Algorithms Online Course

 
Chapter 1: Introduction to Algorithms
Provides an overview of algorithms and their role in computer science and software engineering. It explains the concept of asymptotic notation and big-O complexity, which are used to analyze and compare the efficiency of algorithms. The chapter also covers common algorithms and data structures, including sorting algorithms, searching algorithms, graph algorithms, and data structures such as arrays, linked lists, and trees. The chapter concludes by emphasizing the importance of learning algorithms and data structures in computer science and software engineering.

Chapter 2: Data Structures
This section covers the basics of data structures, including arrays, linked lists, stacks, queues, trees, and graphs. Topics include data structure implementation, time and space complexity, and data structure manipulation.

Chapter 3: Sorting Algorithms
Sorting algorithms: This section covers the various sorting algorithms, including bubble sort, insertion sort, quick sort, merge sort, and heap sort. Topics include time and space complexity, sorting algorithm implementation, and sorting algorithm analysis.

Chapter 4: Searching Algorithms
Searching algorithms: This section covers the various searching algorithms, including linear search, binary search, and hashing. Topics include time and space complexity, searching algorithm implementation, and searching algorithm analysis.

Chapter 5: Graph Algorithms
Graph algorithms: This section covers graph algorithms, including depth-first search, breadth-first search, minimum spanning trees, and shortest path algorithms. Topics include graph representation, graph traversal, and graph analysis.

Chapter 6: Dynamic programming
This section covers dynamic programming, including the principles of dynamic programming, common dynamic programming algorithms, and dynamic programming implementation.

Chapter 7: Greedy algorithms
This section covers greedy algorithms, including the principles of greedy algorithms, common greedy algorithms, and greedy algorithm implementation.

Chapter 8: Backtracking
This section covers backtracking, including the principles of backtracking, common backtracking algorithms, and backtracking implementation.

Chapter 9: String algorithms
This section covers string algorithms, including string manipulation, pattern matching, and string matching algorithms.

Chapter 10: Advanced topics
This section covers advanced topics, including computational geometry, algorithms on trees, and algorithms for parallel and distributed systems.

Chapter 11: Practice problems and coding exercises
This section provides hands-on experience with algorithms and data structures through coding exercises and problem sets.

Chapter 12: Final project
This section provides an opportunity for students to apply their knowledge of algorithms and data structures to a real-world problem.