Analyzing and processing data with Python Pandas

This course is a course to learn data analysis methods using the Pandas library. You will learn practical application methods by learning the library and analyzing sample data in parallel.

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.

You can use Python to analyze large amounts of data and visualize the analyzed data in graph form so that you can easily understand it at a glance.

In this class, you will learn about the principles and methods of analyzing and processing various types of financial data using Python's Pandas library, apply them to situations you may encounter in the real world, and ultimately learn how to implement backtesting based on financial statement data (based on Kang Hwan-guk's book, "You Can Do Quant Investment"). As a result, you can break away from being a "passive investor" who simply follows what others say without verifying or basing the investment logic, and become a "self-directed and active investor" who can freely extract various elements necessary for strategy implementation from data and quantitatively analyze them using Python and Pandas.

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

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

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.

"Why does my automated trading collapse in a bear market?" I have also deeply pondered this same question. Most automated trading logics on the market seem to generate profits during a bull market, but they lose their ability to respond the moment the market sentiment changes. To solve this problem, I have combined the know-how I researched myself over the past two years with the latest AI technology. In this course, you will learn how to directly design a 'Transformer-based deep learning model' that simultaneously reads macroeconomic trends and individual stock flows, and how to automatically train and deploy it in a free GPU environment. In particular, we will introduce sophisticated data normalization techniques at the hedge fund level to build an 'Intelligent Comprehensive Scoring System' that goes beyond simple indicators to incorporate news sentiment. To this, we add an 'AI Safety Gate' that implants the insight of a trader with 20 years of experience on Wall Street, adding a final layer of confidence from a human trader's perspective right before a purchase. Beyond simple buying and selling functions, you will directly implement the entire process of an institutional-grade algorithmic system by adding dynamic take-profit and stop-loss logic, eight self-recovery mechanisms, and real-time Slack reports.

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!

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.

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.

If you're just learning Python and don't know what you can do or what you should do? Let's learn how to use web technologies with Python, such as web crawling, automation, and APIs.

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.

Let's cultivate professional expertise in programming, algorithms, and statistical analysis!

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

Even without coding experience, you can create a real-world work automation program yourself with Cursor AI. This course is a hands-on project in which you use Python and Cursor AI to automatically collect news comments (Crawling), save them as a CSV file, and analyze public opinion and sentiment, creating a complete project from start to finish. This is not a course where you simply type along with the code. It is designed to let you experience the entire real-world development process, including how to ask Cursor AI effective questions, setting up the development environment, writing automation programs, and troubleshooting errors. By following the course, you will learn step by step—from installing Python and configuring Cursor AI to the basics of work automation, scraping news comments, saving data as a CSV file, performing sentiment analysis, and fixing errors that arise in actual projects. Even if you are new to programming, you will naturally learn how to develop alongside AI, automate repetitive tasks, and build practical skills for using data.

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.

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!

This is a basic Python Excel programming course learned through an xlsxwriter tutorial. You can analyze data and automate tasks using Python and Excel.

🚀 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.