Getting Started with Python 3 Concurrency from Pluralsight

This course will teach you how to make your Python programs faster, more efficient, and responsive. You will learn how and when to use the threading, multi-processing, concurrent.futures, and asyncio modules.

Until recently, you could speed up your programs by simply running them on newer, faster hardware. Now, instead of making CPU's faster, hardware makers are adding more CPU cores to your computing devices. To make programs run faster today, employing concurrency techniques to get your programs running on multiple cores simultaneously is paramount. In this course, Getting Started with Python Concurrency, you'll learn how to make your Python programs more efficient, responsive, and faster using Python's concurrency packages. First, you'll explore how to choose the right concurrency package for your task. Next, you'll discover how to distribute a task to worker processes to speed up execution by running on multiple cores. Finally, you'll cover how to do node.js style asynchronous programming using the asyncio package. When you're finished with this course, you'll have a solid understanding of concurrency concepts and how to apply them in Python in a simple and readable manner to achieve greater performance results.

What's inside

Syllabus

Course Overview

Concurrency Concepts

Threading

Multiprocessing

Abstracting Concurrency

Asynchronous Programming

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Appropriate for intermediate learners who want to improve their knowledge of Python

Instructors, Tim Ojo, are recognized for their work in concurrency and Python

Touches on several concurrency packages

No prerequisites are explicitly stated

Provides a solid overview of concurrency concepts and implementation in Python

Covers asynchronous programming, which is helpful for developing responsive web applications

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 Getting Started with Python 3 Concurrency with these activities:

Review Python Basics

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Review core Python principles and syntax to establish a solid foundation for advanced concepts in concurrency.

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Review variables, data types, and operators in Python.
Refresh your understanding of control flow (if-else, while, for loops).
Practice defining and calling functions.

Concurrency Resources Compilation

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Gather and organize a collection of valuable resources on Python concurrency to aid your learning and reference.

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Curate a list of articles, tutorials, and documentation on Python concurrency.
Create a centralized repository or document to store your compiled resources.
Share your compilation with other learners or within the course community.

Concurrency Discussion Group

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Engage in regular discussions with peers to exchange ideas, share experiences, and reinforce your comprehension of concurrency concepts.

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Participate in weekly or bi-weekly discussion sessions.
Share your understanding and insights on various concurrency topics.
Seek clarification and support from fellow participants.

Three other activities

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Threaded Task Execution Practice

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Reinforce your understanding of threading by solving exercises and challenges.

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Create different threads in a Python program.
Control and manage thread execution.
Troubleshoot and debug common threading issues.

AsyncIO Mastery with Tutorial

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Enhance your understanding of asyncio through guided tutorials and examples.

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Follow step-by-step tutorials on how to write asynchronous code.
Build practical projects using asyncio to apply your knowledge.
Debug and troubleshoot common issues faced in asynchronous programming.

Concurrency Case Study Analysis

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Apply your knowledge of concurrency to real-world scenarios by analyzing and presenting a case study.

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Identify a case study that showcases the use of concurrency.
Analyze the case study to identify concurrency patterns and challenges.
Create a presentation or report that summarizes your findings.

Career center

Learners who complete Getting Started with Python 3 Concurrency will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists analyze and interpret large datasets to extract meaningful insights and trends. They use statistical and mathematical techniques to build models that can predict future outcomes. Python is widely used in data science for tasks such as data cleaning, data analysis, and machine learning. This course provides a solid foundation in Python concurrency, which can help Data Scientists improve the performance and efficiency of their code. By understanding how to use multiple cores and asynchronous programming, Data Scientists can speed up their data processing and analysis tasks, leading to faster and more accurate insights.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They use programming languages and tools to create software that meets the needs of users. Python is a popular programming language for software development due to its versatility and ease of use. This course provides Software Engineers with the skills they need to implement concurrency in their Python code. By learning how to use threading, multiprocessing, and asyncio, Software Engineers can improve the performance and responsiveness of their applications.

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Web Developer

Web Developers design and develop websites and web applications. They use a variety of programming languages and tools to create websites that are both functional and visually appealing. Python is a popular programming language for web development due to its simplicity and versatility. This course provides Web Developers with the skills they need to implement concurrency in their Python web applications. By learning how to use asyncio, Web Developers can create more responsive and efficient web applications that can handle a high volume of traffic.

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DevOps Engineer

DevOps Engineers work to bridge the gap between software development and operations. They use a variety of tools and techniques to automate the software development and deployment process. Python is a popular programming language for DevOps due to its versatility and wide range of libraries. This course provides DevOps Engineers with the skills they need to implement concurrency in their Python scripts and tools. By learning how to use threading, multiprocessing, and asyncio, DevOps Engineers can improve the efficiency and reliability of their DevOps pipelines.

