Data-Structures-and-Algorithms-with-Python.

Here is a detailed syllabus for learning Data Structures and Algorithms (DSA) with Python:

1)Introduction to Python programming basics

Installing Python and setting up the development environment Python data types, variables, and operators Control structures (if, else, for, while) Functions, modules, and packages

2)Arrays and Strings

Introduction to arrays and strings Operations on arrays and strings (traversal, searching, sorting, merging, reversing) Applications of arrays and strings (matrix manipulation, string manipulation, dynamic programming)

3)Linked Lists

Introduction to linked lists Types of linked lists (singly linked list, doubly linked list, circular linked list) Operations on linked lists (insertion, deletion, traversal) Applications of linked lists (stacks, queues, linked list implementation of other data structures)

4)Stacks and Queues

Introduction to stacks and queues Operations on stacks and queues (push, pop, peek, traversal) Implementing stacks and queues using arrays and linked lists Applications of stacks and queues (expression evaluation, backtracking, tree traversal)

5)Trees and Binary Search Trees

Introduction to trees Types of trees (binary tree, binary search tree, AVL tree, B-tree, Red-Black tree) Operations on trees (insertion, deletion, traversal) Tree algorithms (height, diameter, lowest common ancestor) Applications of trees (sorting, searching, Huffman coding)

6)Graphs

Introduction to graphs Types of graphs (directed, undirected, weighted, unweighted) Graph representations (adjacency matrix, adjacency list) Graph algorithms (breadth-first search, depth-first search, shortest path algorithms) Applications of graphs (networks, social media analysis, recommendation systems)

7)Sorting and Searching Algorithms

Introduction to sorting and searching algorithms Types of sorting algorithms (bubble sort, selection sort, insertion sort, quicksort, mergesort, heapsort) Types of searching algorithms (linear search, binary search, interpolation search) Comparison of sorting and searching algorithms based on time complexity and space complexity

8)Dynamic Programming

Introduction to dynamic programming Principles of dynamic programming (overlapping subproblems, optimal substructure) Memoization and tabulation techniques Applications of dynamic programming (knapsack problem, longest common subsequence, matrix chain multiplication)

9)Greedy Algorithms

Introduction to greedy algorithms Principles of greedy algorithms (optimal substructure, greedy choice property) Examples of greedy algorithms (minimum spanning tree, Dijkstra's shortest path algorithm, Huffman coding)

10)Backtracking

Introduction to backtracking Principles of backtracking (recursive solution, decision tree) Examples of backtracking (n-queens problem, Sudoku solver)

This syllabus covers the fundamental topics of DSA with Python. However, the syllabus can be expanded further based on your interests and the level of expertise you want to achieve in DSA.