Public Data Analysis Theory

This course will help you quickly learn the core concepts of Python and the Pandas library. Improve your programming skills by working through the exercises after each section!

This is a lecture on data visualization using the Matplotlib library.

Master the Big Data Analysis Certification—an essential credential that is by no means easy to self-study—in a short period using R, an easy-to-learn data analysis tool!

On your way to work tomorrow morning, your phone will have “Today’s stocks selected by the system and the reasons why” waiting for you. An AI-powered Korean stock auto-trading system that reads foreign and institutional buying trends and handles everything from purchasing to taking profits and cutting losses automatically—you build it yourself, making it truly yours, rather than relying on a program made by someone else.

- Practical Kaggle Competition Project: From Data Exploration to Submission - Beyond Tutorials: Follow a Real Kaggle Competition from Start to Finish Have you signed up for Kaggle and completed the Titanic tutorial, only to feel lost about where to start when facing a real competition? If you simply copy someone else’s notebook and submit it, you’ll eventually hit the wall of wondering, "Why isn’t my score improving?" In this course, we’ll work through an actual Kaggle competition from start to finish, covering data exploration, validation design, feature engineering, modeling, and submission. Instead of memorizing code, you’ll develop an intuitive understanding of the competition mindset: “What should I try and why, and how can I validate its effectiveness?” [One line about your competition participation, awards, or practical experience]

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.

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.

"Why does my automated trading system collapse in a declining market?" I, too, have wrestled deeply with the same question. Most automated trading logic on the market may appear to generate profits during bull markets, but the moment market conditions change, it loses its ability to adapt. To solve this problem, I combined the expertise I developed through two years of hands-on research with the latest AI technology. In this course, you will learn how to design a 'Transformer-based deep learning model' that simultaneously reads macroeconomic trends and the movements of individual stocks, and how to automatically train and deploy it in a free GPU environment. In particular, by introducing sophisticated, hedge-fund-level data normalization techniques, you will build an 'intelligent comprehensive scoring system' that goes beyond simple indicators by incorporating short-selling balance and order-flow data. On top of that, an ‘AI safety gate’ infused with the insight of a Wall Street trader with 20 years of experience provides the final confirmation from the perspective of a human trader just before a purchase. Going beyond simply buying and selling, you will implement the entire process of an institution-grade algorithmic system with dynamic take-profit and stop-loss logic, eight self-recovery mechanisms, and real-time Slack reporting—all with your own hands.

"From the basics to 7 domain projects, completing the A to Z of deep learning (41 lectures in total)." Go beyond simply calling models and learn how to build clean, efficient deep learning pipelines using PyTorch Lightning, the current industry trend. Grow into a confident AI engineer capable of handling any data by directly implementing 7 projects, ranging from stock price prediction to generative AI, medical imaging, and sound analysis.

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 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.

You can learn the skills and know-how needed to improve work efficiency by utilizing Open API and Python.

Through this course, you will learn about SQL and databases, SQL syntax, database design for practical use, how to use databases through programming, and real-world applications of data analysis.

I want to create an algorithm trading bot that makes unlimited money and run it automatically. Where should I start?

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.

🚀 Practical AI Anomaly Detection with Databricks! 💡 Stop using expensive and slow models! From large-scale data optimization to real-time deployment, complete an anomaly detection AI that can be used immediately in corporate practice.

Linear Algebra in One Go. Learn linear algebra concepts in detail—concepts that, once learned, will serve as the building blocks for your thinking for a lifetime. Going beyond simple explanations, we kindly guide you through practical exercises to show you how to apply them in real-world situations.

Creating your own data analysis portfolio with Google Antigravity CLI harness engineering and vibe coding: data collection, analysis, reporting, dashboard creation, and task automation. Even if you are new to data collection, once you master the principles, prompt engineering, and how to use Antigravity, you can perform data collection, analysis, visualization, reporting, and dashboard creation just like a data analyst. From Python data collection to report writing learned with Antigravity IDE and CLI. Prompt writing and error resolution methods for foolproof data collection and analysis.

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!

AI that thinks and adapts? Now you can build it yourself. Master the core of reinforcement learning with intuitive explanations and code, not complex theories, and grow into a 'practical developer' through finance and industry projects.