Part 3 of the Full-Stack course series, designed to help you thoroughly master the latest server technologies and Docker. To enable you to genuinely incorporate Docker into your tech stack, the course meticulously tests Docker’s various options, covers related technologies such as Linux that you need to understand for Docker, and is structured so that you can even build complex real-world services.

For those who want to learn data science, big data, and web scraping, (1) we summarize the most essential Python syntax in a short time, and (2) help you become familiar with Python and web scraping through practical scraping programming.

This is Part 1 of a roadmap course that neatly organizes, in a short amount of time, the essential computer science (CS) knowledge you need to excel at development and data. This course has also been used for years as an in-house course at top companies in Korea. Based on 20 years of experience in the field, ranging from systems software to web and app development and data science, this course systematically and thoroughly covers the core concepts you need to excel at development and data—while keeping them easy to understand.

This course has been newly designed to help you gradually learn the mathematics and theory needed to understand deep learning, PyTorch-based implementation, transfer learning, and the core GPT Transformer, based on the instructor’s experience of struggling when first learning deep learning.

A full-stack series course aimed at beginners, based on real-world experience developing actual web services, with the goal of building web and app services to a level suitable for actual commercialization. As Part 2 of this series, this course thoroughly covers the latest HTML5, CSS3, and Vanilla JavaScript (Javascript) + ES6, including responsiveness and compatibility, among the many available frontend technologies.

This course covers everything from the basics of Dart programming to Flutter’s fundamental and intermediate features, and even integrating the backend Firebase to build a real-world project! It is a comprehensive course that lets you learn Flutter step by step.

This is a full-stack series aimed at beginners, with the goal of developing web and app services to a level that can be genuinely commercialized, based on real-world experience developing actual web services. As the first step (part1-1), this course provides an in-depth explanation of intermediate Python for full-scale development, backend implementation with Flask [플라스크], and essential web technologies such as HTTP, REST API, and the MVC pattern.

A FastAPI course for modern full-stack and backend development that is easy to use and delivers high performance. It covers backend concepts step by step, the core syntax of FastAPI, and asynchronous database usage, all organized clearly through well-structured materials and projects.

Learn NoSQL technology for handling big data, one of the fundamental technologies used in the full-stack and data science fields at modern startups. MongoDB is one of the easiest NoSQL technologies to learn and use quickly. In this course, you will learn the basics of MongoDB in a short time, as well as how to work with and utilize MongoDB using Python.

Master intermediate-to-advanced SQL and build solid SQL data analysis skills by solving 101 real-world data analysis problems together.

Based on the instructor’s experience of struggling when first learning machine learning, this course has been newly designed—unlike conventional courses—to help you understand machine learning easily and apply it to real-world problems.

Learn NoSQL technology for handling big data, one of the fundamental technologies used in the full-stack and data science fields at modern startups. MongoDB is one of the easiest NoSQL technologies to learn and use quickly. In this course, you will learn the basics of MongoDB in a short time, as well as how to work with and utilize MongoDB using Python.

I learned it the hard way, but I want you to learn it easily. I've discarded unnecessary theories and included only the core essentials of SQL. Build your SQL confidence quickly.

This course will solidify the basics of SQL through various problems that can be encountered in the actual IT industry. Solve the problem-solving quests, take the first step toward data analysis, and strengthen your SQL skills that can be applied in real life!

SQL data analysis! We will teach you how to solve practical problems that are applicable in the field, quickly and easily!

King Basic🐥, only for those who say they have no more words to describe me.👂 Concepts go to the universe🚀, King Basic practice day and night✍, learn SQL with your hands!✋ No installation required.🔥 Here is the lecture restaurant, Jecobe!🎉

This course aims to obtain the state-certified SQLD (SQL Developer) certification. An SQLD refers to a developer who can write accurate SQL to manipulate and extract data while developing application software based on knowledge of databases and data modeling.

