Honestly, After Tax: An ETF Backtesting and Paper Trading Pipeline Built with Claude Code

Learn how to work efficiently using Python, a basic programming language.

When using Pandas for data analysis, preprocessing, machine learning, and deep learning, we often find ourselves relying on the same core functions. There is a Pandas Cheat Sheet that compiles only these essential features. For those who have grown tired of trying to learn Pandas through thick books, this two-page document will teach you Python Pandas. Master the core functions of Pandas using the cheat sheet provided by the official Pandas tutorial.

Fully prepare for the Goorm environment—the biggest challenge of the Big Data Analysis Engineer practical exam—on Algorithun’s practice platform. Repeatedly practice past Type 1, 2, and 3 problems in the exact same way as the actual exam environment, developing real exam-day familiarity rather than memorizing code. This is a hands-on, practice-focused course designed to train you to work instinctively even in an environment different from Colab or Jupyter.

Now display Python results as web pages! You can create your own web page using only Python. This is an opportunity to create an Interactive Dashboard that allows you to check results created with Python, such as data analysis and machine learning, on a web page!

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.

Based on the instructor’s experience of struggling when first learning machine learning, this course has been newly designed—unlike conventional courses—to help you understand machine learning easily and apply it to real-world problems.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

- 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]

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

Son Heung-min's 100th goal project! Practical crawling & data analysis of Transfermarkt

This sharing session was prepared not for those who are already proficient in Claude, but for those who are just starting out and feel overwhelmed by where to begin. We will show you the entire process of a real-world project—from planning and CRM automation to content creation and ad analysis—all handled in one-stop within a single conversation, using actual cases.

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.

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

If you're planning to learn Python, pay attention to this course!!! It's completely free~ I highly recommend it for preliminary learning before taking formal courses or starting personal study.

"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.

🚀 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.

In this class, you will learn techniques for retrieving (crawling) and processing data from various operating sites using Python, and apply them directly to various examples that you may encounter in the field. Through this, you will learn techniques that focus on solving practical problems based on an understanding of the basic principles of how the web works, rather than one-dimensional task automation by copying and pasting given codes, and accordingly, you will be able to collect and process data from any site in the future. You will also learn how to resolve various exceptions that occur in this process with code, and ultimately, you will be able to solve and automate various problems that occur in daily life with programming thinking.

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

Starting from a blank starter app, you’ll build a fully functional investment analysis workflow by manually adding 65 nodes for stock screening, VCP pattern analysis, supply-and-demand tracking, backtesting, AI commentary, and Telegram alerts. This hands-on course teaches you how to connect AI-generated code to real app features and complete your own analysis pipeline covering both the Korean and U.S. stock markets.

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.