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Kurs

Writing Efficient Python Code

MedelnivåKunskapsnivå
Uppdaterad 2026-01
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
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PythonProgramming
4 tim
15 videor
52 Övningar
4,000 XP
150K+
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Kursbeskrivning

As a Data Scientist, the majority of your time should be spent gleaning actionable insights from data -- not waiting for your code to finish running. Writing efficient Python code can help reduce runtime and save computational resources, ultimately freeing you up to do the things you love as a Data Scientist. In this course, you'll learn how to use Python's built-in data structures, functions, and modules to write cleaner, faster, and more efficient code. We'll explore how to time and profile code in order to find bottlenecks. Then, you'll practice eliminating these bottlenecks, and other bad design patterns, using Python's Standard Library, NumPy, and pandas. After completing this course, you'll have the necessary tools to start writing efficient Python code!The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos. The course glossary can be found on the right in the resources section. To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

Förkunskapskrav

Data Types in PythonPython Toolbox
1

Foundations for efficiencies

In this chapter, you'll learn what it means to write efficient Python code. You'll explore Python's Standard Library, learn about NumPy arrays, and practice using some of Python's built-in tools. This chapter builds a foundation for the concepts covered ahead.
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2

Timing and profiling code

In this chapter, you will learn how to gather and compare runtimes between different coding approaches. You'll practice using the line_profiler and memory_profiler packages to profile your code base and spot bottlenecks. Then, you'll put your learnings to practice by replacing these bottlenecks with efficient Python code.
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Writing Efficient Python Code
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