Python Basics and Data Analysis for Beginners

For beginners, learn about the most basic commands of git & github, flow.

In this course, you will learn how to operate PostgreSQL and explore methods for integrating it with Python.

This course is designed to teach the basics of the Python programming language in an easy and practical way that suits modern life. Python is easy to learn even for beginners thanks to its concise and easy-to-read syntax, and is used in various fields such as data analysis, web development, and artificial intelligence.

There was a rumor that Ediya would open a store near Starbucks. How different would the locations of Ediya and Starbucks be? Will the real estate price fluctuation trend from 2013 to 2019 be reflected in the apartment sales price? What kind of parks are there in my neighborhood? How can I utilize the data in the public data portal? The goal is to become familiar with Python and various data analysis libraries by handling various types of data through public data.

This course is for both beginners who have never been exposed to programming before and those who have some theoretical knowledge about Python. The main purpose of the course is to create interest and enjoyment in Python based on real projects. I hope that through this course, you will gain more ideas and have more fun with Python.

This introductory course, designed to be the easiest and most practical for Python beginners, covers intuitive dashboards using Streamlit, Google Cloud Platform for deployment, and more. Additionally, you can also prepare for the 빅데이터 분석기사 practical exam.

AI does the coding, I get the results! Maximize practical programming skills, achievable even for beginners!

Let's build a solid foundation in Python and solve data analysis problems!

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.

A course where you can not only build programs required for blog operation using Python, but also earn additional revenue through sales and branding.

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.

Solve coding test problems using Python.

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>.

A lecture consisting of special lectures by professors from Canadian national universities. It covers Python, Code, Scratch programming introduction/basics and practical applications. After completing the lecture, receive a global certificate from Codesprint : )

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

This is a Python coding test lecture centered on problem solving, tailored to those who are new to coding tests.

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.

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.

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.

Anyone can easily create a Python game using the Pygame library, starting from zero base.

We guide non-majors and beginners to quickly obtain the Big Data Analytics Engineer practical certification! Theory lightly, practice thoroughly - even without complex background knowledge, we focus on learning only the essential points that appear on the exam, centered around past exam questions.

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.