SkillSoft Explore Course – Lambers, Inc.

Aspire Python Novice to Pythonista Pythonista Track 2: Python Apprentice

This 13-video course explores the theory of graph and tree data structures in Python. Learners will examine a specific type of tree: the binary search tree, its structures and properties. You will then observe how to execute common tasks in binary tree; examine the binary search algorithm; and review data structures of linked lists, stacks, and queues. Next, learners will examine how a binary tree structure offers several applications that cannot be done by using stacks or queues. The course demonstrates different depth first traversals, including pre-order, in-order traversals, and post-order traversals. Explore graphs, which are data structures used to model relationships, and different representations of a graph, and learn to model a vertex. Learners continue by observing how to represent an adjacency list as a graph, and examining the adjacency matrix, the adjacency list, and the adjacency set. Then you will explore graph traversal algorithms, including the topological sort. Finally, learn how to traverse through each of the vertices in a graph.

Asset ID	it_pydsapdj_05_enus
Course Type	Video Course
Course Category	Python Novice to Pythonista

Objectives
Data Structures & Algorithms in Python: Trees & Graphs discover the key concepts covered in this course describe how a sorted list of elements can be searched efficiently using a binary search recognize what trees and binary trees are and recall the properties of a binary search tree summarize how insert and lookup operations occur in a BST identify the minimum and maximum values in a BST, identify the greatest depth of the data structure, and calculate the sum of values from the root to a leaf node recall the different ways in which to traverse a BST and describe the method to perform a breadth first traversal summarize the pre-order and in-order depth first traversal techniques for a BST describe the post-order traversal technique for a BST identify the components that make up a graph and recognize the different terms associated with these data structures recognize the different ways to represent graphs and describe the structure of an adjacency matrix summarize the representation of a graph in the form of an adjacency list and adjacency set traverse over the nodes in a graph using the topological sort algorithm summarize the properties of a binary search tree and list three different ways in which a graph can be represented

Objectives

Data Structures & Algorithms in Python: Trees & Graphs

discover the key concepts covered in this course
describe how a sorted list of elements can be searched efficiently using a binary search
recognize what trees and binary trees are and recall the properties of a binary search tree
summarize how insert and lookup operations occur in a BST
identify the minimum and maximum values in a BST, identify the greatest depth of the data structure, and calculate the sum of values from the root to a leaf node
recall the different ways in which to traverse a BST and describe the method to perform a breadth first traversal
summarize the pre-order and in-order depth first traversal techniques for a BST
describe the post-order traversal technique for a BST
identify the components that make up a graph and recognize the different terms associated with these data structures
recognize the different ways to represent graphs and describe the structure of an adjacency matrix
summarize the representation of a graph in the form of an adjacency list and adjacency set
traverse over the nodes in a graph using the topological sort algorithm
summarize the properties of a binary search tree and list three different ways in which a graph can be represented