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Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. They use statistical and mathematical techniques to extract meaningful insights from data. Python is a popular programming language for data analysis due to its large ecosystem of data science libraries. This course provides Data Analysts with the skills they need to implement concurrency in their Python data analysis code. By learning how to use threading and multiprocessing, Data Analysts can speed up their data processing and analysis tasks, leading to faster and more accurate insights.

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Machine Learning Engineer

Machine Learning Engineers design and develop machine learning models. They use statistical and mathematical techniques to train models that can learn from data and make predictions. Python is a popular programming language for machine learning due to its large ecosystem of machine learning libraries. This course provides Machine Learning Engineers with the skills they need to implement concurrency in their Python machine learning code. By learning how to use threading and multiprocessing, Machine Learning Engineers can speed up their model training and evaluation tasks, leading to faster and more accurate models.

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Cloud Engineer

Cloud Engineers design and manage cloud computing infrastructure. They use a variety of tools and technologies to create and maintain cloud-based applications and services. Python is a popular programming language for cloud computing due to its versatility and wide range of libraries. This course provides Cloud Engineers with the skills they need to implement concurrency in their Python cloud computing code. By learning how to use threading, multiprocessing, and asyncio, Cloud Engineers can improve the performance and scalability of their cloud-based applications and services.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Python is a popular programming language for quantitative finance due to its large ecosystem of financial data analysis libraries. This course provides Quantitative Analysts with the skills they need to implement concurrency in their Python quantitative finance code. By learning how to use threading and multiprocessing, Quantitative Analysts can speed up their data processing and analysis tasks, leading to faster and more accurate investment decisions.

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Systems Engineer

Systems Engineers design, develop, and maintain complex systems. They use a variety of tools and technologies to create systems that meet the needs of users. Python is a popular programming language for systems engineering due to its versatility and wide range of libraries. This course provides Systems Engineers with the skills they need to implement concurrency in their Python systems engineering code. By learning how to use threading, multiprocessing, and asyncio, Systems Engineers can improve the performance and reliability of their systems.

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Business Analyst

Business Analysts analyze business processes and systems to identify opportunities for improvement. They use a variety of tools and techniques to gather and analyze data, and to develop recommendations for improvement. Python is a popular programming language for business analysis due to its versatility and wide range of data analysis libraries. This course provides Business Analysts with the skills they need to implement concurrency in their Python business analysis code. By learning how to use threading and multiprocessing, Business Analysts can speed up their data processing and analysis tasks, leading to faster and more accurate recommendations for improvement.

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Product Manager

Product Managers work with engineers and designers to develop and launch new products. They use a variety of tools and techniques to gather and analyze user feedback, and to make decisions about product development. Python is a popular programming language for product management due to its versatility and wide range of data analysis libraries. This course provides Product Managers with the skills they need to implement concurrency in their Python product management code. By learning how to use threading and multiprocessing, Product Managers can speed up their data processing and analysis tasks, leading to faster and more accurate product development decisions.

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Technical Writer

Technical Writers create and maintain technical documentation, such as user manuals, white papers, and training materials. They use a variety of tools and techniques to gather and organize information, and to write clear and concise documentation. Python is a popular programming language for technical writing due to its versatility and wide range of documentation generation libraries. This course provides Technical Writers with the skills they need to implement concurrency in their Python technical writing code. By learning how to use threading and multiprocessing, Technical Writers can speed up their documentation generation tasks, leading to faster and more accurate documentation.

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Project Manager

Project Managers plan and execute projects, and they work with stakeholders to ensure that projects are completed on time and within budget. Python is a popular programming language for project management due to its versatility and wide range of project management libraries. This course provides Project Managers with the skills they need to implement concurrency in their Python project management code. By learning how to use threading and multiprocessing, Project Managers can speed up their project planning and execution tasks, leading to faster and more successful project outcomes.

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Marketing Analyst

Marketing Analysts analyze marketing data to identify trends and patterns. They use statistical and mathematical techniques to extract meaningful insights from data. Python is a popular programming language for marketing analysis due to its large ecosystem of data science libraries. This course provides Marketing Analysts with the skills they need to implement concurrency in their Python marketing analysis code. By learning how to use threading and multiprocessing, Marketing Analysts can speed up their data processing and analysis tasks, leading to faster and more accurate insights.

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Financial Analyst

Financial Analysts analyze financial data to identify trends and patterns. They use statistical and mathematical techniques to extract meaningful insights from data. Python is a popular programming language for financial analysis due to its large ecosystem of financial data analysis libraries. This course provides Financial Analysts with the skills they need to implement concurrency in their Python financial analysis code. By learning how to use threading and multiprocessing, Financial Analysts can speed up their data processing and analysis tasks, leading to faster and more accurate insights.

See salaries and explore the career path for Financial Analyst