Complete Mastery of Big Data Analysis Certification Practical Exam Past Questions [Includes all previous sessions · Continuous updates · Dedicated practice environment provided]

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!

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!

This is a Python basics course for starters who want to grow as data analysts. - Those who want to do data analysis but don't know Python - Those who want to learn machine learning with Python - Those preparing for the Big Data Analytics Engineer practical exam - Those who want to develop machine learning-based applications with Python

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

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

Learn how to understand advanced machine learning models without math and implement them easily in Python.

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

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.

There are frequently appearing question patterns in the Big Data Analysis Certification practical exam. There are also a few lines of code that you must memorize before going to the testing center. If your goal is to pass the exam, be sure to take this course.

This course is not just about "how to implement" a Transformer, but about dissecting why this architecture was created, what role each module plays, and how the entire model works from the designer's perspective. We deeply analyze the internal computation principles of Self-Attention and Multi-Head Attention, and directly verify through formulas, papers, and implementation code what limitations Positional Encoding, Feed-Forward Networks, and Encoder·Decoder structures were introduced to solve. Starting from Attention, we assemble the entire Transformer structure ourselves, and actually perform training to experience firsthand how the model operates. This course is the most structured and practical roadmap for "anyone who wants to completely understand Transformers."

A complete roadmap for obtaining the Big Data Analysis Engineer certification! We provide a systematic learning process for passing the exam, from data analysis beginners to experts.

"While following trends is important, unwavering fundamentals are what build true skill." This lecture is a beginner course designed to help you understand the flow of data science and properly master the most practical techniques.

Numpy and Pandas are the foundation of data analysis and the backbone of AI frameworks, and I've packed this course with the core practical know-how I've accumulated through my work and teaching experience. I've designed it with a hands-on approach so that marketers, planners, and beginner analysts can easily follow along with data cleaning and processing—the areas where they most often get stuck. Through this course, anyone can develop the skills to handle data and produce analysis results on their own, without needing help from developers.

This introductory course, designed to be the easiest and most practical for Python beginners, covers intuitive dashboards using Streamlit, Google Cloud Platform for deployment, and more. Additionally, you can also prepare for the 빅데이터 분석기사 practical exam.

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

Do you want to automate repetitive tasks with AI without using paid tools? I will share the practical know-how I gained from building systems myself using n8n and Llama. This is not a lecture where you simply learn "how to connect nodes." We will build actual workflows that you can use immediately, such as automatic Google Sheets organization, YouTube subtitle collection, AI fact-checking, and schedule management.

"Mastering Model Context Protocol (MCP)" is a practice-oriented engineering course designed to help developers build secure, production-ready AI backends. Based on my experience helping thousands of students resolve confusion in LLM integration, Tool Calling, and backend architecture, I aimed to address the most common challenges through this course. "How can I build a reliable backend that an LLM can call securely?" "Should I choose SSE, stdio, or streamable-http?" "How can I scale MCP to a real-world application level using FastAPI, Auth0, and LangGraph?" "How should I structure MCP Tools, Resources, Prompts, and Context?" This course provides detailed, step-by-step guidance, from running a minimal MCP server to deploying a fully secured, Docker-based system. All lessons are practice-focused, centering on reducing complexity and providing clear, repeatable workflows for building modern AI systems. If you are tired of vague tutorials and want a clear, concrete, engineering-level understanding of MCP, this course was made for you.

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

Building a Full-Stack System Without Coding: Complete the entire pipeline from data collection to AI/ML analysis and web dashboards using only Korean prompts (Vibe Coding). Owning a 100% Automated Pipeline: Secure an independent stock analysis tool that sends S&P 500 screening and investment opinions via Telegram every morning. Acquiring a Practical Data-Based Investment Framework: Master institutional-level data analysis and risk management capabilities based on source code currently in actual operation.

RAG (Retrieval-Augmented Generation) from theory to the latest multimodal and agent-based RAG! This is a hands-on lecture designed to be understandable even for non-majors. From paper reviews to practical code implementation, it's designed so that even those encountering RAG for the first time can easily follow along.

We explored the essential Python syntax necessary for big data analysis and, through various examples, presented methods such as big data file processing and visualization.

This course aims to cover the latest Gemini API SDK (@google/genai) in a Node.js and TypeScript environment. You will learn how to use multimodal technology to process text, images, and video simultaneously. We will practice the entire process, from issuing an API key to project setup and model calling.

Starting with the question, "If I were on the Titanic, would I have survived?", this course completes a full-stack project where you develop an AI model to predict survival probabilities based on real data and deploy it as a web service. You will gain hands-on experience in the entire process of AI and web development, from deep learning modeling using PyTorch to building a backend server with FastAPI and implementing a user interface with Next.js.

This course focuses on developing practical visualization skills that can be immediately applied in the workplace by utilizing Seaborn, a core tool for data visualization, and plotly/Dash for building interactive dashboards. Through various hands-on exercises, you can enhance both your ability to create insight-driven visual materials and implement web-based dashboards.

Build data-driven investment strategies with Python quant investing and put them into practice. You can implement various asset allocation strategies and ultimately create your own unique investment strategy.