Python Data Structures Training

• Recursion
• Big O
• How to implement, and use various Data Structures?
• How to implement and use various Algorithms?
• Space and Time Complexity of Algorithms and Data Structures

• Recorded Videos After Training
• Digital Learning Material
• Course Completion Certificate
• 24x7 After Training Support

• Computer science graduates
• Self-taught programmers, who want to learn Data Structures and Algorithms
• Python programmers, who want to learn data analysis with Python

• Candidates should have basic knowledge of Python

• Multisoft Systems provides a globally recognized training certificate after successful completion of Python Data Structures Training Certification Course.

Module 1: Introduction

• Curriculum Walkthrough
• What are Data Structures?
• What is an algorithm?
• Why are Data Structures and Algorithms important?
• Types of Data Structures
• Types of Algorithms
• Python Programming For Everyone
• Python Programming
• Introduction to DS and Algorithms

Module 2: Recursion

• What is Recursion
• Why do we need recursion?
• How Recursion works? 
• Recursive vs. Iterative Solutions
• When to use/avoid Recursion?
• How to write Recursion in 3 steps?
• How to find Fibonacci numbers using Recursion?

Module 3: Challenging Recursion Problems

• Important Note!
• Power
• Factorial
• Product of Array
• Recursive Range
• fib
• SOLUTIONS PART 1
• Reverse

Module 4: Big O Notation

• Analogy and Time Complexity
• Big O, Big Theta and Big Omega
• Time complexity examples
• Space Complexity
• Drop the Constants and the non-dominant terms
• Add vs. Multiply
• How to measure the codes using Big O?
• How to find time complexity for Recursive calls?
• How to measure Recursive Algorithms that make multiple calls?
• Time Complexities

Module 5: Arrays

• What is an Array
• Types of Array
• Arrays in Memory
• Create an Array
• Insertion Operation
• Traversal Operation
• Accessing an element of Array
• Searching for an element in Array
• Deleting an element from Array
• Time and Space complexity of One Dimensional Array
• One Dimensional Array Practice
• Create Two Dimensional Array
• Insertion - Two Dimensional Array
• Accessing an element of Two Dimensional Array
• Traversal - Two Dimensional Array
• Searching for an element in Two Dimensional Array
• Deletion - Two Dimensional Array
• Time and Space complexity of Two Dimensional Array
• When to use/avoid array

Module 6: Python Lists

• What is list? How to create it?
• Accessing/Traversing a list
• Update/Insert a List
• Slice/Delete from a List
• Searching for an element in a List
• List Operations/Functions
• Lists and strings
• Common List pitfalls and ways to avoid them
• Lists vs. Arrays
• Time and Space Complexity of List

Module 7: Challenging Array/List Problems

• Middle Function
• Solution to Middle Function
• 2D Lists
• Solution to 2D Lists
• Best Score
• Solution to Best Score
• Missing Number
• Solution to Missing Number
• Duplicate Number
• Solution to Duplicate Number
• Pairs
• Solution to Pairs

Module 8: Tuples

• What is a Tuple? How to create it? 
• Tuples in Memory / Accessing an element of Tuple
• Traversing a Tuple
• Search for an element in Tuple
• Tuple Operations/Functions
• Tuple vs. List
• Time and Space complexity of Tuples

Module 9: Linked List

• What is a Linked List?
• Linked List vs. Arrays
• Types of Linked List
• Linked List in the Memory
• Creation of Singly Linked List
• Insertion in Singly Linked List in Memory
• Insertion in Singly Linked List Algorithm
• Insertion Method in Singly Linked List
• Traversal of Singly Linked List
• Search for a value in Single Linked List
• Deletion of node from Singly Linked List
• Deletion Method in Singly Linked List
• Deletion of entire Singly Linked List
• Time and Space Complexity of Singly Linked List
• Time Complexity of Linked List vs. Arrays

Module 10: Circular Singly Linked List

• Creation of Circular Singly Linked List
• Creation of Circular Singly Linked List
• Insertion Algorithm in Circular Singly Linked List
• Insertion method in Circular Singly Linked List
• Traversal of Circular Singly Linked List
• Searching a node in Circular Singly Linked List
• Deletion of a node from Circular Singly Linked List
• Deletion Algorithm in Circular Singly Linked List
• Delete Method in Circular Singlu Linked List
• Deletion of entire Circular Singly Linked List
• Time and Space Complexity of Circular Singly Linked List

Module 11: Stack

• What is a Stack?
• Stack Operations
• Create Stack using List without size limit
• Operations on Stack using List (push, pop, peek, isEmpty, Delete)
• Create Stack with limit (pop, push, peek, isFull, isEmpty, delete)
• Create Stack using Linked List
• Operation on Stack using Linked List (pop, push, peek, isEmpty, delete)
• Time and Space Complexity of Stack using Linked List
• When to use/avoid Stack

Module 12: Queue

• What is Queue?
• Queue using Python List - no size limit
• Queue using Python List - no size limit , operations (enqueue, dequeue, peek)
• Circular Queue - Python List
• Circular Queue - Python List, Operations (enqueue, dequeue, peek, delete)
• Queue - Linked List
• Queue - Linked List, Operations (Create, Enqueue)
• Queue - Linked List, Operations (Dequeue(), isEmpty, Peek)
• Time and Space complexity of Queue using Linked List
• List vs. Linked List Implementation
• Collections Module
• Queue Module
• Multiprocessing module

