SQLD, where should I start? For those of you feeling completely overwhelmed, this is the lecture for you. You will meet a transformed version of yourself after completing the course. SQLD All-in-One Pass!

This course covers how to use queries to build dashboards in Logpresso. We will discuss how to visualize real-time data, trigger alerts, and dynamically drill down and filter as needed.

"Big Data + AI Full Stack Development" - The process of building big data that breathes life into AI is carried out in the form of a pilot project. We identify requirements, configure the platform step by step, and perform smart car data exploration and analysis.

Through this course, you will be able to immediately carry out corporate Apache Spark projects.

We introduce methods for planners and marketers without data analysis experience or technical skills to try data analysis at the most basic level, complete with various examples. Built on years of hands-on practice with over 2,000 participants in more than 100 companies and public institutions, the content is designed around the most practical analysis methods for data non-experts.

SSIS, SSAS, MachineLearning, BI, ETL. You can learn important technologies that cannot be found in domestic books, YouTube, lectures, blogs, and academies. I also recommend it to those who are interested in employment at domestic large companies, American large companies, and American state funding agencies.

You can learn the core essentials of GA4 (Google Analytics 4) e-commerce setup—which is often too difficult to learn alone just by searching the internet—through hands-on practice. This course is designed so that performance marketers and beginner developers setting up GA4 e-commerce for the first time can easily understand it by practicing step-by-step on a Cafe24 demo shopping mall. Since we delegate the developer's role to ChatGPT during the practice sessions, even marketers, PMs, or planners without professional development knowledge can learn how to set up GA4 e-commerce.

This is a special lecture that can lay the theoretical foundation to simultaneously prepare for the Microsoft AZ-900 certification, and as the latest content reflecting the scope of questions as of May 2025, by providing content related to core data concepts, Azure's relational data, Azure's non-relational data, and Azure's analytical workloads in a form combining theory and practice, it can be utilized as a meaningful educational opportunity to not only obtain the certification but also take the first step towards becoming a data professional.

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!

We’ll guide non-majors and beginners to quickly earn the Big Data Analysis Engineer practical certification! Keep the theory light and the practical training solid. Even without complex background knowledge, you’ll focus on past exam questions and study only the key points that are sure to appear on the test.

This is a detailed and practical course for beginners to Google Analytics. Rather than covering isolated features, it guides you through scenario-based analyses so you can learn the practical process of conducting analysis with GA4. It is continuously updated to reflect the latest changes to GA4!

Become a pioneer in the latest large language model (LLM) technology through Langchain online lectures! This course provides practical skills and innovative knowledge that will upgrade your career. #LangChain #LangGraph #LangSmith #MCP

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.

Using the fictional e-commerce company Aurora Market as an example, build a Snowflake-based data warehouse from start to finish. Gain practical experience across the entire process—from loading raw data through staging and core transformations, fact and dimension modeling, incremental ELT pipelines, and data quality validation to delivering data marts, optimizing performance and costs, and developing operational and recovery strategies—while acquiring the skills to design and operate an enterprise-grade data warehouse.

It covers basic data concepts, useful R functions and packages, and data analysis exercises, to allow anyone to perform big data analysis using R programming.

