Pandas Pandas Data Analysis Basics Practice

We will explain ElasticSearch, LogStash, and Kibana from the basics. In the final step, you will find yourself using the ELK stack to analyze world population and stocks.

This is a comprehensive lecture on machine learning. It consists of short and easy-to-understand video lectures based on practical experience.

Covers essential programming problems and solutions you need to have to successfully pass a coding interview (developer interview).

The fact that you can analyze data more easily and effectively with Python Pandas instead of Excel! Learn the basic functions of the Pandas library and how to use it in practice through financial data analysis.

You can use Python to analyze large amounts of data and visualize the analyzed data in graph form so that you can easily understand it at a glance.

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 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.

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

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

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.

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.

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.