Data Structures and Algorithms: In Depth using C#
Data Structures and Algorithms: In Depth using C#, available at $69.99, has an average rating of 4.49, with 397 lectures, based on 1784 reviews, and has 8711 subscribers.
You will learn about Understand Complexity of Algorithms ie Time and Space they take at runtime Learn and compare Algorithms used in Searching and Sorting Learn different Data Structures and how to use them in applications Learn how to Code and Implement various data structures and algorithms in C# This course is ideal for individuals who are Anyone who wants to learn Data Structures and Algorithms using C# It is particularly useful for Anyone who wants to learn Data Structures and Algorithms using C#.
Enroll now: Data Structures and Algorithms: In Depth using C#
Summary
Title: Data Structures and Algorithms: In Depth using C#
Price: $69.99
Average Rating: 4.49
Number of Lectures: 397
Number of Published Lectures: 397
Number of Curriculum Items: 397
Number of Published Curriculum Objects: 397
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will Learn
- Understand Complexity of Algorithms ie Time and Space they take at runtime
- Learn and compare Algorithms used in Searching and Sorting
- Learn different Data Structures and how to use them in applications
- Learn how to Code and Implement various data structures and algorithms in C#
Who Should Attend
- Anyone who wants to learn Data Structures and Algorithms using C#
Target Audiences
- Anyone who wants to learn Data Structures and Algorithms using C#
This course will help you in better understanding of the basics of Data Structures and how algorithms are implemented in C# programming language. This course consists of lectures on data structures and algorithms which covers the computer science theory + implementation of data structures in C#. This course will also help students to face interviews confidently 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:
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We have a discussion of why we need data structures and why we need to learn Algorithms
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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.
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 C#. We then move on to learn:
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Analysis of Algorithms
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Recursion
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Searching Algorithms
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Sorting Algorithms
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Linked List
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Stacks
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Queues & Deques
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Trees, Binary Trees & Binary Search Trees
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Balanced Search Trees
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Heaps
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Hashing
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Graphs & Graph Traversal Algorithms (Breadth-First Search & Depth First Search)
Again, each of these sections includes theory lectures covering Data structures & their Abstract Data Types and Algorithms. Plus the implementation of these topics in C#.
Course Curriculum
Chapter 1: Course Overview
Lecture 1: Course Outcomes & Curriculum
Lecture 2: Why we Need Data Structures ?
Lecture 3: Why Learn Algorithms ?
Lecture 4: Abstract Data Type (ADT)
Lecture 5: Visual Studio C# : Execution Environment
Chapter 2: 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 3: Recursion and Analysis of Recursive Functions
Lecture 1: How Recursion Works ?
Lecture 2: Lab: Iteration Vs Recursion – Implementation
Lecture 3: Time Complexity of Recursion – Recurrence Relation
Lecture 4: Recurrence Relation – Another example
Lecture 5: Tail and Head Recursion
Lecture 6: Tree Recursion
Lecture 7: Indirect Recursion
Lecture 8: Sum of N Natural Numbers
Lecture 9: Lab: Sum of N Numbers – Implementation
Lecture 10: Factorial
Lecture 11: Lab: Factorial – Implementation
Chapter 4: Searching Algorithms
Lecture 1: Linear Search Algorithm
Lecture 2: Lab: Linear Search – Implementation
Lecture 3: Binary Search Iterative Algorithm
Lecture 4: Lab: Binary Search using Iterations – Implementation
Lecture 5: Binary Search Recursive Algorithm
Lecture 6: Lab: Binary Search using Recursion – Implementation
Chapter 5: Sorting Algorithms
Lecture 1: Sorting Introduction
Lecture 2: Stable and Unstable Sorting
Lecture 3: Selection Sort – How does it Work ?
Lecture 4: Selection Sort – Algorithm and Analysis
Lecture 5: Lab: Selection Sort – Implementation
Lecture 6: Insertion Sort – How does it Work ?
Lecture 7: Insertion Sort – Algorithm and Analysis
Lecture 8: Lab: Insertion Sort – Implementation
Lecture 9: Bubble Sort – How does it Work ?
Lecture 10: Bubble Sort – Algorithm and Analysis
Lecture 11: Lab: Bubble Sort – Implementation
Lecture 12: Shell Sort – How does it Work ?
Lecture 13: Shell Sort – Algorithm and Analysis
Lecture 14: Lab: Shell Sort – Implementation
Lecture 15: Merge Sort – How does it Work ?
Lecture 16: Merge Sort – Algorithm
Lecture 17: Merging – Algorithm
Lecture 18: Merge Sort – Complexity Analysis
Lecture 19: Lab: Merge Sort – Implementation
Lecture 20: Quick Sort – How does it Work ?
Lecture 21: Quick Sort – Algorithm
Lecture 22: Quick Sort – Complexity Analysis
Lecture 23: Lab: Quick Sort – Implementation
Lecture 24: Summary of Complexities – Sorting Algorithms
Chapter 6: 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: Lab: Creating and Displaying Linked List – Implementation
Lecture 7: Insert Element at the Beginning of Linked List
Lecture 8: Lab: Insert Element at the Beginning of Linked List – Implementation
Lecture 9: Insert Element Anywhere in between the Linked List
Lecture 10: Lab: Insert Element Anywhere in Between the Linked List – Implementation
Lecture 11: Delete Element at Beginning of Linked List
Lecture 12: Lab: Delete Element at Beginning of the Linked List – Implementation
Lecture 13: Delete Element at End of Linked List
Lecture 14: Lab: Delete Element at End of Linked List – Implementation
Lecture 15: Delete Element Anywhere in between Linked List
Lecture 16: Lab: Delete Element Anywhere in between Linked List – Implementation
Lecture 17: Searching Element in Linked List
Lecture 18: Lab: Searching the Linked List – Implementation
Lecture 19: 24. Exercise Solution Inserting Elements in Sorted Order
Chapter 7: Circular Linked List
Lecture 1: What is Circular Linked List ?
Lecture 2: Creating Circular Linked List
Lecture 3: Traversing Circular Linked List
Lecture 4: Lab: Creating and Displaying Circular Linked List – Implementation
Lecture 5: Insert Element at the Beginning of Circular Linked List
Lecture 6: Lab: Insert Element at the Beginning of Circular Linked List – Implementation
Lecture 7: Insert Element Anywhere in between the Circular Linked List
Lecture 8: Lab: Insert Element Anywhere in Between the Circular Linked List -Implementation
Lecture 9: Delete Element at Beginning of Circular Linked List
Lecture 10: Lab: Delete Element at Beginning of the Circular Linked List – Implementation
Lecture 11: Delete Element at End of Circular Linked List
Lecture 12: Lab: Delete Element at End of Circular Linked List – Implementation
Lecture 13: Delete Element Anywhere in between Circular Linked List
Lecture 14: Lab: Delete Element Anywhere in between Circular Linked List – Implementation
Chapter 8: Doubly Linked List
Lecture 1: What is Doubly Linked List ?
Lecture 2: Creating Node of Doubly Linked List
Lecture 3: Playing with links of Doubly Linked List
Lecture 4: Creating Doubly Linked List
Lecture 5: Traversing Doubly Linked List
Instructors
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Syed Mohiuddin
Instructor
Rating Distribution
- 1 stars: 17 votes
- 2 stars: 24 votes
- 3 stars: 187 votes
- 4 stars: 691 votes
- 5 stars: 873 votes
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