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)

Session Details

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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