Python for Advanced Users: Learn the Grammar of Concurrency and Parallelism Feat. Multithreading vs. Multiprocessing (Inflearn Original)

Python, Beyond the Basics and into Practice!
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• ✅ Python Basics to Advanced Grammar
• ✅ Python metaclass design
• ✅ Concurrent Programming

In-depth Python grammar lecture!
Python Technical Interview Preparation & Concurrency and Parallelism Basic Programming Methods !

This course is designed for those who know the basic grammar of Python and can use it , job seekers who want to find a job in a field that utilizes Python , developers (engineers) who are preparing to change careers , and those who want to study the internal principles of Python in more depth . It is designed to help you acquire knowledge about Python concurrency, parallelism, and distributed processing . It is not a simple mechanical explanation, but rather a live course where you code together directly .

Complete Guide to Python for Masters
Concurrency and Parallelism, Threading and Multiprocessing Basic Core Operational Principles and Features

From explaining Python concurrent programming to writing practical examples

While various open sources are being developed in a wide range of fields utilizing Python, global services are already being provided in a wide range of fields. Support for concurrency technology is a hot topic in the overall programming field. Python also provides a framework/library related to concurrency that is not lacking compared to other languages .

I think that many development languages that are somewhat behind the development of hardware are showing vulnerabilities in processing speed and stability in the process of building infrastructure and systems related to processing the latest large amounts of data.

People who develop software using Python believe that learning concurrent programming that can solve data distribution problems and fully utilize hardware performance is necessary to improve their skills to a certain level. This can be confirmed through the desired talent profiles of many IT companies.

While working with and educating many developers, engineers, and analysts in the field, I have witnessed the growth of various colleagues. Some people read the specs (document) first and start coding without writing code themselves, some use Python as a utility after learning the appropriate amount of theory, and some use Python by going back and forth between theory and practice... The conclusion from various patterns is that people who learn the unique operating principles of programming languages and apply them to practice grow very quickly. This will also be related to moving to the desired job, increasing their salary, and starting a startup.

Based on the above experience, I prepared this lecture to convey the difficult theoretical and practical learning of concurrent programming, which is often covered in depth in Python and other programming languages, in a way that is easy to read and fits the unique grammar features of Python .

Python is recognized as a language with slow performance compared to other languages. We will study various functions that solve performance problems while looking into the internal working principles. Prior learning of computer architecture and working principles is also important.

I planned and conducted this lecture based on my extensive experience in Python development and online and offline classes. You will not simply understand the core principles theoretically, but will naturally understand them through the process of coding together in this class.

Lecture Table of Contents

Sections (0-1) of the lecture provide a preliminary learning on concurrency and parallelism, which will be covered later, based on easy examples of basic environment setup and Python threading.

Through this, you will learn examples that enable multiple calculations at the same time through general threads and CPUs. In addition, you will also be able to acquire sufficient basic knowledge about operating systems.

Sections 2 and 3 are the main topics of this lecture. We will provide examples of AsyncIO including multi-threading and multi-processing performance comparison and all its advantages through simple and easy-to-understand examples of parallelism and concurrency.

You will also learn how to write concise and simple code, which is an advantage of Python, through the high-level abstract package Future.

After taking this lecture

Whether it's for hobby, research, or practical development, once you have experience developing using Python, it's time to study about fast execution times. We will provide you with various experiences and know-how to shorten the time and effort required as much as possible through well-organized examples.

By the end of the course, you will have a deep and scalable knowledge of Python concurrency and parallelism, and will be able to prepare for high-level technical interviews with a skillful and scalable Python knowledge base that can be utilized at any time in collaborations in various fields.

Furthermore, based on your knowledge of Python and operating systems, you will be able to take the first step toward developing basic programming skills for large-scale data distribution processing and server-side large-scale services by acquiring knowledge of concurrency and parallel processing.

I recommend this to these people!

(Inflearn Original - This course is for those who have learned Python beginner or intermediate level, or those who can use Python at a higher level 🙌)

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