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From 0 to 1: Data Structures & Algorithms in Java [Video]

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From 0 to 1: Data Structures & Algorithms in Java [Video]

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Video Details

ISBN 139781788626767
Course Length15 hours

Video Description

This is an animated, visual and spatial way to learn data structures and algorithms. Our brains process different types of information differently - evolutionary we are wired to absorb information best when it is visual and spatial i.e. when we can close our eyes and see it. More than most other concepts, Data Structures and Algorithms are best learnt visually. These are incredibly easy to learn visually, very hard to understand most other ways. This course has been put together by a team with tons of everyday experience in thinking about these concepts and using them at work at Google, Microsoft and Flipkart What's Covered: Big-O notation and complexity, Stacks, Queues, Trees, Heaps, Graphs and Graph Algorithms, Linked lists, Sorting, Searching.

Style and Approach

A 15 – high-quality courses available at super low prices has been put together by a team with tons of everyday experience in thinking about these concepts and using them at work at Google, Microsoft and Flipkart

Table of Contents

What this course is about
You, This course and Us
Data Structures And Algorithms - A Symbiotic Relationship
Why are Data Structures And Algorithms important?
Complexity Analysis and the Big-O Notation
Performance and Complexity
The Big-O Notation
What is the complexity of these pieces of code?
Linked Lists
The Linked List - The Most Basic Of All Data Structures
Linked List Problems
Linked Lists vs Arrays
Stacks And Queues
Meet The Stack - Simple But Powerful
Building A Stack Using Java
Match Parenthesis To Check A Well Formed Expression
Find The Minimum Element In A Stack In Constant Time
Meet The Queue - A Familiar Sight In Everyday Life
The Circular Queue - Tricky But Fast
Build A Queue With Two Stacks
Sorting and Searching
Sorting Trade-Offs
Selection Sort
Bubble Sort
Insertion Sort
Shell Sort
Merge Sort
Quick Sort
Binary Search - search quickly through a sorted list
Binary Trees
Meet The Binary Tree - A Hierarchical Data Structure
Breadth First Traversal
Depth First - Pre-OrderTraversal
Depth First - In-Order and Post-Order Traversal
Binary Search Trees
The Binary Search Tree - an introduction
Insertion and Lookup in a Binary Search Tree
Binary Tree Problems
Minimum Value, Maximum Depth And Mirror
Count Trees, Print Range and Is BST
Heaps
The Heap Is Just The Best Way to Implement a Priority Queue
Meet The Binary Heap - It's A Tree At Heart
The Binary Heap - Logically A Tree Really An Array
The Binary Heap - Making It Real With Code
Heapify!
Insert And Remove From A Heap
Revisiting Sorting - The Heap Sort
Heap Sort Phase I – Heapify
Heap Sort Phase II - The Actual Sort
Heap Problems
Maximum Element In A Minimum Heap and K Largest Elements In A Stream
Graphs
Introducing The Graph
Types Of Graphs
The Directed And Undirected Graph
Representing A Graph In Code
Graph Using An Adjacency Matrix
Graph Using An Adjacency List And Adjacency Set
Comparison Of Graph Representations
Graph Traversal - Depth First And Breadth First
Graph Algorithms
Topological Sort In A Graph
Implementation Of Topological Sort
Shortest Path Algorithms
Introduction To Shortest Path In An Unweighted Graph - The Distance Table
The Shortest Path Algorithm Visualized
Implementation Of The Shortest Path In An Unweighted Graph
Introduction To The Weighted Graph
Shortest Path In A Weighted Graph - A Greedy Algorithm
Dijkstra's Algorithm Visualized
Implementation Of Dijkstra's Algorithm
Introduction To The Bellman Ford Algorithm
The Bellman Ford Algorithm Visualized
Dealing With Negative Cycles In The Bellman Ford Algorithm
Implementation Of The Bellman Ford Algorithm
Spanning Tree Algorithms
Prim's Algorithm For a Minimal Spanning Tree
Use Cases And Implementation Of Prim's Algorithm
Kruskal's Algorithm For a Minimal Spanning Tree
Implementation Of Kruskal's Algorithm
Graph Problems
Design A Course Schedule Considering Pre-reqs For Courses
Find The Shortest Path In A Weighted Graphs - Fewer Edges Better

