Understanding the importance of data structures and algorithms in computer science. Overview of various data structures and their applications. Introduction to Big O notation and its significance in analyzing algorithms.

Understanding the significance of arrays as a basic data structure. Learning how to access and manipulate array elements. Understanding array indexing and slicing. Analyzing the time and space complexity of array operations.

Understanding the concept of linked lists and their advantages over arrays. Learning how to create, traverse, and manipulate linked lists. Understanding the different types of linked lists and their applications.

Understanding the concept of stacks and their applications. Learning how to implement stacks using arrays and linked lists. Understanding the concept of queues and their applications. Learning how to implement queues using arrays and linked lists.

Understanding the concept of trees and their applications. Learning how to represent trees using arrays and linked lists. Understanding the different types of trees and their applications.

Understanding the concept of hash tables and their applications. Learning how to implement hash tables using arrays and linked lists. Understanding the significance of hash functions and their role in hash tables.

Understanding the importance of sorting algorithms and their applications. Learning various sorting algorithms, including bubble sort, selection sort, insertion sort, merge sort, and quicksort. Understanding the time and space complexity of sorting algorithms.

Understanding the importance of searching algorithms and their applications. Learning various searching algorithms, including linear search and binary search. Understanding the time and space complexity of searching algorithms.

Understanding the concept of graphs and their applications. Learning various graph algorithms, including depth-first search, breadth-first search, Dijkstra's algorithm, and Bellman-Ford algorithm. Understanding the significance of graph algorithms in solving real-world problems.

Understanding advanced data structures, such as heaps, trie trees, and B-trees. Learning advanced algorithms, such as dynamic programming and greedy algorithms. Understanding the significance of advanced data structures and algorithms in solving complex problems.

Reviewing the key concepts learned throughout the course. Completing a final project to apply the knowledge and skills gained during the course. Receiving feedback and support from peers and instructors.