金日韓のPythonベースのパンダスを活用したデータ分析

You can understand JSP WEB MVC Model2 Programming and learn how to apply it in practice through the MVC Model2 bulletin board project. (This lecture does not provide Q&A with the instructor.)

This course covers everything from JSP basics to a car purchasing shopping mall project. After completing the course, you will be able to carry out a real project right away. (This course does not provide Q&A.)

This course is a course to learn the basics of Python Windows programming. It enhances understanding by explaining how to use widgets and providing sample codes.

Currently, Android can be developed using the Java and Kotlin languages. The same Android application can be developed using either language. This course covers most of the content from basic to advanced Android application development, and all code is written using the Kotlin language starting from the basic content. We also use Material 3, Google's latest UI library. This course mainly covers menus, Android application components, threads, and Fragments among the entire Android curriculum, and future courses are planned to cover more in-depth topics. Soft Campus will always strive to provide the latest lectures. Thank you.

In this course, you will learn how to develop web application Front End using Angular JS. Angular JS is currently attracting attention as a next-generation web application development method along with React JS.

You can complete jQuery, which makes JavaScript easier and more powerful, through this course.

Introduction to C language, the foundation language for software development

You can create hybrid casual games using Unity 6.

Learn C# in the Unity environment. Learning C# will help you naturally learn the Unity development environment, which will be very beneficial for beginner developers.

Android 15 Version App Development with Latest Material 3 Library Part 1 UI Programming Course.

With the latest version in 2024 and a running time of 17 hours and 48 minutes, you can completely conquer Kotlin with this one course.

This course teaches you how to develop front-end web using React. You will learn the core JavaScript grammar for React, CDN-based React development, and NPM-based React development methods.

Using Unity based on the C# language, you will learn how to create a defense game step by step.

Develop three practical projects (memo application, LBS application, bulletin board application using FireBase).

You can learn the basic principles of run games like Cookie Run and create Unity games through practical projects in Unity by genre.

This course is a Java Android app development course consisting of a total of 4 parts. In the third lecture, we will look at data management and networks in Android apps.

This course is a Java Android app development course consisting of a total of 4 parts. In the second lecture, we will look at the menu and the 4 major components through practical examples.

This lecture is a lecture by instructor Jaeseong Yoon, who teaches Android lectures at App School: Like a Cool Lion.

This course is a course to learn the basics of Python grammar. It enhances understanding by combining grammar explanations with sample code. Learn basic Python libraries.

In this course, you will learn how to use the Tensorflow library to make data predictions. You will learn data analysis and processing methods that can be applied in practice by learning the library and analyzing sample data in parallel.

When you use Pandas for data analysis, preprocessing, machine learning, and deep learning, you tend to use only the functions you use frequently. There is a Pandas Cheat Sheet that collects and organizes only these essential contents. For those who get tired of learning Pandas from a thick book, we will teach you Python Pandas with just two pages of documents. Learn the core functions of Pandas with the cheat sheet provided in the official Pandas tutorial.

Using pandas, we will learn data processing patterns that frequently appear in real-world situations and analyze the processed data.

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.

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.

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.

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>.

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.

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.

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.

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