The Binary Tree Algorithms Crash Course in Python

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The Binary Tree Algorithms Crash Course in Python , Learn the essential binary tree traversal algorithms and solve common interview problems.

What you”ll learn:

  • Solve common technical interview problems that deal with binary tree data structures
  • Implement the essential binary tree traversal algorithms in Python
  • Visualize the mechanics of binary tree algorithms
  • Analyze the Big-O time and space complexity of binary tree algorithms

Course Description

Welcome to the Binary Tree Algorithms Crash Course in Python. This mini-course is designed to give you the background and tools you need to start solving Binary Tree interview problems. We’ll give you all the essential knowledge you need in less than three hours. This course is designed especially for beginners, so you don’t need to know any data structures and algorithms to get started.

This crash course is a great fit for you if:

  • you are a beginner to data structures and algorithms
  • you need a structured system to learn about binary trees in a short amount of time
  • you don’t like wasting your time drilling random, disconnected leetcode problems
  • you want to build a robust visual and mental understanding of algorithms
  • you want to be taught by a former Google engineer with years of professional teaching experience

The course features video explanations for every piece of curriculum. This includes conceptual video animations and Python implementation walkthroughs for every problem. We will start simple and progress to more complex problems to ensure that you understand every step of the way.

During the course we’ll learn how to:

  • master the two essential algorithms that serve as the backbone to virtually every Binary Tree problem
  • apply these algorithms to solve common technical interview problems
  • visualize the thought-process behind each problem
  • analyze the Big-O time and space complexity of solutions

This course is the most efficient way to learn about Binary Tree algorithms.

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