SQL data analysis learned through various cases

Through detailed lectures and hands-on practice on the core elements of SQL, we will provide you with a solid foundation to grow into a SQL analysis expert.

We will help you easily understand the core concepts of machine learning and acquire the ability to implement practical machine learning applications by moving away from theory-based machine learning courses.

This course will help you grow into a deep learning-based computer vision expert needed in the field through in-depth theoretical explanations of Object Detection and Segmentation, along with practical examples at a level that can be immediately applied in the industry.

This course is a practical Airflow master program designed to help you understand "why Airflow works this way" and enable you to design and debug data pipelines on your own. It covers everything from Apache Airflow's core mechanisms to detailed theory and hands-on practice involving DAGs, Operators, Hooks, Scheduling, Timezones, Idempotency, and Templates.

This course is designed to help you learn the core functions of FastAPI, as well as the entire process of web service development. Through this course, we will help you become a FastAPI expert developer needed in the field.

Through in-depth theoretical explanations of Kafka Connect and detailed practical training that can be used immediately in the field, we will help you grow into an expert in building data linkage and data pipelines based on Kafka Connect that are needed in the field.

From the core of Kafka to in-depth content on internal mechanisms, the course is structured so that even beginners can quickly reach the expert level through detailed theoretical explanations, hands-on practice, and practical Kafka application development practice.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

From core theories of Deep Learning and CNN to implementation methods of various CNN models, and practical Deep Learning development know-how through real-world problems, If you want to become a Deep Learning CNN technology expert, join us in this lecture :)

From Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder based MarianMT translation model, and even Vision Transformer, you'll learn Transformer inside and out by implementing them directly in code.

This course is designed to help you learn the use of ksqlDB and its core mechanisms through various hands-on exercises. After completing the course, you will be able to easily and quickly build a real-time streaming data analysis system based on Kafka.

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.

It provides a key guide to understanding the internal mechanisms of Oracle DB architecture and growing as a performance tuning and performance analysis expert.

This course is designed to upgrade your skills as a practical machine learning development expert by implementing the machine learning model of the Home Credit Default Risk competition on Kaggle.

Through detailed lectures and hands-on practice on the core elements of SQL, we will provide you with a solid foundation to grow into a SQL analysis expert.

We will directly extract indicators frequently used in marketing/planning/CRM using SQL.

I have summarized the essential SQL SELECT statements and basic usage of Google Sheets that I learned while working as an analyst at a service company.

We conduct app log analysis using data from Google Analytics 4 and Firebase. It includes useful content for practical use, such as arrays, window functions, funnels, retention, Google Sheets, and more.

A DBA instructor with 30 years of experience teaches 45 essential SQL tuning techniques that can be immediately applied in practice through real-world examples. Master the practical know-how to make slow queries 10 times faster, from execution plan analysis to index optimization and join tuning!

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.

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.

Is it enough to just search for past exam questions and pass the Google Analytics Individual Qualification (GAIQ)? A certification holds little meaning if you obtain it without knowing how GA4 is actually used in practice or how to query and utilize data. This course focuses on practical application, allowing you to directly examine reports and GA demo accounts, and even set up a demo shopping mall yourself. You will build the skills necessary for the field while solving the latest 2026 GAIQ exam questions.

This is an introductory BigQuery (SQL) course for beginners. I recommend it to those who are curious about how to process data. I will teach SQL from the perspective of data analysis and data engineering, rather than from a backend development perspective.