Based on the instructor’s experience of struggling when first learning machine learning, this course has been newly designed—unlike conventional courses—to help you understand machine learning easily and apply it to real-world problems.

In this class, you will learn techniques for retrieving (crawling) and processing data from various operating sites using Python, and apply them directly to various examples that you may encounter in the field. Through this, you will learn techniques that focus on solving practical problems based on an understanding of the basic principles of how the web works, rather than one-dimensional task automation by copying and pasting given codes, and accordingly, you will be able to collect and process data from any site in the future. You will also learn how to resolve various exceptions that occur in this process with code, and ultimately, you will be able to solve and automate various problems that occur in daily life with programming thinking.

Starting from installing MariaDB and understanding its structure, you will learn step-by-step from writing SQL queries and utilizing functions to joins and subqueries. You will master core practical features such as table design, performance management, backup, and recovery through hands-on practice, completing the practical skills necessary for database development and operation.

This is a course for learning MS-SQL through baseball and corporate databases.

SQLD Yellow Book 176 Problem-Solving Course for those who studied but can't solve problems. After completion, you'll go beyond passing to become an expert. SQLD All-in-One Pass!

If you want to be recognized as a machine learning expert in large-scale data environments—from understanding the core framework of Spark Machine Learning to SQL-based data processing through high-difficulty practical problems, and the ability to implement optimized machine learning models through business domain analysis—join this course.

I'll show you everything about how to build servers for stateful services and how zero-downtime deployment works.

In an era where flexible data processing beyond the limits of SQL is required, learn everything about MongoDB. From NoSQL concepts to installation, CRUD practice, aggregation pipelines, text search, and indexing, you will learn step-by-step and complete your practical skills through Python crawling integration and container environment construction.

"SQLD is an exam where past questions are not officially released." Preparing for the upcoming 60th exam (2026.3.7)— Practical study material featuring 50 vividly reconstructed questions by subject and chapter, based on the latest 59th exam!

What do you learn in the 'Management Information Visualization Professional' certification course? This certification course focuses on developing the ability to analyze data effectively, visualize it for easy understanding, and communicate necessary information clearly and efficiently for important decision-making within companies or organizations. In simple terms, it is a process of learning the skills to not just look at data, but to uncover hidden meanings within it and express them through charts, tables, and graphs so they can be easily explained to others. Through this course, you will: Develop the power of strategic thinking through data, build practical skills that can be applied immediately to real-world tasks, and enhance your professional expertise and competitiveness. Through this lecture, cultivate the ability to connect management with data and take a step forward as an expert who understands and explains data.

