Data structures and algorithms tutorial from basic to advanced

Introduction to Data Structures and Algorithms:
Overview of data structures and algorithms
Importance and applications in programming
Understanding time and space complexity
Basic Data Structures:
Arrays and dynamic arrays
Linked lists (singly linked, doubly linked)
Stacks and queues
Hash tables and hash maps
Basic Algorithms:
Searching algorithms (linear search, binary search)
Sorting algorithms (bubble sort, selection sort, insertion sort)
Recursion and recursive algorithms
Advanced Data Structures:
Trees (binary trees, binary search trees, AVL trees, B-trees)
Heaps (binary heaps, priority queues)
Graphs (undirected, directed, weighted, unweighted)
Advanced Algorithms:
Advanced searching algorithms (binary search trees, depth-first search, breadth-first search)
Advanced sorting algorithms (merge sort, quick sort, heap sort)
Dynamic programming and memoization
Greedy algorithms
String Algorithms:
String searching algorithms (Naive string matching, Knuth-Morris-Pratt algorithm)
String manipulation and matching algorithms
Regular expressions and pattern matching
Graph Algorithms:
Shortest path algorithms (Dijkstra's algorithm, Bellman-Ford algorithm)
Minimum spanning tree algorithms (Prim's algorithm, Kruskal's algorithm)
Topological sorting
Graph traversal algorithms (DFS, BFS)
Advanced Topics:
Advanced data structures (trie, Fenwick tree, segment tree)
Approximation algorithms
Network flow algorithms (Ford-Fulkerson algorithm, Edmonds-Karp algorithm)
NP-complete problems and approximation algorithms
Algorithm Analysis and Optimization:
Analyzing algorithm complexity (time complexity, space complexity)
Big O notation and asymptotic analysis
Optimizing algorithms (loop unrolling, caching, memoization)
Practical Applications:
Solving coding interview problems
Algorithmic problem-solving techniques
Real-world examples and case studies
Implementing algorithms in programming languages like Python, Java, C++
Data Structures and Algorithms in Practice:
Using data structures and algorithms in software development projects
Design patterns and algorithmic approaches
Code optimization and best practices
Tools and libraries for implementing data structures and algorithms

Thank you.