Data-Driven Investment Methods for a Lifetime with Python Quant Investing

This is a lecture where you can learn Deep Learning using TensorFlow. Learn linear regression, logistic regression, and softmax regression models, and finally create an MLP model that can classify fashion images!

This course will help you quickly learn the core concepts of Python and the Pandas library. Improve your programming skills by solving the practice problems at the end of each part!

This course covers everything from core recommendation system algorithms to practical implementation. - Content-based filtering - Collaborative filtering and deep learning-based recommendation model implementation - Two-step recommender systems implementation - Hands-on practice using PyTorch/RecBole - Industry know-how and recommendation result visualization

This is a full-stack restaurant map tutorial that allows you to learn everything from front-end, back-end, Kakao Map API usage, and deployment all at once while creating a restaurant map of your favorite YouTuber.

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.

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.

Leverage Python to create your own indicators and a convenient tool for securities analysis!

We explored the essential Python syntax necessary for big data analysis and, through various examples, presented methods such as big data file processing and visualization.

Revolutionize your stock investments with AI! In this lecture, you will directly build a real, working AI automated trading system targeting the top 25 NASDAQ stocks. We provide a special opportunity to gain experience and know-how through simulated investments with the 한국투자증권 API, and to perform AI stock automated trading with a real account. Even stock beginners will have no problem. We will easily explain basic stock concepts and why you should invest in US stocks, and provide all source code and remote support. From now on, investment is essential for the next 60 years of our lives. Create your own AI investment partner that will be with you!

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

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

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

Based on the instructor's own failed experiences when first learning machine learning, this is a newly designed course that differs from existing lectures, making it easy to understand machine learning and apply it to real-world problems

You can quickly convert MATLAB code to PYTHON by extracting only the PYTHON grammar that matches the MATLAB grammar.

Creating Your Own Data Analysis Portfolio with Free Gemini CLI Vibe Coding: Data Collection, Analysis, Reporting, and Dashboard Automation Even if you are new to data collection, you can collect, analyze, visualize, and create reports and dashboards like a professional data analyst once you master the principles, prompt engineering, and VSCode. From Python data collection to report writing with Gemini CLI and VSCode: Learn prompt engineering and troubleshooting methods for foolproof data collection and analysis.

The "How Far Can You Go with ChatGPT?" series: Creating a Stock Auto-Trading Application and Web Investment Reports!

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.

Learn NoSQL technology for handling big data, one of the fundamental skills in full-stack and data science technologies used by modern startups. MongoDB is the easiest and fastest NoSQL technology to utilize. In this course, you will learn the basics of MongoDB in a short time and master the skills to handle and utilize MongoDB with Python.

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.

This course aims to acquire Python-based web crawling technology to collect data from web pages and improve data collection and analysis capabilities at work based on this.

A course chosen by 3,000 students! Updated to GPT-5 version! How far can you go with ChatGPT? Create automated stock trading applications and web investment reports!

Leveraging Python's basic syntax and popular libraries Auto-generating Python syntax and library code using ChatGPT

A course to understand and practice machine learning for efficiently conducting complex analysis and predictions! Even machine learning, which may seem difficult and complex, can be easy and fun to master with Masocampus's <Python Machine Learning>.

Through this course, you can learn SQL and database fundamentals, SQL syntax, database design for practical applications, database utilization through programming, and data analysis use cases.