[Engineering Organizational Strategy and Individual Competency Roadmap through 'The Great Rewiring'] 1. Introduction: 'The Great Rewiring' and the Shift in Organizational Paradigms Modern enterprises are facing an unprecedented technological turning point known as 'The Great Rewiring,' the initial phase of Artificial Intelligence (AI) adoption. This is defined as a complex task that goes beyond the simple introduction of unit technologies or partial task automation; it requires a fundamental reconfiguration of the structural blueprint of the organization as a massive system. Despite the supply of high-efficiency power sources in the form of Generative AI, many organizations are experiencing performance degradation and system instability due to structural inertia. Analysis suggests this phenomenon occurs because, while the power output has been strengthened, the redesign of processes and structures to control that energy and convert it into meaningful business outcomes has not followed. This course aims to present an in-depth architectural design strategy to evolve organizations into sophisticated organic systems. 2. [Diagnosis] Analysis of Three Major Structural Defects in the AI Adoption Phase ① Chassis Collapse (Lack of Rigidity in Organizational Substructure) When a high-performance power source like AI is installed while maintaining a rigid vertical hierarchy, the existing structure fails to accommodate the accelerated information throughput and decision-making speed. This is an organizational dysfunction caused by a decision-making system that cannot keep pace with technological deployment, which is highly likely to lead to the physical collapse of leadership authority and management systems. ② Jagged Frontier (Misjudgment of Performance Boundaries and Reduced System Reliability) This problem arises from indiscriminately deploying Generative AI—a probabilistic reasoning mechanism—to tasks that require strict deterministic logic. Overlooking the probabilistic nature of AI in areas where mathematical precision or legal compliance is essential leads to 'System Knocking,' where overall system reliability drops sharply, causing significant tangible and intangible asset losses to the organization. ③ NVH: Noise, Vibration, Harshness (Neglect of Cognitive Friction and Psychological Instability) Just as mechanical vibration and noise increase system fatigue, widespread job insecurity and ambiguous job guidelines within an organization accelerate the cognitive load of members to a critical point. Organizations that fail to properly control this psychological NVH (Noise, Vibration, Harshness) may face a crisis of internal self-destruction due to discord between components, despite the introduction of intelligent systems. 3. [Individual Competency] Evolution from Passive Compliance to Sovereign Architect Individual members in the AI era must move away from the status of 'Passive Sheep' dependent on technology and be reborn as 'Existential Architects' who deconstruct and rewire systems. ① Recovery of Intellectual Sovereignty and Escaping Slave Morality Uncritically accepting AI outputs and entirely delegating analytical processes to machines leads to 'Cognitive Offloading,' which eventually causes the degeneration of the Executive Control Network. One must reject the position of the 'Good Sheep' who settles for technological convenience and awaken as a 'Sovereign' subject capable of expressing critical anger toward system absurdities and technical debt. ② Designing 'Intentional Friction' for Cognitive Plasticity Individual members must resist the seamless answers provided by AI and design intentional 'Cognitive Friction' into their work processes. By utilizing AI not as a simple answer generator but as an adversarial partner that stimulates and deepens human thought, one must maintain brain neuroplasticity and strengthen intellectual muscle. ③ AI Command Capability: S.E.E.D Prompt Architecture Beyond simple queries, the ability to design logical interfaces that AI can process is essential. S.E.E.D Framework: Cultivate the capability as a 'Director' to precisely control AI by systematically structuring Situation, Expectation, Engineering Structure, and Data. 4. [Methodology] Organizational Innovation Strategy through Building a Cognitive Powertrain ① Cognitive Powertrain (Dual Engine Architecture Design) Optimize the system by clearly decoupling the organization's cognitive processes into predictive and generative models. Predictive AI: Ensures system stability by taking full charge of precise logical systems and quantitative analysis tasks. Generative AI: Provides innovative momentum by handling creative synthesis and context generation tasks. ② Golden Pattern (Intelligent Collaboration Protocol based on Reliability Engineering) Systematize the collaboration process between humans and AI to control Hallucination risks. Serial Process Optimization: Establish standard operating procedures consisting of Generative AI information processing, human logical filtering, and re-optimized output. Human-Centric Gatekeeper Capability: Maintain technical alignment by ensuring humans hold the sovereign position to direct the system's orientation and perform final decision-making. ③ Application of Behavioral Software Engineering An engineering approach is needed where both leaders and members can actively mitigate emotional resistance and cognitive load. Strategic Design of Ethical Latency: Insert intentional review stages to ensure the race for technological adoption does not lead to ethical bankruptcy. Transparent Feedback Loops: Maximize organizational transparency by embedding feedback mechanisms to minimize mutual trust costs. 5. Conclusion: Securing Future Competitiveness through Sovereign Architecture This masterclass avoids abstract discourse and translates 40 years of engineering insight gained from tuning massive systems into the business language of the AI era. Will you remain a 'Good Sheep' slowly degenerating while submerged in structural inertia and technological convenience, or will you become an 'Existential Architect' who sees through the illusions of the system and proactively rewires it? We will help you redesign the architecture of your organization and yourself through a precise cognitive partnership, enabling you to fully control the powerful engine of AI and drive sustainable growth.

