AICE Associate Certification Quick-Pass Guide — From Concepts to Solving Reconstructed Past-Exam Mock Tests

The Perfect Roadmap to Earning the Big Data Analysis Engineer Certification! We provide a systematic learning process to help everyone pass, from data analysis beginners to experts.

At a time when training data analysis professionals is needed, this course is designed to help you obtain the nationally recognized 'Data Analysis Associate' certification.

1. Mock exam problem-solving for passing AICE Associate, Korea's only state-certified AI certification. 2. A course featuring 12 mock exam sessions with the same question types as the actual AICE Associate exam.

Are you feeling overwhelmed by the practical exam because you’re new to Python? An instructor with over 10 years of experience teaching data analysis will take you from the basics—starting with a single variable—to Type 1 preprocessing, Type 2 modeling and result.csv submission, and Type 3 statistical tests across 81 lessons. Each video comes with a fill-in-the-blank notebook, and you’ll type along yourself in a practice environment identical to the exam setting, so what you learn stays with you instead of ending when the video does. Use the self-assessment to choose your track (complete beginner: 6 weeks · experienced learner: 3 weeks · two weeks before the exam) and take only the lessons you need.

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!

Simple tutorials alone make it difficult to apply in practice. I will clearly pass on my professional know-how, covering complex state management and multi-agent design methods.

Have you learned C++ and Java separately but felt confused about why they were designed differently? This course compares both languages simultaneously, helping you fully understand object-oriented structure, memory, and operational principles. Beyond simple syntax, you will gain a deep understanding through hands-on practice of: ✔ Compilation structures ✔ Memory model differences ✔ Internal workings of inheritance and polymorphism ✔ Virtual tables and JVM architecture Additionally, we provide a 'Python-based code comparison auto-generator tool' to help you visually understand the conversion structure between C++ and Java. We will elevate your skills from simply "using" object-oriented programming to a level where you can "design and explain" it.

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.

Master the nationally certified data analysis associate specialist qualification exam in 2 weeks! Acquire the core theories and background knowledge of data, and learn advanced knowledge of data analysis using R, a statistical analysis tool.

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."

We will develop a web app that collects data using concurrent/parallel programming in Python, loads it into MongoDB, analyzes it, and visualizes the collected data using FastAPI, a modern web framework. In the process, you will learn and understand Python concurrent/parallel programming using coroutines, multi-threading, and multi-processing, and understand how to use them to improve Python's performance. We will help you apply Python concurrent/parallel codes directly to your work. We will not only teach you how to develop and end, but also how to deploy and operate an actual app on the AWS cloud.

Do you manually type in numerous commands every time you build a complex project or create a deployment package? Currently, most open-source projects use build automation scripts to save the trouble of typing commands. Do you also find building tedious? Then use this course to automate your builds and increase your work efficiency.

Putting a neural network on an FPGA—the phrase is familiar, but few have actually done it. If you want to stay a step ahead in capstone projects, competitions, or graduation projects, you must be able to go beyond just training a model and actually build and prove how those computations run inside a real chip. This course bridges that exact gap between theory and physical hardware. You will complete a neural network accelerator (NPU) for MNIST digit recognition from start to finish—from reading specifications to Python golden models, Verilog RTL design, and FPGA verification. From Conv layers made with line buffers and parallel MACs, through ReLU, MaxPool, and Flatten, to fully connected layers and Argmax—you will integrate nine blocks into a single chip and prove for yourself that "the Python reference and the hardware output do not differ by even a single bit (Bit-True)." I have even included how to overcome those frustrating moments when the simulation runs fine but the results are subtly different. I will guide you through practical know-how that general deep learning textbooks don't cover, such as integer quantization, array axis reordering, and accumulator overflow prevention. I have distilled the trial and error I experienced over years of RISC-V CPU design and FPGA Bit-True verification into this course so that you don't have to repeat them. By the end of the class, you will have a completed hardware project that allows you to say, "I designed and verified it myself."

This course is for those who are familiar with deep learning terminology but feel overwhelmed by how models learn. Across 13 lectures, it connects and explains the key principles, from the fundamentals of linear models and neural networks to backpropagation, optimization, CNNs, RNNs, and transformers. You will build a solid foundation for studying deep learning systematically by understanding model architectures, the learning process, and the factors that affect performance.

Through this course, students will learn various reinforcement learning theories and implement them themselves, as well as create a reinforcement learning environment to test the reinforcement learning algorithm implemented using Unity Machine Learning Agents.

Theory, numerical approximations, intuition, and visualization in integral calculus. Enhance your math-coding skills.

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students will build their own chatbot using the GPT API and deploy it on the web using Streamlit. By experiencing everything from the basics to hands-on practice, you can build a solid foundation for your first steps in utilizing AI.

If you ask an AI to "build a model with this data," the first few lines might look plausible, but as soon as things get slightly complex, it starts using the wrong columns, introduces data leakage, and results in a model that performs worse despite a higher score. This course does not teach you better prompts. Instead, it covers how to directly design a loop (harness) where an agent understands data, plans, writes code, validates its own results, and backtracks when wrong. After manually walking through the five-piece loop of DS-STAR, you will assemble components one by one, from data profile cards, hooks, and skill hierarchies to independent validators and ablation.

This lecture is designed for job seekers preparing for Evaluation and Analysis roles in Samsung Electronics' DS Division (Memory, S.LSI, Foundry, TSP, AVP) and PE, AE, and Solution PE roles at SK hynix, who are struggling with the differences between business units and job functions. You will learn how to categorize job titles by company based on the actual workflow of Product Evaluation roles and how to translate your major-related experiences into professional terminology suitable for resumes and interviews. We will help you establish an application strategy by connecting experiences in Analog circuit design, Verilog/FPGA, process experiments, and data analysis with key industry terms such as Yield, Wafer Map, Shmoo Plot, and Failure Analysis.