Concurrent and Parallel Programming in Python from Coursera

Dive into the world of concurrent and parallel programming with this detailed course designed for Python developers. Begin with threading, learning how to create and manage threads, and progress to building complex systems with threading classes. Explore practical projects like creating a Wikipedia reader and a Yahoo Finance reader, mastering the use of queues, schedulers, and database integration.

Advance to multiprocessing, understanding how to leverage multiple CPU cores for enhanced performance. Learn to implement multiprocessing queues and pools, handle multiple arguments, and optimize CPU-intensive tasks. Finally, delve into asynchronous programming, mastering async tasks, timeouts, and libraries. Combine asynchronous and multiprocessing techniques for robust and scalable applications.

This course provides a thorough understanding of concurrent and parallel programming, preparing you to tackle real-world challenges and optimize your Python applications for performance and efficiency.

This course is ideal for Python developers, software engineers, and data scientists who want to enhance their skills in concurrent and parallel programming. A basic understanding of Python is recommended.

What's inside

Syllabus

Threading

In this module, we will delve into the fundamentals of threading in Python. We'll start with an introduction to threading, multiprocessing, and asynchronous programming concepts. Then, we will explore the creation and management of threads, develop practical applications like a Wikipedia reader and Yahoo Finance reader, and integrate advanced features such as queues, schedulers, and locking mechanisms to optimize our concurrent and parallel programming skills.

Multiprocessing

In this module, we will explore the powerful capabilities of multiprocessing in Python. We'll begin with an introduction to the core concepts and advantages of using multiprocessing. Next, we will dive into practical implementations, including the use of multiprocessing queues, pools, and advanced techniques for handling multiple arguments and tasks. Finally, we will cover methods for efficiently processing and checking elements in lists within specific ranges, ensuring optimal performance in concurrent and parallel programming tasks.

Asynchronous

In this module, we will cover the essentials of asynchronous programming in Python. We'll start with an introduction to writing asynchronous programs and managing asynchronous tasks. You'll learn to use the async gather method and implement timeouts for handling multiple asynchronous operations effectively. We will also explore creating asynchronous for loops and using various asynchronous libraries. Finally, we'll delve into combining asynchronous programming with multiprocessing to achieve the highest level of concurrency and performance in your Python applications.

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Covers threading, multiprocessing, and asynchronous programming, which are essential for optimizing Python applications and tackling real-world concurrency challenges

Teaches how to create and manage threads, build systems with threading classes, and integrate queues, schedulers, and database interactions

Explores multiprocessing queues and pools, which are useful for handling multiple arguments and optimizing CPU-intensive tasks

Requires a basic understanding of Python, so learners without this background may need to acquire it before taking this course

Includes practical projects such as creating a Wikipedia reader and a Yahoo Finance reader, which can help learners build a portfolio

Explores combining asynchronous programming with multiprocessing, which allows learners to achieve a high level of concurrency and performance

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Concurrent and Parallel Programming in Python with these activities:

Review Python Fundamentals

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Solidify your understanding of Python syntax, data structures, and control flow to prepare for the more advanced concepts in concurrent and parallel programming.

Browse courses on Python

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Review basic Python syntax and data structures.
Practice writing simple Python programs.
Complete online Python tutorials or exercises.

Review 'Python Cookbook'

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Reference 'Python Cookbook' to gain practical insights and solutions for implementing concurrent and parallel programming techniques in Python.

View Python Cookbook: Recipes for Mastering Python 3 on Amazon

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Browse the chapters related to concurrency and parallelism.
Study the code examples and adapt them to your projects.

Create a Concurrency Cheat Sheet

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Consolidate your knowledge by creating a cheat sheet summarizing the key concepts, functions, and best practices for concurrent and parallel programming in Python.

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Review the course materials and external resources.
Summarize the key concepts and functions related to threading, multiprocessing, and asyncio.
Organize the information in a clear and concise format.

Four other activities

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Solve Concurrency Problems on LeetCode

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Sharpen your skills in concurrent programming by solving problems related to threading, locking, and synchronization on platforms like LeetCode.

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Find concurrency-related problems on LeetCode.
Implement solutions using Python's threading and multiprocessing modules.
Test your solutions thoroughly to ensure correctness.

