[Renewal] First-time SQL and Database (MySQL) Bootcamp [From Beginner to Application]

This is a course for beginners to master the fundamental techniques of the entire Python data analysis process. As an e-commerce planner and developer currently applying data analysis techniques in the field, I have designed this course to help you easily learn and immediately apply the entire Python data analysis workflow.

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

Based on the instructor's own failed experiences when first learning machine learning, this is a newly designed course that differs from existing lectures, making it easy to understand machine learning and apply it to real-world problems

This course is Part 3 of the Full Stack lecture series, designed to help you solidly master the latest server technologies and Docker. The course is structured to enable you to practically incorporate Docker into your tech stack by meticulously testing Docker's various options, learning related technologies such as Linux that are necessary for understanding Docker, and building actual complex services.

Learn intermediate to advanced SQL and solve 101 real-world data analysis problems together to build solid SQL data analysis skills.

This is a newly designed course that allows you to gradually learn the mathematics, theory, PyTorch-based implementation, transfer learning, and GPT's core transformer needed to understand deep learning, based on the instructor's own failed experiences when first learning deep learning.

Learn NoSQL technology for handling big data, one of the fundamental skills in full-stack and data science technologies used by modern startups. MongoDB is the easiest and fastest NoSQL technology to utilize. In this course, you will learn the basics of MongoDB in a short time and master the skills to handle and utilize MongoDB with Python.

This is Part 1 of a roadmap lecture that concisely organizes the essential computer science (CS knowledge) needed to excel in development and data, in a short period. This lecture has also been used as an in-house lecture at top domestic companies for several years. Based on 20 years of experience in actual practice, from system SW to web, app development, and data science, this lecture systematically yet easily organizes the essential core knowledge needed to excel in development and data.

This is a full-stack series course aimed at developing web and app services at a commercially viable level for beginners, based on real-world experience developing web services in the field. As Part 2 of this series, this course thoroughly covers the latest HTML5, CSS3, and vanilla JavaScript + ES6, considering responsiveness and compatibility among the various frontend technologies available.

Based on real-world experience developing web services in the field, this is a full-stack series course aimed at helping beginners develop web and app services at a level suitable for actual commercialization. As the first step (part1-1) toward this goal, this course provides detailed explanations of intermediate Python, backend implementation techniques using Flask, and essential web technologies (HTTP, REST API, MVC pattern, etc.) for full-scale development.

This is a course where you can learn FastAPI, which is easy and fast for modern full-stack and backend development, starting from backend concepts step by step to FastAPI core syntax and asynchronous database usage, organized with clean materials and projects.

This course is a comprehensive lecture where you can learn Flutter step by step, from the basics of dart programming to basic and intermediate Flutter features, even integrating with Firebase backend, all the way to actual projects!

Learn NoSQL technology for handling big data, one of the fundamental skills in full-stack and data science technologies used by modern startups. MongoDB is the easiest and fastest NoSQL technology to utilize. In this course, you will learn the basics of MongoDB in a short time and master the skills to handle and utilize MongoDB with Python.

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!

Based on the instructor's own failed experiences when first learning machine learning, this is a newly designed course that differs from existing lectures, making it easy to understand machine learning and apply it to real-world problems

You will be able to extract the data you want directly without the help of a developer. We will comfortably cover everything needed for analysis, from simple queries to joins. Start learning SQL with a lecture directly from the author of the book <SQL for Product Managers>.

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

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 is a master class that systematically covers the fundamental principles and practical applications of MongoDB. Starting with core MongoDB concepts, you will systematically learn essential topics for practical work, such as database design, CRUD operations, indexing, aggregation pipelines, performance optimization, transactions, and security. It explains topics step-by-step so even beginners can understand, and you can deeply learn the working principles of MongoDB through exercises and examples. This is the best introductory and practical course for developers who want to build strong MongoDB skills for practical work.

SQLD, where do I even start? This course is for those who feel completely lost - you'll meet a transformed version of yourself after completing it. SQLD All-in-One Pass!

[Only for those new to web development] Spring Boot REST API: Building step-by-step while properly understanding JPA

Learn about Race Condition, a representative concurrency problem. Practice by comparing single server environments and distributed environments that cause concurrency issues. Understand transaction boundaries and isolation levels. Learn the differences between optimistic locking, pessimistic locking, and distributed locking.

[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.

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.

Finally, the Probability and Statistics course for everyone is here! Learn the principles of probability and statistics step by step through theory and coding practice. Reference book "Probability and Statistics for Everyone" https://wikidocs.net/book/18165

This course is a hands-on program for practicing the fundamentals of machine learning mathematics and statistics using Python. While machine learning and deep learning require programming skills, a mathematical background is necessary to understand the underlying principles. Through this course, you can step-by-step master the foundational mathematics required for artificial intelligence.

Now display Python results as web pages! You can create your own web page using only Python. This is an opportunity to create an Interactive Dashboard that allows you to check results created with Python, such as data analysis and machine learning, on a web page!

The Intermediate Python course is designed to deepen your understanding of Python by covering advanced topics such as functional programming, metaprogramming, exception handling, and object-oriented programming.

We guide non-majors and beginners to quickly obtain the Big Data Analysis Certification (Practical Exam)! Keep the theory light and the practice solid—focusing on core points that are guaranteed to appear on the exam through past questions, without the need for complex background knowledge.

By implementing various practical data analysis cases using SQL, you can simultaneously improve your data analysis and SQL utilization skills.

Perfect preparation for the SQLD certification, from understanding SQL to practical function application, finishing with the latest exam questions!