We start this module by considering priority queues which are used to efficiently schedule jobs, either in the context of a computer operating system or in real life, to sort huge files, which is the most important building block for any Big Data processing algorithm, and to efficiently compute shortest paths in graphs, which is a topic we will cover in our next course. Module 3: Priority Queues and Disjoint Set Union It is used both for Dynamic Arrays analysis and will also be used in the end of this course to analyze Splay trees. Amortized analysis is very often used to analyse performance of algorithms when the straightforward analysis produces unsatisfactory results, but amortized analysis helps to show that the algorithm is actually efficient. Here, we also discuss amortized analysis: a method of determining the amortized cost of an operation over a sequence of operations. In this module, we discuss Dynamic Arrays: a way of using arrays when it is unknown ahead-of-time how many elements will be needed. Module 2: Dynamic Arrays and Amortized Analysis Once you’ve completed this module, you will be able to implement any of these data structures, as well as have a solid understanding of the costs of the operations, as well as the tradeoffs involved in using each data structure.
Next, we look at trees: examples of how they’re used in Computer Science, how they’re implemented, and the various ways they can be traversed. From there, we build up two important data structures: stacks and queues.
We start this module by looking in detail at the fundamental building blocks: arrays and linked lists. In this module, you will learn about the basic data structures used throughout the rest of this course. We look forward to seeing you in this course! We know it will make you a better programmer. What are good strategies to keep a binary tree balanced?.
You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. In this course, part of the Algorithms and Data Structures MicroMasters program, we consider the common data structures that are used in various computational problems. A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently.