Data Structures and Algorithms- In-Depth using Python

Data Structures and Algorithms: In-Depth using Python, available at $84.99, has an average rating of 4.51, with 429 lectures, 1 quizzes, based on 3072 reviews, and has 22083 subscribers.

You will learn about Learn Data Structures, Abstract Data Types and their implementation in Python Implementation of Searching Algorithms in Python Implementation of Stacks, Queues, Linked List, Binary Trees, Heaps and Graphs in Python Implementation of Binary Tree Traversal Techniques in Python Graph traversals techniques ie Depth First Search and Breadth-First Search in Python Implementation of Sorting Algorithms in Python Enhance Analytical Skill and efficiently use searching and sorting algorithms in real applications This course is ideal for individuals who are Students who want to have better understanding of Data Structures or Python programmers curious about Data Structures or IT Professional experimenting implementation of Data Structures in Python It is particularly useful for Students who want to have better understanding of Data Structures or Python programmers curious about Data Structures or IT Professional experimenting implementation of Data Structures in Python.

Enroll now: Data Structures and Algorithms: In-Depth using Python

Summary

Title: Data Structures and Algorithms: In-Depth using Python

Price: $84.99

Average Rating: 4.51

Number of Lectures: 429

Number of Quizzes: 1

Number of Published Lectures: 429

Number of Published Quizzes: 1

Number of Curriculum Items: 430

Number of Published Curriculum Objects: 430

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Learn Data Structures, Abstract Data Types and their implementation in Python

Implementation of Searching Algorithms in Python

Implementation of Stacks, Queues, Linked List, Binary Trees, Heaps and Graphs in Python

Implementation of Binary Tree Traversal Techniques in Python

Graph traversals techniques ie Depth First Search and Breadth-First Search in Python

Implementation of Sorting Algorithms in Python

Enhance Analytical Skill and efficiently use searching and sorting algorithms in real applications

Who Should Attend

Students who want to have better understanding of Data Structures

Python programmers curious about Data Structures

IT Professional experimenting implementation of Data Structures in Python

Target Audiences

Students who want to have better understanding of Data Structures

Python programmers curious about Data Structures

IT Professional experimenting implementation of Data Structures in Python

This course will help you in better understanding of the basics of Data Structures and how algorithms are implemented in high-level programming language. This course consists of lectures on data structures and algorithms which covers the computer science theory + implementation of data structures in python language. This course will also help students to face interviews at the top technology companies. This course is like having personal tutors to teach you about data structures and algorithms.

There’s tons of concepts and content in this course. To begin the course:

We have a discussion of why we need data structures.

Then we move on to discuss Analysis of Algorithms ie Time and Space complexity, though the Asymptotic Notation ie Big O, Omega and Theta are taken up at the end of this course so that you do not get confused and concentrate on understanding the concepts of data structures.

We have a programming environment setup to make sure you have all the software you need in order to get the hands-on experience in implementing Data structures and algorithms.

Then we get to the essence of the course; algorithms and data structures. Each of the specific algorithms and data structures is divided into two sections. Theory lectures and implementation of those concepts in Python. We then move on to learn:

1. Recursion

2. Stacks, Queues, Deques

3. Linked List

4. Trees & Binary Trees

5. Binary Search Trees

6. Priority Queues and Heaps

7. Graphs & Graph Traversal Algorithms

8. Searching and Sorting algorithms

Again, each of these sections includes theory lectures covering data structures & their Abstract Data Types and/or algorithms. Plus the implementation of these topics in Python.

Course Curriculum

Chapter 1: Course Overview

Lecture 1: Course Introduction

Lecture 2: Get the most out of this course

Lecture 3: Why we need Data Structure ?

Lecture 4: Why Learn Algorithms ?

Lecture 5: Abstract Data Type (ADT)

Lecture 6: Python Installation on Windows

Lecture 7: PyCharm (IDE) Installation on Windows

Chapter 2: Bonus: Python Crash Course (Basics and Fundamentals)

Lecture 1: First Python Program, Data Types and Variables

Lecture 2: Integers & Float Data Types

Lecture 3: Strings Data Types

Lecture 4: Boolean & None Data Types

Lecture 5: Arithmetic Operators & Integer Division

Lecture 6: Relational or Comparison Operators

Lecture 7: Logical Operators

Lecture 8: input() Function

Lecture 9: print() Function

Lecture 10: if, if-else and elif Statements

Lecture 11: range() Function

Lecture 12: while() & for() Loops

Lecture 13: break & continue Statements

Lecture 14: What are Lists?

