[Mito] Python Data Analysis Starting with Mito

This is a lecture on the National Technical Qualification Big Data Analysis Technician Practical with Python. We hope you all pass!

In the [Learn Machine Learning with AutoKeras] lecture, you can learn about AutoKeras, which was developed from the Keras library previously used in machine learning, and use it directly through examples.

Let's use the Folium library and Google Cloud API provided by default in Python. You can perform spatial data analysis based on coding without a separate tool.

In the [Beginner's] Data Visualization with Power BI course, you will learn how to edit data using the query editor, visualize data using various tools, and check and share the analyzed results.

In the [Beginner's] Data Visualization with QGIS course, you will learn about basic spatial analysis methods using the QGIS program and spatial data.

This is a basic course on machine learning and deep learning analysis for data analysis beginners.

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.

What is Kaggle? Every year, Kaggle conducts a survey of users around the world. When you want to learn data science, you may have many questions. Is it too late to start? Which language should I choose between Python and R? How much is the salary? Find out by reading the responses from users around the world! With just a few powerful features of Pandas, you can perform frequency analysis and data visualization.

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>.

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.

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

With Python Quant Investment, you can create investment strategies based on data and invest according to the strategy. You can implement various asset allocation strategies and ultimately create your own investment strategy.

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.

Make AI data analysis and modeling results into a Web Program.

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.

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.