파이썬(Python)으로 데이터 기반 주식 퀀트 투자하기 Part2

Repetitive pipeline management in Data Warehouses (DW), now free yourself from that painful cycle with dbt! Building on the efficiency provided by dbt, become a data analytics engineer who creates higher value through business-centric data modeling, effective data lifecycle management, and more.

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.

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.

The first class in the curriculum, "Anyone Can Learn Python", is an introductory programming course for those with no programming/coding experience. Based on my experience with numerous classes taught to non-majors, this class focuses on the "story" and "principles" from the structure of a computer to how programs work and the role of programming languages in this process, allowing beginners to approach the unfamiliar concept of programming more familiarly. In addition, by adopting the Python language, which has recently been growing at an incredible rate, the barrier to entry for programming has been lowered even further, and when explaining the grammatical elements of the language, the focus is on "Why" and "How" rather than "What", so that it is not a simple grammar memorization class, but rather on real-world examples and practical applications. Based on the incorrect learning methods that beginners often use, it also covers tips on how you should view and learn programming in the future, and based on the trial and error I experienced while struggling and wasting time while teaching programming on my own in the past and the inner strength I gained from conducting offline classes for many years, it focuses on what beginners find difficult and how to convey it, so it will be of great help to those who are hesitant about getting started with Python.

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.

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

Do you want to create a tool that can help you with your real-world investments using Python? In this course, you will learn how to create an interactive web dashboard for financial data analysis for intermediate Python users and learn about pair trading strategies for in-depth quantitative work. We hope that you will be able to create your own useful analysis tools to help you analyze financial data more powerfully and make wise investment decisions.

This course covers data analysis using ChatGPT and Pandas utilization. Get a preview of the changed future ChatGPT will create.

This is a course for beginners in stock data analysis. Get started with stock data analysis using Python!

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.

Automated trading program, source code ready-to-use! Deploy to production with Oracle Free Tier server!

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>.

This course covers the process of automating algorithmic trading in local and cloud environments, and focuses on hands-on practice.

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.

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