[Understanding AI Foundation Models and Their Operating Principles: Engineering Control and System Architecture, Practical Methodologies for Resolving AI Uncertainty and Engineering Assetization] 1. Introduction: The Necessity of Engineering Control of Intelligence (Engineering Control vs. Systemic Chaos) A core conclusion derived from long-term practical insights in industrial fields is that power that is not properly controlled acts as a potential liability rather than an asset. Even a high-performance engine is nothing more than an unstable physical mass if it lacks sophisticated combustion logic and microsecond-level control systems. The organizational chaos currently appearing in the process of adopting Generative AI is judged to stem from a lack of understanding of these control principles and a blind faith in technical "black boxes." This masterclass redefines Artificial Intelligence not as a mysterious stochastic phenomenon, but from the perspective of Model-Based Engineering (MBE). By transforming the uncertain domain of intelligence into a predictable and reliable engineering framework, we aim to present a strategic methodology that allows organizations to secure strong leadership across the entire system without being dependent on technical trends. 2. The 4 Pillars for Solving Core Challenges ① Epistemological Paradigm Shift: Visualizing the Black Box and Assetizing Technical Debt Many companies are facing "technical debt"—characterized by exposure to security vulnerabilities and exponential increases in maintenance costs—by adopting AI models without a clear understanding of their internal structures. This course assetizes this through the following approaches: Deconstruction of Mechanisms: We engineeringly deconstruct the Self-Attention mechanism, the core of the Transformer architecture, from the perspective of numerical weight analysis. By understanding the numerical mechanisms that determine information priority, we visualize the basis for the model's judgment. Analysis of ID Formation: We transparently track the process by which a series of pipelines—leading from Pre-training to Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF)—forms the model's technical identity and ethical guidelines. This converts invisible threats into controllable system parameters. ② Securing Deterministic Reliability: Hallucination Control Strategies to Overcome Probabilistic Limits Large Language Models (LLMs) are not systems that reason for truth, but systems that generate the next most probable token. The "hallucination" phenomenon resulting from this inherent characteristic becomes a fatal flaw in engineering fields where reliability is vital. Constraints of Retrieval-Augmented Generation (RAG): We move away from closed structures that rely solely on the model's internal fixed memory (Internal Weights). We establish an "open-book strategy" that provides clear grounding for generated results by allowing real-time reference to trusted external knowledge bases. Hybrid Model Architecture: We design a redundancy strategy that achieves both accuracy and operational efficiency by deploying large models for areas requiring enterprise-wide knowledge and optimized Small Language Models (SLMs) for specific domains where security and real-time response are essential. ③ Computing Architecture Optimization: Overcoming Physical Bottlenecks (Memory Wall) While intelligence is implemented in software, its performance and economic sustainability are defined by the physical limits of hardware. Physical Constraint Analysis: We diagnose the "Memory Wall" problem, where data transfer speeds cannot keep up with the processing speeds of computing units, and heat generation issues resulting from high-density computation from an engineering perspective. Infrastructure Design Capability: We precisely analyze the physical impact of High Bandwidth Memory (HBM) stacking structures and 2.5D/3D advanced packaging technologies on inference efficiency. We cultivate design capabilities to optimize Total Cost of Ownership (TCO) through full-stack integrated insights that complement hardware limitations with software architecture. ④ Acceleration of Functional Expansion: Transitioning from Passive Tools to Autonomous Agent Systems Current AI remains at the level of simple Q&A, failing to create added value for practical business automation. This course evolves AI into an active subject that judges and executes on its own. Decomposition: We learn techniques for decomposing complex goals into achievable sub-tasks and logically organizing the execution sequence upon receiving them. Digital Workforce Deployment: We define the process of applying "Active Agent" systems to the field, which autonomously call internal ERPs, browsers, and external APIs to complete actual business logic and accept feedback on results. 