**From 0 to 1: Data Structures & Algorithms in Java**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 15 Hours | 3.64 GB

Learn so you can see it with your eyes closed

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.

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

**Table of Contents**

01 You, This course and Us

02 Why are Data Structures And Algorithms important

03 Performance and Complexity

04 The Big-O Notation

05 What is the complexity of these pieces of code

06 The Linked List - The Most Basic Of All Data Structures

07 Linked List Problems

08 Linked Lists vs Arrays

09 Meet The Stack - Simple But Powerful

10 Building A Stack Using Java

11 Match Parenthesis To Check A Well Formed Expression

12 Find The Minimum Element In A Stack In Constant Time

13 Meet The Queue - A Familiar Sight In Everyday Life

14 The Circular Queue - Tricky But Fast

15 Build A Queue With Two Stacks

16 Sorting Trade-Offs

17 Selection Sort

18 Bubble Sort

19 Insertion Sort

20 Shell Sort

21 Merge Sort

22 Quick Sort

23 Binary Search - search quickly through a sorted list

24 Meet The Binary Tree - A Hierarchical Data Structure

25 Breadth First Traversal

26 Depth First - Pre-OrderTraversal

27 Depth First - In-Order and Post-Order Traversal

28 The Binary Search Tree - an introduction

29 Insertion and Lookup in a Binary Search Tree

30 Minimum Value, Maximum Depth And Mirror

31 Count Trees, Print Range and Is BST

32 The Heap Is Just The Best Way to Implement a Priority Queue

33 Meet The Binary Heap - It's A Tree At Heart

34 The Binary Heap - Logically A Tree Really An Array

35 The Binary Heap - Making It Real With Code

36 Heapify!

37 Insert And Remove From A Heap

38 Heap Sort Phase I – Heapify

39 Heap Sort Phase II - The Actual Sort

40 Maximum Element In A Minimum Heap and K Largest Elements In A Stream

41 Introducing The Graph

42 Types Of Graphs

43 The Directed And Undirected Graph

44 Representing A Graph In Code

45 Graph Using An Adjacency Matrix

46 Graph Using An Adjacency List And Adjacency Set

47 Comparison Of Graph Representations

48 Graph Traversal - Depth First And Breadth First

49 Topological Sort In A Graph

50 Implementation Of Topological Sort

51 Introduction To Shortest Path In An Unweighted Graph - The Distance Table

52 The Shortest Path Algorithm Visualized

53 Implementation Of The Shortest Path In An Unweighted Graph

54 Introduction To The Weighted Graph

55 Shortest Path In A Weighted Graph - A Greedy Algorithm

56 Dijkstra's Algorithm Visualized

57 Implementation Of Dijkstra's Algorithm

58 Introduction To The Bellman Ford Algorithm

59 The Bellman Ford Algorithm Visualized

60 Dealing With Negative Cycles In The Bellman Ford Algorithm

61 Implementation Of The Bellman Ford Algorithm

62 Prim's Algorithm For a Minimal Spanning Tree

63 Use Cases And Implementation Of Prim's Algorithm

64 Kruskal's Algorithm For a Minimal Spanning Tree

65 Implementation Of Kruskal's Algorithm

66 Design A Course Schedule Considering Pre-reqs For Courses

67 Find The Shortest Path In A Weighted Graphs - Fewer Edges Better

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