[Systematization of Engineering Intuition and Intelligent Problem-Solving Methodology Roadmap] 1. Introduction: Shift in the Problem-Solving Paradigm (The Birth of the Problem Architect) The complexity of modern industry has reached a level that exceeds the cognitive capacity of individual engineers, clearly exposing the limitations of "troubleshooting," which is a simple reactive repair approach. To derive meaningful solutions in manufacturing and R&D sites where tens of thousands of variables are intertwined, it is essential to evolve into a "Problem Architect" who goes beyond solving occurring problems to eliminating the problem-generating structure itself at the design stage. This masterclass combines 40 years of engineering intuition with cutting-edge artificial intelligence technology to present specific intelligent problem-solving techniques that transform uncertainty into engineering necessity. 2. Step-by-Step Intelligent Problem-Solving Techniques (The Methodology) ① Data-Driven Causal Identification Technique: DMAIC 4.0 The traditional Six Sigma methodology—Define, Measure, Analyze, Improve, and Control—is advanced by combining it with the computational power of modern AI. Selection of Key Process Input Variables (KPIV): AI algorithms are utilized to extract key factors with actual causal relationships, rather than mere correlations, from among thousands of variables. Securing Statistical Consistency: We establish an analytical process that proves capabilities with a Process Capability Index (Cpk) of 1.33 or higher through data, realizing "zero defects through statistical necessity" rather than "accidental good products." ② Virtual Verification and Lead Time Reduction Technique: Zero-Trial Strategy To minimize physical trial and error, we introduce verification techniques in a Digital Twin environment, which perfectly replicates real-world processes in a digital space. Cost-Free Limit Testing: Optimal process conditions are derived by performing tens of thousands of simulations in a virtual environment without interrupting actual production lines or destroying prototypes. Multi-Agent System-based RCA: We apply a "time leverage" technique that innovatively shortens the lead time required for Root Cause Analysis (RCA) by deploying multiple AI agents that simultaneously perform search, reasoning, and cross-verification. ③ Logical Thinking and Knowledge Assetization Technique: Pyramid Narrative and Knowledge Graphs This is a knowledge structuring technique that ensures field problem-solving experiences do not remain in individual memories but spread as the intelligence of the entire organization. Pyramid Principle and SCQA Framework: By combining Barbara Minto's Pyramid Principle with the Situation, Complication, Question, and Answer (SCQA) structure, complex technical issues are reconstructed into logical narratives that management can immediately accept. Building Knowledge Graphs: Fragmented technical reports and drawing data are converted into a graph structure capable of real-time reasoning. Through this, an organizational learning system is established where problem-solving cases from a specific point are immediately propagated (Yokoten) to production bases worldwide. ④ Transparency-Based Decision Support Technique: Explainable AI (XAI) This technique involves verifying AI outputs based on engineering logic rather than blindly accepting them. Chain of Thought Visualization: By transparently disclosing (Glass Box) the step-by-step logical development process the AI went through to reach its final conclusion, the basis for decision-making is secured. Human-in-the-Loop Governance: AI serves as a Co-pilot suggesting optimal alternatives, while the system is designed so that the final trigger is approved by human engineering insight and ethical judgment. 3. Human-Centered Cognitive Sovereignty Acquisition Techniques As the automation rate of systems increases, active response techniques are required to prevent the decline of the cognitive capabilities of the humans operating them. Sandwich Workflow: Instead of delegating the entire work process to AI, a structural work technique is applied where humans preemptively occupy context design (Top Bun) and final value judgment (Bottom Bun) to prevent cognitive paralysis. RQTDW Learning Protocol: By mandating five stages—Read, Question, Think (confronting contradictions), Discuss (virtual debate), and Write—into the practical process, we reject simple acceptance of information and actively internalize knowledge into the brain. Intentional Cognitive Friction: To maintain a critical distance from the overly smooth answers provided by AI, an adversarial verification process using a Critique Agent is conducted. 4. Practical Application: Super-Gap Leadership for "Designing Out" Problems The ultimate maturity of problem-solving lies not in solving problems well after they occur, but in "Designing Out" the system structure so that problems cannot occur. Data Infrastructure and Autonomous Operation Design: We block the possibility of human error by building an autonomous operation system that ranges from real-time data collection to feedback loop-based correction. Organizational Intelligence: By converting individual expert know-how into standardized algorithms and knowledge graphs, we secure an upwardly standardized problem-solving capability for the entire organization. 5. Conclusion: Future Competitiveness Completed by Engineering Rigor While intelligence is manifested through AI technology, the vessel that contains that intelligence and makes it operate according to its purpose is only a rigorous and sophisticated engineering problem-solving methodology. This masterclass will move away from technology adoption that relies on luck or probability and present a path to becoming a "True AI Architect" who perfectly commands systems with data and logic. We hope you establish your status as a super-intelligent helmsman who dominates technology and eliminates problems.

Leveraging Python's basic syntax and popular libraries Auto-generating Python syntax and library code using ChatGPT

Channel Talk interviewers will directly analyze the difference between failing and passing answers to the same questions, and help you prepare for actual follow-up questions.

You will learn how to analyze and quickly visualize various types of data using the Python Streamlit library and output it as a web service. You can use it when you want to apply it to your work faster than the existing web development method.