Are you still using pandas? Switch your data analysis stack to Polars+Streamlit right now

You can implement hands-on practice of infinitely generating cryptocurrency trading signals automatically using ChatGPT and Python, and backtesting and validating them. Additionally, you can learn how to combine all the strategies automatically generated by AI into a single strategy and apply it to actual trading.

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

Learn about the basics of Python required for data analysis, the data analysis framework (pandas), and the data analysis process.

If you could draw graphs but couldn't explain the data, this course is a process for building the "ability to read and explain" data. In PART 3, we perform Exploratory Data Analysis (EDA) focusing on real-world cases. ✔ Checking data distribution ✔ Analyzing relationships between variables ✔ Outlier detection and visualization interpretation You will learn the analysis structure using the Titanic and Iris datasets, and through a project utilizing TMDB 5000 movie data, you will directly experience the entire analysis process: Data cleaning → Setting analysis topics → Visualization interpretation. By the end of this course, you will possess the analytical capability to read and explain data.

Are you feeling anxious because it seems like everyone is trying Vibe Coding except you? Unsure of where to even begin? Don't worry. SSG Coach is here to help. Game development isn't difficult when you're Vibe Coding with AI. I will guide you so that anyone can create a game using only the free Gemini CLI. I will also provide essential game development knowledge, so that after completing this course, you'll be ready to take on other game projects as well.

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.

Here are some key competencies that are often overlooked by others in the fiercely competitive and complex semiconductor process job market.

This course is a direct lecture by the author based on the book "Python Quick Start - Python for Work - Ruby Paper (2023/04)". This course is a course to learn the basics and usage of Python, which is known to be easy to learn, so that even non-majors can use programming in real life or work. You will learn Python programming methods through real business cases rather than grammar-centered ones, and you will be able to increase the efficiency and reliability of your work by automating various business processes including HR and finance.

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.

* Lecture for those who are new to Git and GitHub * Basic Git features and Git-related plugins in VS Code

We will create a remote development environment using VS Code and extensions, and learn about WSL and code-server, a VS Code used on the web.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

We will learn how to develop Python, a general programming language that is widely used for data analysis, web development, security, and more, more efficiently.

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.

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

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>.

In this course, you will learn how to program kernels and develop PyTorch modules. You can use the knowledge you have gained to develop models faster.

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

"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.

I wanted to briefly cover the basics of Python through Jupyter Notebook, which allows you to immediately check the results of your coding.

How to easily set up git, VS Code, Node.js, Python, Java dev environments on Windows within 2 hours without struggling.

In Part 3 of Creating a Python Trading Room for Quant Investment, you will learn about portfolio theory and concepts necessary for real investment, as well as how to implement them with code. In Parts 1 & 2, you mainly learned about code implementation methods for analyzing financial data, and in Part 3, you will learn about 'portfolios', which are an important part of quantitative investment. With the question of why you should create a portfolio, you will learn portfolio theory, implement it in Python, and practice the code necessary for real portfolio investment. I hope this lecture will be helpful for your own portfolio investment.