Implement a Concurrent Web Scraper

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Apply your knowledge of threading, multiprocessing, and asynchronous programming by building a web scraper that fetches data from multiple websites concurrently.

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Choose a set of websites to scrape.
Implement the scraper using threading, multiprocessing, or asyncio.
Compare the performance of different concurrency models.
Optimize the scraper for speed and efficiency.

Write a Blog Post on Asynchronous Programming

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Deepen your understanding of asynchronous programming by writing a blog post explaining the concepts, benefits, and use cases of asyncio in Python.

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Research asynchronous programming concepts.
Write a clear and concise blog post explaining asyncio.
Include code examples to illustrate key concepts.
Publish your blog post on a platform like Medium or your personal website.

Review 'Concurrency with Modern C++'

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Read 'Concurrency with Modern C++' to broaden your understanding of concurrency concepts and compare them with Python's implementation.

View C++ Core Guidelines Explained on Amazon

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Read the book.
Compare and contrast the C++ and Python concurrency models.

Career center

Learners who complete Concurrent and Parallel Programming in Python will develop knowledge and skills that may be useful to these careers:

Python Developer

A Python developer writes software using Python, and this course is directly designed for them. This course provides a thorough understanding of concurrent and parallel programming, which helps a Python developer to write code that performs well under high loads. The course's detailed modules on threading, multiprocessing, and asynchronous programming help a Python developer write high performance code. The skills gained from the described practical projects will be invaluable for anyone working on complex systems. One who wishes to become a Python developer should absolutely take this course.

See salaries and explore the career path for Python Developer

Software Engineer

A software engineer often writes and tests code, and this course is directly applicable to their work when building scalable applications. Engineers must have a strong grasp of concurrency and parallelism, as learned in this course, to build systems that handle multiple operations efficiently. The course's hands-on projects like creating a Wikipedia reader and a Yahoo Finance reader illustrate building practical systems, directly relevant to what a software engineer does. This course helps build systems that utilize threading, multiprocessing, and asynchronous programming which are critical for real world development.

See salaries and explore the career path for Software Engineer

Systems Programmer

A systems programmer works on low-level software that controls a computer, and this course helps develop their ability to write optimized code. The course modules on threading, multiprocessing, and asynchronous programming are essential for writing performant and reliable systems-level software. The course's practical projects, specifically those that involve queueing, scheduling, and managing concurrent tasks, are fundamental to systems programming. A systems programmer needs to build systems that can execute tasks concurrently and this course focuses on that skill. By the end of this course, a systems programmer will have a more complete understanding of high performance Python.

See salaries and explore the career path for Systems Programmer

Algorithm Developer

An algorithm developer creates the computational logic for systems, and this course directly helps them write performant algorithms in Python. The course's focus on concurrency and parallelism will help them write algorithms that are robust and fast. The core concepts of threading and multiprocessing that are covered in this course are necessary to build sophisticated algorithms that handle large data sets. An algorithm developer should find the material covered in this course helpful in their day to day work. The focus on high-performance programming helps any algorithm developer.

See salaries and explore the career path for Algorithm Developer

Backend Developer

A backend developer builds the server logic for applications, and this course may help in the development of high performance server code in Python. The course's focus on concurrency and parallelism is directly relevant to building backend systems that can handle multiple requests efficiently. The detailed exploration of threading, multiprocessing, and asynchronous programming helps a backend developer to build robust servers. The practical projects, specifically those involving network requests, provide valuable experience for backend development. A backend developer should find the material covered in this course to be highly applicable to building scalable software.

See salaries and explore the career path for Backend Developer

Application Developer

An application developer builds software applications, and this course may be useful in creating performant apps. This course's coverage of threading, multiprocessing, and asynchronous programming directly impacts the design of applications that handle multiple operations concurrently. The course's practical projects in networking, such as the Wikipedia and Yahoo Finance readers, are useful for building real-world applications. An application developer will use the skills learned in this course to make their applications robust and scalable. This course is helpful for building a foundation in writing high-performance Python applications.

