Python library for data analysis and EDA

Learn Oracle Database SQL syntax.

A star instructor in IT development, artificial intelligence, and IT strategic planning provides an easy, detailed, and accurate explanation of the Python programming language.

Explanation of web data collection techniques using Python. Explanation of web data scraping using the latest technologies.

This article explains how to develop an AI web service using the API from OpenAI.

Explaining how to connect to a Python-based AI web server using FastAPI from a Spring Boot project.

We will introduce how to create deep learning models and CNN models using artificial neural network algorithms.

A star trainer in IT development, artificial intelligence, and IT strategic planning provides an easy, detailed, and accurate explanation of the fundamentals of the Java programming language.

Using pandas, we will learn data processing patterns that frequently appear in real-world situations and analyze the processed data.

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.

Learn a systematic approach to S&P 500 pair trading with Python. Lay the foundation for an investment strategy that excludes emotions through professional data analysis.

Datarian's Pandas basic course with over 10,000 cumulative students and rich online/offline lecture experience. Learn Pandas, Python's representative data analysis library, systematically with official documentation. You can also improve your proficiency by learning theories and solving problems in parallel.

Unlike existing lectures, this course has been newly designed based on the instructor's initial failures in learning machine learning, enabling easy understanding and application to real-world problems.

This lecture is a follow-up lecture to 'Python Data-Based Stock Quant Investment Part 1'. If Part 1 was more of an introduction, Part 2 is an in-depth lecture that focuses on the entire flow of practical strategy implementation and quantitative investment development. This class focuses on advanced Pandas techniques for handling time series data, and how to implement signal-based strategies and static/dynamic asset allocation strategies that require adjusting asset weights at various intervals based on this. Furthermore, it goes beyond strategy implementation and learns about 'code framework' that directly verifies and backtests various investment strategies with minimal code modifications, how to extend this to improve it so that it can lead to actual investment, and what to watch out for in this process. In addition to the programming component, you can experience the best Python quant investment flow that you cannot find in investment books, blogs, YouTube, etc. by deeply covering theoretical contents such as the two types of return concepts (simple return, log return) and evaluation indicators related to backtesting.

A course to understand and practice machine learning that efficiently performs complex analysis and prediction! Machine learning, which seemed difficult and complicated, can be easily and enjoyably learned in its core with Maso Campus' <Python Machine Learning>.

I will guide non-majors and beginners to quickly pass the Big Data Analysis Engineer practical exam! Theory lightly, practice thoroughly. Even without complex background knowledge, focusing on past questions, we intensively study only the points sure to appear on the exam.

Introductory lecture on recommendation systems by Datalian, who has over 10,000 cumulative students and rich online/offline lecture experience. Learn the basic theory of recommendation systems and practice movie data analysis together.

The Intermediate Python course is designed to deepen your understanding of Python by covering advanced topics such as functional programming, metaprogramming, exception handling, and object-oriented programming.

Learn how to collect data from the web and create a supervised learning model for machine learning!