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. Explore sorting algorithms with simple iterative sorts, followed by Divide and Conquer algorithms. Use the course visualizations to understand the performance.
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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. 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. Time complexity is threaded throughout the course within all the data structures and algorithms.
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). AVL trees are a subgroup of BSTs and thus inherit all the properties and constraints from BSTs. Additionally, AVLs incorporate rotations that are triggered when the tree is mutated and becomes out of balance. (2-4) trees are a subgroup of B-Trees and are non-binary trees with more than 2 children. 2-4 defines the range of children that exists in the trees. However, these trees are extremely flexible and allow the nodes to shrink and grow as needed to store more data. With this flexibility comes more issues to handle, like overflow and underflow which require more intense techniques to resolve the issues.
As you enter the algorithm portion of the course, you begin with a couple of familiar iterative sorting algorithms: Bubble and Selection. There are optimizations that can be included in the standard Bubble sort to make it more adaptive in sorting. There is also a derivation of bubble sort, called Cocktail Shaker sort, that puts new a spin on the basic algorithm. Insertion sort is the last iterative sort that is investigated in this group of sort algorithms. Divide & Conquer sorting algorithms are examined and are broken into two groups: comparison sorts and non-comparison sorts. The two comparison sorts are Merge and In-place Quick sort. Both are recursive and focus on subdividing the array into smaller portions. LSD Radix sort is the non-comparison sort that deconstructs an integer number and examines the digits. All algorithms are analyzed for stability, memory storage, adaptiveness, and time complexity.
The course design has several components and is built around modules. A module consists of a series of short (3-5 minute) instructional videos. In between the videos, there are textual frames with additional content information for clarification, as well as video errata dropdown boxes. All modules include an Exploratory Lab that incorporates a Visualization Tool specifically designed for this course. The lab includes discovery questions that lead you towards delving deeper into the efficiency of the data structures and examining the edge cases. This is followed by a set of comprehension questions on topics covered in the module that count for 10% of your grade. The modules end with Java coding assignments which are 60% of your grade. Lastly, you'll complete a course exam, which counts for the remaining 30% of your grade.
This course is part of the Data Structures and Algorithms Professional Certificate.
What you'll learn
- Improve Java programming skills by implementing AVLs and sorting algorithms
- Study techniques for restoring balance in AVL and (2-4) trees
- Distinguish when to apply single and double rotations in AVLs
- Investigate complex (2-4) trees that exhibit underflow and overflow problems
- Demonstrate the appropriate use of promotion, transfer and fusion in (2-4) trees
- 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
- Study the time complexity for the AVLs, (2-4) Trees and sorting algorithms
Syllabus
Module 0: Introduction and Review
Review of important Java principles involved in object-oriented design
The Iterator & Iterable design patterns, and the Comparable & Comparator interfaces
Basic “Big-Oh” notation and asymptotic analysis
Module 8: AVL Trees
Explore the AVL tree subgroup from Binary Search Trees (BST) and their distinguishing properties
Discover the self-balancing of AVL trees, and which rotations are used to balance
Implement the entire AVL tree data structure, and examine its performance
Module 9: (2-4) Trees
Extend understanding of tree structures beyond binary trees to a more complex model
Study the properties of (2-4) trees, and how operations maintain those properties
Recognize when overflow and underflow situations arise within the (2-4) tree, and how to resolve those situations with promotion, fusion and transfer
Module 10: Iterative Sorting Algorithms
Understand and implement four basic iterative, comparison sorting algorithms: Bubble Sort, Insertion Sort, Selection Sort and Cocktail Shaker Sort
Examine the characteristics of sorting algorithms: Stability, Adaptation and Memory
Implement optimizations of these algorithms to yield better performance
Analyze the time complexity of each of the algorithms
Module 11: Divide & Conquer Sorting Algorithms
Introduction to the Divide & Conquer approach to sorting algorithms
Implement and comprehend each of the divide & conquer algorithms presented: Merge Sort, In-Place Quick Sort and LSD Radix sort
Examine the stability and memory usage of these sorting algorithms
Explore the novel approach that LSD Radix sort uses to solve the sorting dilemma
