11.1 Introduction

  • Here, we concentrate on some key aspects of computer science thinking that go beyond programming fundamentals
  • Focus on performance issues is a nice way to transition into AI and big data techniques that place extraordinary performance demands on a system’s resources

11.1 Introduction (cont.)

  • Recursive functions (or methods) call themselves, either directly or indirectly through other functions (or methods)
    • Can help solve problems more naturally when an iterative solution is not apparent
  • We'll look at searching and sorting arrays and other sequences
    • Fascinating problems, because no matter what algorithms you use, the final result is the same
    • Choose algorithms that perform "the best"—most likely, the ones that run the fastest or use the least memory
  • For big data applications, you’ll want to choose algorithms that are easy to parallelize
    • Put lots of processors to work simultaneously

11.1 Introduction (cont.)

  • Intimate relationship between algorithm design and performance
  • Simplest and most apparent algorithms often perform poorly and that developing more sophisticated algorithms can lead to superior performance
  • Big O notation concisely classifies algorithms by how hard they have to work to get the job done
    • Helps you compare the efficiency of algorithms.
  • Optional section: Develop an animated visualization of the selection sort so you can see it "in action."
    • Great for understanding how algorithms work

©1992–2020 by Pearson Education, Inc. All Rights Reserved. This content is based on Chapter 5 of the book Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud.

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