3. Core Architecture: Closed-loop Control System The way an AI agent manifests intelligence and performs complex tasks is theoretically identical in logical structure to the closed-loop control system performed by an ECU (Electronic Control Unit), the core brain of a car. This course analyzes this in detail from the perspective of the ReAct (Reasoning and Acting) framework. First, the system begins at the Input stage, receiving ambiguous and complex requests from the user. This plays the same role as a sensor in control engineering collecting physical data from the external environment and delivering it to the system, serving as the standard for defining the initial state of the task at hand. Second, based on the received data, the Thought stage proceeds, where plans are established through logical reasoning within the LLM architecture. This is in line with the process where control algorithms in an ECU calculate optimal control values by processing input sensor data. At this stage, the agent sets the optimal path to achieve the goal and secures the logical rigor of the system. Third, the Action stage follows, where tasks are completed by calling external tools or APIs according to the established plan. This logically matches the mechanism where the calculation results of a control system are converted into physical power through an actuator to execute commands. Through this, intelligence exerts actual physical and digital influence beyond abstraction. Finally, the Observation stage is performed, analyzing the execution results and correcting errors relative to the initial goal. This is identical to the core principle of control engineering, which reduces system deviation through a feedback loop. The agent self-verifies whether the execution results meet the goal and continuously upgrades performance by reflecting occurred errors into the next action plan. AI equipped with such a closed-loop structure is no longer an incomplete system dependent on probability. By securing engineering rigor that self-verifies execution results and corrects errors, it functions as a trust-based partner capable of performing business-critical tasks. 4. Practical Application and Expansion: Software-Defined Vehicles (SDV) and Physical AI The final destination of AI architecture lies in the cross-industry proliferation of Software-Defined Vehicles (SDV) and Physical AI, which overcome and evolve physical constraints through software intelligence. This is the standard model for future System Integration (SI) across manufacturing and service industries. Securing Edge Intelligence and Data Sovereignty: Small models (SLMs) mounted on-device in vehicles or facilities learn real-time field data immediately. This minimizes cloud dependency, perfectly protecting data sovereignty—a core asset of the company—and enables precision services based on ultra-low latency. Hardware Optimization and Lightweight Engineering: To implement the best intelligence within limited power and computational resources, we actively introduce model compression technologies such as Quantization, Pruning, and Knowledge Distillation. Model deployment considering hardware bandwidth becomes a core competency that determines system response speed and user experience. Hybrid Orchestration: We design an integrated architecture that organically connects "Cloud LLMs" possessing broad general knowledge with "Edge SLMs" specialized for specific physical control and security. Integration from a full-stack perspective, penetrating from silicon chipsets to software stacks, provides a powerful competitive advantage that evolves the entire system through software updates alone. 5. Conclusion: The Role and Vision of the AI Architect The ultimate goal of this masterclass is to elevate students from the position of a "User" who passively relies on technology and hopes for luck, to a professional "AI Architect" who perfectly controls and tunes everything from the physical limits of the system to the depths of the software architecture. While the phenomenon of intelligence manifests from software logic, it is silicon (hardware) that defines the physical limits of that intelligence, and only sophisticated engineering can overcome those limits to complete actual business value. "Intelligence may reside in the realm of probability, but the vessel that contains that intelligence and makes it operate according to purpose must belong solely to the realm of rigorous and sophisticated engineering."

Revolutionize your stock investing with AI! In this course, you’ll build a fully functional AI automated trading system that targets the top 25 Nasdaq stocks. Gain experience and know-how through paper trading with the Korea Investment & Securities API, and take advantage of this special opportunity to conduct AI-powered automated stock trading with a real account. Even if you’re a complete beginner, there’s no problem. We’ll explain the basic concepts of stocks and why you should invest in U.S. stocks in an easy-to-understand way, and provide all source code along with remote support. From now on, investing is essential for the next 60 years of our lives. Build your own AI investment partner to accompany you!

I have packed this with everything I want to tell junior PMs/POs who are struggling due to a lack of good mentors. I will generously share various techniques and mental models used by high-performing PMs/POs.

Learn various features of Looker Studio (formerly Google Data Studio) using GA4 demo data. This includes explanations and hands-on practice for Looker Studio's visualization elements. In particular, marketers or planners who are not familiar with SQL can learn easy ways to connect and use BigQuery data with Looker Studio. You can learn methods to create dashboards using Looker Studio.