Python Concurrent Programming: From Data Collection to Web Development (feat. FastAPI, async, await)

Slow Python?
Soar with concurrent and parallel programming!

The lecture
Before you start

This lecture has two main goals: to help you understand the concepts of concurrency, parallelism, and asynchronous programming, and to teach you which code to use in which situations .
In the latter half of the course, we will work on a real-world project using concurrent programming to help you move beyond theoretical understanding to practical application of the concepts.

Concurrent, asynchronous, and parallel programming
With the latest Python syntax

Since Python version 3.7, support for concurrent, asynchronous, and parallel programming has expanded significantly. As demand for these features grows, many frameworks and libraries supporting concurrent, asynchronous, and parallel programming have emerged. However, many people struggle with Python's ever-changing syntax. Furthermore, many who have learned the theory are unsure of how and when to apply the syntax in practice.
This course will help you immediately apply the concepts of concurrency, asynchrony, and parallelism by providing practical training in accordance with the latest Python syntax .

Concurrency and parallelism
at the same time

This lecture covers both concurrency and parallelism, demonstrating practical applications. Therefore, the title, "Concurrent Programming," has a dual meaning: concurrency in the context of concurrency and parallelism, and concurrency in the sense of "at the same time ." This is because parallelism is often expressed as "simultaneously" or "at the same time."

This lecture
I would like to recommend this to these people

Concurrency? Parallelism?
What is all this?

Concurrency and parallelism
What it is and why it is needed
If you want to understand

With the latest Python syntax
Concurrent programming

Concurrency, asynchrony, and parallelism
Grammar up to date
If you want to write

Python concurrency syntax
In practice too

It can also be applied in practice
Concurrency that can be
Programming

Backend developers too
Curious FastAPI

Actively use asynchronous grammar
FastAPI used
If you want to try it

• Data analysts and developers who want to speed up their Python programs
• Those who want to learn concurrency, asynchrony, and parallelism grammar in line with the latest Python grammar
• Those who want to immediately apply Python concurrent, asynchronous, and parallel programming in practice.
• Backend developers who want to learn FastAPI, a web framework that actively uses asynchronous syntax.

This lecture
This will also help people like this.

• JavaScript (asynchronous programming language) developer learning Python

Concurrency Programming in Python:
From data collection to web development

This lecture
It goes like this

Python Coroutines and Asynchronous Functions

Learn about basic terminology and Python coroutines.

• We will explain and organize important but confusing terms such as bound, blocking, synchronous, asynchronous, subroutine, and coroutine using mind maps and code.
• This book introduces basic asynchronous programming in Python using coroutines. It also demonstrates, through practical examples (such as network I/O and CPU operations), why asynchronous programming is essential for speeding up specific situations.

Python multithreading and multiprocessing

Multithreading? Multiprocessing? Concurrency? Parallelism?

• We will implement the coroutine-based program developed in Section 1 using multithreading techniques that utilize concurrency (parallelism) across multiple threads. This will help you understand how to process synchronous code in parallel without the help of the asyncio module. We will also explain, using examples, why multithreaded parallel programming in Python is impossible due to the Python Global Interpreter Lock (GIL).
• We'll implement a program that uses multiprocessing techniques for parallel processing. This will teach you how to use multiprocessing in Python and explain how to design code for distributed processing in Python, under what circumstances.

Collecting data with concurrent programming

Applying Concurrency and Asynchrony to Real Projects

• Based on the theories learned so far, we'll develop a web crawler and a data collector using open APIs. We'll also spend time developing actual programs and becoming familiar with concurrent programming.

Real-World Project: "Data Collector Web App" with FastAPI

MongoDB CRUD, data visualization, and web development with concurrent programming

• Based on the theory learned so far, we will develop a "data collector web app." This project uses concurrent programming to crawl the web and store the collected data in MongoDB. We will connect MongoDB with FastAPI to develop a web app that analyzes and visualizes the stored data. This project will help you understand and apply asynchronous and concurrent programming in real-world situations.

Deploying Projects with AWS Cloud Computing

The project doesn't end with simple development. We'll also teach you how to deploy a web app to a server using AWS cloud computing technology.

Bonus lecture

Lectures updated through questions

• The Python grammar lectures required for this course will be continuously uploaded.
• Important questions will be answered and uploaded continuously in bonus lectures.

Expected Lecture Questions
Q&A

Knowledge sharer
What I want to say

The lecture will continue in the future
It will be continuously updated

Technology is constantly changing. It's said that if you can't fix the code you wrote in the past, you haven't learned anything. As Python versions are upgraded, we'll update our courses to reflect the new versions so you can learn even better techniques.

The question is
You are always welcome

If you encounter any questions or need further explanation during the course, we'll provide logical, case-based answers. We look forward to hearing your questions!