Imagine you are developing a Python application that automates data handling for a research team. The application has three main tasks: first, it downloads several large CSV files from different remote servers; second, it processes each downloaded file to clean and transform the data; third, it generates summary statistics and plots from the processed data and saves the results to disk. Each file is independent—downloading one does not depend on another, and processing or analyzing one file does not depend on others.

Suppose your application handles several independent files. For each file, you need to download it, process it, and generate statistics and plots. Which of the following tasks could be executed concurrently for different files?

A beginner-friendly course introducing the concepts, techniques, and practical applications of multithreading and multiprocessing in Python. Learn how to write concurrent programs, manage threads and processes, and solve real-world problems using parallel execution.

Explore the foundational concepts of concurrency, parallelism, and the differences between threads and processes in Python.

Learn how to create, start, and manage threads in Python, and understand thread synchronization.

Explore how to create and manage processes in Python, and understand inter-process communication.

Delve into advanced concurrency patterns, performance considerations, and best practices for writing robust concurrent code.

Challenge: Identify Concurrency Opportunities