What You Will Learn

  • Visualise - really vividly imagine - the common data structures, and the algorithms applied to them
  • Pick the correct tool for the job - correctly identify which data structure or algorithm makes sense in a particular situation
  • Calculate the time and space complexity of code - really understand the nuances of the performance aspects of code

Authors

Table of Contents

What this course is about
You, This course and Us
Data Structures And Algorithms - A Symbiotic Relationship
Why are Data Structures And Algorithms important?
Complexity Analysis and the Big-O Notation
Performance and Complexity
The Big-O Notation
What is the complexity of these pieces of code?
Linked Lists
The Linked List - The Most Basic Of All Data Structures
Linked List Problems
Linked Lists vs Arrays
Stacks And Queues
Meet The Stack - Simple But Powerful
Building A Stack Using Java
Match Parenthesis To Check A Well Formed Expression
Find The Minimum Element In A Stack In Constant Time
Meet The Queue - A Familiar Sight In Everyday Life
The Circular Queue - Tricky But Fast
Build A Queue With Two Stacks
Sorting and Searching
Sorting Trade-Offs
Selection Sort
Bubble Sort
Insertion Sort
Shell Sort
Merge Sort
Quick Sort
Binary Search - search quickly through a sorted list
Binary Trees
Meet The Binary Tree - A Hierarchical Data Structure
Breadth First Traversal
Depth First - Pre-OrderTraversal
Depth First - In-Order and Post-Order Traversal
Binary Search Trees
The Binary Search Tree - an introduction
Insertion and Lookup in a Binary Search Tree
Binary Tree Problems
Minimum Value, Maximum Depth And Mirror
Count Trees, Print Range and Is BST
Heaps
The Heap Is Just The Best Way to Implement a Priority Queue
Meet The Binary Heap - It's A Tree At Heart
The Binary Heap - Logically A Tree Really An Array
The Binary Heap - Making It Real With Code
Heapify!
Insert And Remove From A Heap
Revisiting Sorting - The Heap Sort
Heap Sort Phase I – Heapify
Heap Sort Phase II - The Actual Sort
Heap Problems
Maximum Element In A Minimum Heap and K Largest Elements In A Stream
Graphs
Introducing The Graph
Types Of Graphs
The Directed And Undirected Graph
Representing A Graph In Code
Graph Using An Adjacency Matrix
Graph Using An Adjacency List And Adjacency Set
Comparison Of Graph Representations
Graph Traversal - Depth First And Breadth First
Graph Algorithms
Topological Sort In A Graph
Implementation Of Topological Sort
Shortest Path Algorithms
Introduction To Shortest Path In An Unweighted Graph - The Distance Table
The Shortest Path Algorithm Visualized
Implementation Of The Shortest Path In An Unweighted Graph
Introduction To The Weighted Graph
Shortest Path In A Weighted Graph - A Greedy Algorithm
Dijkstra's Algorithm Visualized
Implementation Of Dijkstra's Algorithm
Introduction To The Bellman Ford Algorithm
The Bellman Ford Algorithm Visualized
Dealing With Negative Cycles In The Bellman Ford Algorithm
Implementation Of The Bellman Ford Algorithm
Spanning Tree Algorithms
Prim's Algorithm For a Minimal Spanning Tree
Use Cases And Implementation Of Prim's Algorithm
Kruskal's Algorithm For a Minimal Spanning Tree
Implementation Of Kruskal's Algorithm
Graph Problems
Design A Course Schedule Considering Pre-reqs For Courses
Find The Shortest Path In A Weighted Graphs - Fewer Edges Better

Video Details

ISBN 139781788626767
Course Length15 hours
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