See salaries and explore the career path for Application Developer

Cloud Engineer

A cloud engineer builds infrastructure on cloud platforms, and this course's focus on parallel and concurrent programming is essential for creating scalable services. This course details how to leverage Python to build scalable and robust applications, which is directly relevant to handling cloud workloads. This course introduces threading, multiprocessing, and asynchronous techniques, all of which are fundamental for any cloud engineer. The course may be valuable to cloud engineers who need to optimize their Python based cloud applications. The focus on real world projects will help the cloud engineer build efficient cloud services.

See salaries and explore the career path for Cloud Engineer

Game Developer

A game developer creates video games, and this course is helpful in creating efficient game systems utilizing Python. This course's detailed approach to threading, multiprocessing, and asynchronous programming may help a game developer make their games performant. The course's focus on optimizing Python applications for concurrency and efficiency will be useful to anyone building games with Python. A game developer should find the content of this course relevant to maximizing their game performance. This course is particularly helpful for a game developer who wants to build high performance game systems.

See salaries and explore the career path for Game Developer

Machine Learning Engineer

A machine learning engineer develops machine learning models, and this course may help them create high-performance data processing routines. The course's detailed lessons on multiprocessing and asynchronous programming directly apply to building more efficient machine learning workflows. Machine learning engineers often deal with large datasets and this course's focus on concurrency helps them better leverage their hardware. They can apply what they learn from this course to speed up their code. A machine learning engineer should find this course useful in optimizing Python code.

See salaries and explore the career path for Machine Learning Engineer

DevOps Engineer

A DevOps engineer automates and manages software deployments, and this course may be useful in writing Python automation tools. This course provides deep insights into concurrency and parallelism in Python, skills directly applicable for writing high-performance automation scripts. This course helps a DevOps engineer to build tools that manage multiple operations efficiently. The content covered in this course is helpful for any DevOps engineer who needs to write scripts using Python and wants to optimize their performance. The learnings from this course are valuable for anyone who wishes to optimize their Python workflows.

See salaries and explore the career path for DevOps Engineer

Data Scientist

Data scientists often need to process large datasets and this course's approach to high performance programming may be useful to them. This course emphasizes optimizing Python applications for performance and efficiency, which is crucial for data scientists who need to process and analyze datasets quickly. The course covers both multiprocessing and asynchronous techniques which are helpful in speeding up data processing workflows. The ability to handle concurrent operations, as taught in this course, will help data scientists write more efficient code. This course may be useful for any data scientist who also writes code.

See salaries and explore the career path for Data Scientist

Embedded Systems Engineer

An embedded systems engineer develops software for hardware devices, and this course may be useful for optimizing Python scripts on resource-constrained hardware. The focus on performance and efficiency taught in this course is helpful for embedded systems programming, where resources are limited. The course covers threading, multiprocessing, and asynchronous programming, which are useful for managing tasks on embedded systems that only have a processor or two. This course will be useful for an embedded system engineer who is writing Python code. They will gain skills to optimize their embedded applications using Python.

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Research Scientist

A research scientist conducts experiments and analyzes data, and this course may help them to optimize their code in Python. The course's focus on high performance programming is relevant to any research scientist who writes code for simulations or data processing. This course covers concurrency and parallelism which can drastically speed up computational tasks. The coverage of multiprocessing and asynchronous techniques can help research scientists make their code more efficient. This course is useful for any research scientist that uses Python for data processing.

See salaries and explore the career path for Research Scientist

Quantitative Analyst

A quantitative analyst uses mathematical and statistical models in finance, and this course may be helpful for writing high performance Python. This course introduces methods for optimizing Python applications which is crucial for a quantitative analyst who needs to process large financial datasets efficiently. The course covers multiprocessing and asynchronous techniques which can be useful for running simulations or processing data. The knowledge of concurrent programming taught in this course helps a quantitative analyst write code that performs well under heavy computational load. This course may be useful for quants who also write code.

See salaries and explore the career path for Quantitative Analyst

Financial Engineer

A financial engineer develops models and tools for the financial industry, and this course may help them in writing efficient Python code in their day to day work. The course's emphasis on optimizing Python applications for high performance is relevant to the high computational demands of financial modeling. This course covers threading, multiprocessing, and asynchronous programming, which is crucial for any high performance financial application. A financial engineer may use the content of this course to make their systems more efficient. This course may be beneficial for someone who wants to create efficient financial applications using Python.

See salaries and explore the career path for Financial Engineer

Concurrent and Parallel Programming in Python

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