[Strategic Roadmap for System Control and Prevention of Cognitive Atrophy in the AI Era] 1. Introduction: Technical Initiative and Strategic Command (Strategic Command vs. Passive Dependence) A core insight derived from the past 40 years of automotive R&D and corporate management is that entities that lose technical control are highly likely to devolve from beneficiaries of a system into its dependents. In particular, the proliferation of Artificial Intelligence (AI) technology—comparable to high-performance engines—presents a crossroads: it will either leave humans as "passive passengers" of technology or allow them to leap forward as "strategic commanders" who master the system. The indiscriminate dependence on AI observed today is accelerating "Cognitive Offloading," a phenomenon where humans entirely delegate their inherent thinking and analytical mechanisms to machines. This induces the deactivation of the brain's Executive Control Network (ECN) and carries the risk of a structural crisis known as "Cognitive Atrophy," leading to the functional decline of the frontal lobe in the long term. This course aims to present a strategic methodology to strengthen human cognitive capabilities and safeguard intellectual sovereignty in response to this intellectual crisis. 2. Five Core Methodologies for Safeguarding Cognitive Sovereignty ① Maintaining Cognitive Plasticity and Designing Intentional Cognitive Load (Cognitive Gym) The convenience of AI, which provides immediate and seamless answers, can lead to a disconnection in thinking and the omission of critical review processes. To prevent this, it is necessary to design intentional "Cognitive Friction" within work processes. By counter-utilizing AI's automation functions to forcibly delay and deepen the human thinking process, advanced training must be conducted to stimulate neuroplasticity and raise the threshold of thought. ② Establishing an Adversarial Verification System Based on Multi-Agent Systems (MAS) Human cognitive systems are susceptible to "Automation Bias," the tendency to uncritically accept AI outputs. To offset this bias, it is effective to operate a "Critique Agent" or a virtual "Red Team" that analyzes and attacks the vulnerabilities of the logic, separate from the primary model that follows the user's instructions. This forces the process of building defensive logic, thereby activating "System 2 (deliberative thinking)" as defined by Daniel Kahneman. ③ Implementation of Literacy-Based Dual-Track and RQTDW Learning Methods The ability to verify digital tools is directly proportional to one's analog foundational thinking system. While maintaining literacy to deeply grasp the context of text, it is recommended to internalize the 5-step RQTDW roadmap: Read (Deep Reading): Perform a multi-faceted identification of the source of information. Question (Questioning): Pose critical questions regarding logical consistency and the validity of premises. Think (Confronting Contradictions): Analyze and contemplate trade-offs and logical gaps between information. Discuss (In-depth Discussion): Diversify points of contention through virtual or actual discussions. Write (Reconstruction): Refine the results of expanded thinking into human-specific language for systemic internalization. ④ Applying the Sandwich Workflow to Clarify Accountability Delegating the entire work process to AI poses a high risk of cognitive paralysis; therefore, it is essential to establish a rigorous workflow that structurally separates the roles of humans and AI. Context Design Phase (Top Bun): Setting the purpose of the task, imposing constraints, and designing the overall architecture must be performed under human leadership. Data Processing Phase (Meat): Tasks involving repetitive and large-scale resources, such as calculating vast data, sorting, and drafting, are delegated to AI. Final Verification Phase (Bottom Bun): Ethical judgment, precise cross-referencing of facts (Fact-check), and final value attribution are returned to the human domain of responsibility to ensure system stability. ⑤ Hallucination Control and Strengthening Epistemic Boundaries via the SIFT Model AI possesses the attributes of a "stochastic parrot," combining tokens based on probabilistic frequency without a substantive understanding of meaning. Therefore, one must be wary of the "illusion of knowledge" created by AI's fluent output. To this end, a 3-step fact-checking protocol and the SIFT model must be strictly applied in practice. The habit of "Lateral Reading"—tracking original sources and contrasting them with external data—becomes a key mechanism to prevent intellectual free-riding on technical convenience. 3. Conclusion: The Strategic Mission of the Hyper-Intelligent Helmsman While the phenomenon of intelligence is manifested through engineering design, the core agent that controls it in a meaningful direction and creates business value remains rigorous human thinking. This masterclass is designed to ensure that students acquire the capabilities of a "Hyper-Intelligent Helmsman" who controls the powerful power source of AI and designs organizational systems. Strengthen the cognitive muscles of individual members and actively respond to technical challenges. When rigorous engineering control is combined with advanced cognitive abilities, AI will finally function as a strategic asset that drives the sustainable growth of both members and the organization.

★.NET 8.0★ This is a basic Windows Desktop development course using WinForms. Learn how to easily perform DB CRUD operations using EntityFrameworkCore. I will teach you class design methods based on loose coupling and the "basics" of Form UI Design. The source code used in the lecture is also provided :)

mongoDB, NoSQL You hear a lot these days, but is it still an unfamiliar database? Aren't you using it like a relational database (RDS/SQL)? No matter how good the technology is, if it's not used correctly, it will have the opposite effect. That's why we often see cases of failure in using MongoDB. This lecture will teach you everything from basic concepts to practical know-how so that you can use MongoDB as MongoDB-like as possible.

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

With the arrival of the AI era, the skills required of developers are becoming increasingly complex. You might have thought, "I just need to be good at frontend," but when you try to create your own service or integrate AI features, you eventually hit a massive wall called the backend. However, as you try to start backend development, are you overwhelmed by how much there is to learn—from database design and authentication (Auth) to storage management and complex infrastructure setup? Now, you can lay down that heavy burden. In this course, we use Supabase, which solves all these backend functions at once. We will guide you through the fastest shortcut to becoming a full-stack developer who can "instantly" complete a backend without complex server knowledge and bring ideas to life immediately.

We will explain ElasticSearch, LogStash, and Kibana from the basics. In the final step, you will find yourself using the ELK stack to analyze world population and stocks.

Do you want to get a job as a data analyst? Based on my experience, let's take a look at the tasks for each data role in real companies.

Learn how modern data teams leverage dbt. Build maintainable analytics models and validated data pipelines hands-on. Master core concepts of analytics engineering with a practical focus. This course is for those who want to take their data career to the next level 🚀

Make the most of relational database systems!

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

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.