Practice Exam, Video Course
Concurrent and Parallel Programming in Python

Concurrent and Parallel Programming in Python

0.0 (140 ratings)
1,200 Learners
Take Free Test

Concurrent and Parallel Programming in Python

Concurrency in Python means running multiple tasks in a way that they seem to progress together. It doesn’t mean they all happen at once, but the computer manages them so smoothly that they appear to. This is helpful when working with tasks like chat apps, where messages are sent and received at the same time, or when many things need attention without delay.

Parallelism in Python means doing many things truly at the same moment. This happens by using multiple CPU cores so that heavy tasks can be split and worked on together. For example, when processing thousands of images, each core can work on some images in parallel, making the process much faster. Together, concurrency and parallelism are powerful ways to boost performance and handle modern computing demands effectively.


Who should take the Exam?

This exam is ideal for:

  • Software Developers
  • Data Scientists
  • Machine Learning Engineers
  • Backend Engineers
  • DevOps Engineers
  • Cloud Specialists
  • System Programmers

Skills Required

  • Python programming language
  • Problem-solving skills
  • Algorithms and data structures
  • Logical and analytical thinking
  • Multithreading and multiprocessing

Course Outline

  • Domain 1 - Introduction to Concurrency and Parallelism
  • Domain 2 - Threads in Python
  • Domain 3 - Async Programming
  • Domain 4 - Multiprocessing in Python
  • Domain 5 - Concurrent Futures
  • Domain 6 - Synchronization Techniques
  • Domain 7 - Practical Applications
  • Domain 8 - Best Practices and Patterns


 

Key Features

Accredited Certificate

Industry-endorsed certificates to strengthen your career profile.

Instant Access

Start learning immediately with digital materials, no delays.

Unlimited Retakes

Practice until you’re fully confident, at no additional charge.

Self-Paced Learning

Study anytime, anywhere, on laptop, tablet, or smartphone.

Expert-Curated Content

Courses and practice exams developed by qualified professionals.

24/7 Support

Support available round the clock whenever you need help.

Interactive & Engaging

Easy-to-follow content with practice exams and assessments.

Over 1.5M+ Learners Worldwide

Join a global community of professionals advancing their skills.

Concurrent and Parallel Programming in Python FAQs

Yes, cloud services often rely on concurrency and parallelism to handle distributed workloads.

Absolutely. It helps in building fast, scalable APIs and servers that handle many requests smoothly.

No, but having a multi-core processor makes parallel execution more effective.

Yes, but using multiprocessing or libraries like concurrent.futures helps overcome this limitation.

Yes, the course covers async programming in detail.

It is ideal for Python developers, data scientists, and professionals working with performance-heavy applications.

No, intermediate Python knowledge is enough, but you should understand basics of coding and problem-solving.

Finance, healthcare, AI/ML, cloud computing, web development, and gaming industries.

Concurrency handles many tasks at once by scheduling, while parallelism runs tasks at the same time on multiple processors.

Yes, parallelism is widely used in handling large datasets and training machine learning models efficiently.