[Mito] Mito로 시작하는 파이썬 데이터 분석

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

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

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 [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.

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.

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

This is not an investment lecture. It covers data collection, analysis, and visualization through securities data. It covers various data formats and teaches various text preprocessing techniques. It covers visualization techniques for time series data and methods for expressing scale. It covers several techniques for interpreting stock price data. This lecture is a course that teaches you how to gain insights through data analysis. The content learned through stock price data is organized so that you can apply it to collecting, analyzing, and visualizing data such as demand, inventory, sales, and traffic volume, where time series is used.

This book covers basic usage and data preprocessing methods for Python beginners/non-developers. As an appendix, we also provide a preprocessing method using the ChatGPT code interpreter.

Introductory lecture on recommendation systems by Datalian, who has over 10,000 cumulative students and rich online/offline lecture experience. Learn the basic theory of recommendation systems and practice movie data analysis together.

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>.

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.

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.

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.

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.

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

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!