Module 13: Tree/Binary tree

• What is a Tree?
• Why tree?
• Tree Terminology
• How to create basic tree in Python?
• Binary Tree
• Types of Binary Tree
• Binary Tree Representation
• Create Binary Tree (Linked List)
• PreOrder Traversal Binary Tree (Linked List)
• InOrder Traversal Binary Tree (Linked List)
• PostOrder Traversal Binary Tree (Linked List)
• LevelOrder Traversal Binary Tree (Linked List)
• Searching for a node in Binary Tree (Linked List)
• Inserting a node in Binary Tree (Linked List)
• Delete a node from Binary Tree (Linked List)
• Delete entire Binary Tree (Linked List)
• Create Binary Tree (Python List)
• Insert a value Binary Tree (Python List)
• Search for a node in Binary Tree (Python List)
• PreOrder Traversal Binary Tree (Python List)
• InOrder Traversal Binary Tree (Python List)
• PostOrder Traversal Binary Tree (Python List)
• Level Order Traversal Binary Tree (Python List)
• Delete a node from Binary Tree (Python List)
• Delete Entire Binary Tree (Python List)
• Linked List vs. Python List Binary Tree

Module 14: Binary Search Tree

• What is Binary Search Tree? Why do we need it?
• Create a Binary Search Tree
• Insert a node to BST
• Traverse BST
• Search in BST
• Delete a node from BST
• Delete entire BST
• Time and Space complexity of BST

Module 15: AVL Tree

• What is an AVL tree?
• Why AVL Tree?
• Common Operations on AVL Trees
• Insert a node in AVL (Left Left Condition)
• Insert a node in AVL (Left Right Condition)
• Insert a node in AVL (Right Right Condition)
• Insert a node in AVL (Right Left Condition)
• Insert a node in AVL (all together)
• Insert a node in AVL (method)
• Delete a node from AVL (LL, LR, RR, RL)
• Delete a node from AVL (all together)
• Delete a node from AVL (method)
• Delete entire AVL
• Time and Space complexity of AVL Tree

Module 16: Binary Heap

• What is Binary Heap? Why do we need it?
• Common operations (Creation, Peek, sizeofheap) on Binary Heap
• Insert a node in Binary Heap
• Extract a node from Binary Heap
• Delete entire Binary Heap
• Time and space complexity of Binary Heap

Module 17: Trie

• What is Trie? Why do we need it?
• Common Operations on Trie (Creation)
• Insert a string in Trie
• Search for a string in Trie
• Delete a string from Trie
• Practical use of Trie

Module 18: Hashing

• What is Hashing? Why we need it?
• Hashing Terminology
• Hash Functions
• Types of Collision Resolution Techniques
• Hash Table is Full
• Pros and Cons of Resolution Techniques
• Practical Use of Hashing
• Hashing vs. Other DS

Module 19: Sort Algorithms

• What is sorting
• Types of Sorting
• Sorting Terminologies
• Bubble Sort
• Selection Sort
• Insertion Sort
• Bucket Sort
• Merge Sort
• QuickSort Overview
• Pivot Function Overview
• Pivot Function Implementation
• QuickSort Algorithm Implementation
• Heap Sort
• Comparison of Sorting Algorithms

Module 20: Searching Algorithms

• Introduction to Searching Algorithms
• Linear Search
• Linear Search in Python
• Binary Search
• Binary Search in Python0
• Time Complexity of Binary Search

Module 21: Graph Algorithms

• What is Graph? Why Graph
• Graph Terminology
• Types of Graph
• Graph Representation
• Create a graph using Python
• Create Graph using Python - Add Vertex
• Add Edge
• Remove Edge
• Remove Vertex

Module 22: Graph Traversal – Breadth First Search and Depth First Search

• Graph traversal - BFS
• BFS Traversal in Python
• Graph Traversal - DFS
• DFS Traversal in Python
• BFS Traversal vs. DFS Traversal

Module 23: Topological Sort Algorithm

• Topological Sort
• Topological Sort Algorithm
• Topological Sort in Python

Module 24: Single Source Shortest Path

• Single Source Shortest Path Problem (SSSPP)
• BFS for SSSPP
• BFS for SSSPP in Python
• Why does BFS not work with weighted Graph? 
• Why does DFS not work for SSSP?

Module 25: Graph Algorithms – Dijsktra’s Algorithm

• Dijkstra's Algorithm for SSSP
• Dijkstra's Algorithm Visualization
• Dijkstra Implementation Part 1
• Dijkstra Implementation Part 2
• Dijkstra Algorithm Testing
• Dijkstra Algorithm with negative cycle

Module 26: Graph Algorithms – Bellman Ford Algorithm

• Bellman Ford Algorithm
• Bellman Ford Algorithm with negative cycle
• Why Bellman Ford runs V-1 times?
• Bellman Ford in Python
• BFS vs. Dijkstra vs. Bellman Ford