Python Data Structures Training

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Python Data Structures Training Course Overview

Master Python Data Structure and Algorithm fundamentals with Python Data Structures Training Certification Course from Multisoft Systems. Get trained in one-on-one and corporate trainings and avail perks like lifetime e-learning access, after training support and globally recognized training certificate.

Python data structures are basically containers for various kinds of data, where data structures group and organize data based on type. There are four main types of data structures, lists, sets, tuples and dictionaries. In Python Data Structures Training Certification Course from Multisoft Systems, participants will be introduced to Python Data Structures and Algorithms and how to use its capabilities for efficient data analysis. This course consists of 26 modules that teaches participants about main data structure types and algorithms, such as Stack, Queue and Big O; recursion; how to implement, and use various Data Structures; implement and use various Algorithms; space and time complexity of Algorithms and Data Structures and more.

Designed by industry experts, this course has been curated for computer science graduates; self-taught programmers, who want to learn Data Structures and Algorithms and python programmers, who want to learn data analysis with Python. With Multisoft, you get the opportunity to learn from global subject matter experts and avail several benefits like, lifetime e-learning access, recorded training session videos and after training support. Participants will receive one-on-one /corporate instructor led training sessions for this course. After successful completion of the program, participants are awarded with a globally recognized training completion certificate.

Python Data Structures Course Objective
  • 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
Python Data Structures Online Training
  • Recorded Videos After Training
  • Digital Learning Material
  • Course Completion Certificate
  • 24x7 After Training Support
Target Audience
  • Computer science graduates
  • Self-taught programmers, who want to learn Data Structures and Algorithms
  • Python programmers, who want to learn data analysis with Python
Python Data Structures Course Prerequisites
  • Candidates should have basic knowledge of Python
Python Data Structures Course Certification
  • Multisoft Systems provides a globally recognized training certificate after successful completion of Python Data Structures Training Certification Course.

Python Data Structures Training Course Content

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

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Free Python Data Structures Training Assessment

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Python Data Structures Training FAQ's

Recursion is a process, where a function works directly or indirectly to solve a problem; the function performing recursion process is known as a recursive function. Recursive algorithm helps solve problems with ease.

Big O Notation helps in describing algorithm’s time complexity. The tool analyses the time taken for an algorithm to run with growing input; in simple words the tool calculates the algorithm’s worst-case time complexity.

It is a built-in data structure in Python, which is an arranged collection of objects. When compared to lists, tuples features limited functionality. The key difference between tuples and lists is that tuples are immutable, while Lists are mutable.

A self-balancing binary search tree is called AVL tree. In AVL tree each node consists of extra information known as balance factor.

Hashing is a technique used to map a large amount of data into smaller tables with the help of hashing function, also called message digest function. In this technique, the tool identifies a specific element from a group of similar elements.

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