Lecture 15: Using Lists and List Indexing

Lecture 16: What are Tuples ?

Lecture 17: Tuple Indexing

Lecture 18: Membership & Identity Operators

Lecture 19: What are Dictionaries?

Lecture 20: Using Dictionaries

Lecture 21: What are Functions?

Lecture 22: Writing Functions in Python?

Lecture 23: Importing Modules in Python

Lecture 24: Creating Your Own Modules

Lecture 25: Fundamentals of Object Oriented Programming

Lecture 26: Defining Classes & Creating Objects

Lecture 27: More on __init__ Method (Constructor)

Lecture 28: Understanding self Parameter

Lecture 29: Static and Local Variables

Chapter 3: Analysis of Algorithms

Lecture 1: Time Complexity

Lecture 2: Order of Growth

Lecture 3: Asymptotic Analysis

Lecture 4: Big-Oh Notation

Lecture 5: Big Omega Notation

Lecture 6: Big Theta Notation

Lecture 7: Performance Summary

Lecture 8: Space Complexity

Chapter 4: Recursion and Analysis of Recursive Functions

Lecture 1: How Recursion Works ?

Lecture 2: Iteration vs recursion lets Implement

Lecture 3: Time Complexity of Recursion – Recurrence Relation

Lecture 4: Recurrence Relation – Another example

Lecture 5: Types of Recursion – Tail and Head Recursion

Lecture 6: Types of Recursion – Tree Recursion

Lecture 7: Types of Recursion – Indirect Recursion

Lecture 8: Sum of N Natural Numbers

Lecture 9: Lets Implement Sum of N Numbers

Lecture 10: Factorial

Lecture 11: Lets Implement Factorial

Chapter 5: Searching Algorithms

Lecture 1: Linear Search Algorithm

Lecture 2: Lets Implement Linear Search

Lecture 3: Binary Search Iterative Algorithm

Lecture 4: Lets Implement Binary Search using Iterations

Lecture 5: Binary Search Recursive Algorithm

Lecture 6: Lets Implement Binary Search using Recursion

Chapter 6: Sorting Algorithms

Lecture 1: Sorting Introduction

Lecture 2: Stable and Unstable Sorting

Lecture 3: Selection Sort – Explanation, Algorithm and Analysis

Lecture 4: Selection Sort – Implementation

Lecture 5: Insertion Sort – Explanation, Algorithm and Analysis

Lecture 6: Insertion Sort – Implementation

Lecture 7: Bubble Sort – Explanation, Algorithm and Analysis

Lecture 8: Bubble Sort – Implementation

Lecture 9: Shell Sort – Explanation, Algorithm and Analysis

Lecture 10: Shell Sort – Implementation

Lecture 11: Merge Sort

Lecture 12: Merge Sort – Algorithm

Lecture 13: Merging – Algorithm

Lecture 14: Merge Sort – Complexity Analysis

Lecture 15: Merge Sort – Implementation

Lecture 16: Quick Sort

Lecture 17: Quick Sort – Algorithm

Lecture 18: Quick Sort – Complexity Analysis

Lecture 19: Quick Sort – Implementation

Lecture 20: Count Sort – Explanation, Algorithm and Analysis

Lecture 21: Count Sort – Implementation

Lecture 22: Radix Sort – Explanation, Algorithm and Analysis

Lecture 23: Radix Sort – Implementation

Lecture 24: Pythons Built-in Sorting Functions

Lecture 25: Sorting Algorithms – Summary of Complexities

Chapter 7: Linked List

Lecture 1: Why do we use Linked List

Lecture 2: Creating Node of Linked List

Lecture 3: Playing with the links of Linked List

Lecture 4: How to Create Linked List

Lecture 5: Displaying or Traversing Linked List

Lecture 6: Lets Implement Creating and Displaying Linked List

Instructors

Syed Mohiuddin
Instructor

Rating Distribution

1 stars: 22 votes

2 stars: 48 votes

3 stars: 335 votes

4 stars: 1207 votes

5 stars: 1460 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!