Data Structures & Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms

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  • Overview
  • Course Content
  • Requirements & Materials

Data Structures & Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms

Course Description

This Data Structures & Algorithms course completes the data structures portion presented in the sequence of courses with self-balancing AVL and (2-4) trees. It also begins the algorithm portion in the sequence of courses as you will investigate and explore the two more complex data structures: AVL and (2-4) trees. Both of these data structures focus on self-balancing techniques that will ensure all operations are O(log n). Time complexity is threaded throughout the course within all the data structures and algorithms and a short Java review is presented on topics relevant to new data structures covered in this course. The course does require prior knowledge of Java, object-oriented programming, and linear and nonlinear data structures.

Course Content


(2-4) TREES



Requirements & Materials


  • Basic knowledge of the Java programming language, object-oriented principles, and the following abstract data types: Binary Search Trees, Heaps, and Hashmaps.


  • Internet connection (DSL, LAN, or cable connection desirable)

Who Should Attend

This course is designed for anyone who wants to learn more complex tree data structures, AVL and (2-4) trees; investigate the balancing techniques found in both tree types; implement these techniques in AVL operations; or explore sorting algorithms with simple iterative sorts, followed by Divide and Conquer algorithms. You will use the course visualizations to understand the performance. 

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What You Will Learn

  • Java programming skills by implementing AVLs and sorting algorithms
  • Study techniques for restoring balance in AVL and (2-4) trees
  • When to apply single and double rotations in AVLs
  • Complex (2-4) trees that exhibit underflow and overflow problems
  • The appropriate use of promotion, transfer, and fusion in (2-4) trees
  • The time complexity for the AVLs, (2-4) Trees, and sorting algorithms
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How You Will Benefit

  • Implement basic iterative sorting algorithms: Bubble, Insertion, and Selection.
  • Explore optimizations to improve efficiency, including Cocktail Shaker Sort.
  • Contemplate two Divide & Conquer comparison sorting algorithms Merge and Quick Sort.
  • Consider one non-comparison Divide & Conquer algorithm: LSD Radix Sort.
  • Analyze the stability, memory usage, and adaptations of all sorting algorithms presented.
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