What you'll learn
Understand, implement and use different type of data structures
Be able to decide which data structure can be used for solving a problem or optimising an application
Understand, implement and use different type of algorithms
How to solve coding problems in technical interviews
How to calculate space and time complexities for your code
Requirements
Basic Java programming knowledge
Description
In this course we will dive deep into data structures and algorithms and learn how to do they work, how to implement them in Java and how to use them for implementing and optimizing your application. we will also learn how to calculate time complexity and space complexity and how to decide which data structure or algorithm should be used for solving a specific problem.We will also solve coding challenges from Leetcode to reinforce the data structures and algorithms knowledge and to explain how they can be used for solving coding problems efficiently.Data structures and algorithms are two of the most important aspects of computer science, learning data structures and algorithms will help you become a better programmer, write more efficient code and solve problems quicker, that's why Tech companies focus on data structures and algorithms in the technical interviews.Throughout this course we will cover everything you need to master data structures and algorithms, including:Big O notation (time complexity and space complexity)ArraysLinked listsStacksHeapsQueuesHashmapsTriesTrees (and tree traversal algorithms)GraphsBreadth first search and depth first searchLinear searchBinary searchBubble sortQuick sortSelection sortInsertion sortMerge sortRecursionI am confident that you will like this course and that you will be a different programmer once you finish it, join me in this course and master data structures and algorithms!
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 What is an algorithm
Lecture 3 What are Data structures
Lecture 4 Why programmers need algorithms and data structures
Section 2: BigO Notation - time and space complexity
Lecture 5 Time complexity
Lecture 6 Space complexity
Section 3: Array
Lecture 7 Introduction to arrays
Lecture 8 Arrays example in Java
Lecture 9 When to use arrays
Lecture 10 Two dimentional arrays
Lecture 11 Two dimensional array example in Java
Lecture 12 Time complexity of array's operations
Lecture 13 Array's coding challenge (with solution)
Section 4: Linked list
Lecture 14 Introduction to linked lists
Lecture 15 Types of linked lists
Lecture 16 Linked list's operations and their time complexity
Lecture 17 Linked list implementation in Java
Lecture 18 Arrays vs linked lists and when to use each
Lecture 19 Linked list's coding challenge (with solution)
Section 5: Stack
Lecture 20 Introduction to stacks
Lecture 21 Stack implementation in Java
Lecture 22 Stack's operations time complexity
Lecture 23 Stack's coding challenge (with solution)
Section 6: Queue
Lecture 24 Introduction to queues
Lecture 25 Queue implementation in Java
Lecture 26 Queue's operations time complexity
Lecture 27 Queue's coding challenge (with solution)
Section 7: Hashmap
Lecture 28 Introduction to hashmaps
Lecture 29 Hashmap's operations time complexity
Lecture 30 When to use hashmaps
Lecture 31 Hashmap usecase in Java
Lecture 32 Hashmap's coding challenge (with solution)
Section 8: Tree
Lecture 33 Introduction to trees
Lecture 34 Types of tree
Lecture 35 Tree's depth
Lecture 36 Tree's traversal algorithms
Lecture 37 Implementation of the tree and it's traversal algorithms in Java
Lecture 38 Tree's coding challenge (with solution)
Section 9: Heap
Lecture 39 Introduction to heaps
Lecture 40 Heap implementation in Java
Lecture 41 Heap's operations time complexity
Lecture 42 When to use heaps
Lecture 43 Priority Queue in Java
Lecture 44 Heap's coding challenge (with solution)
Section 10: Graph
Lecture 45 Introduction to graphs
Lecture 46 Types of graphs
Lecture 47 Time complexity of BFS and DFS for graphs
Lecture 48 Applications of graphs
Lecture 49 Graph implementation and usecase in Java
Lecture 50 DFS implementation in Java
Lecture 51 BFS implementation in Java
Lecture 52 Graph's coding challenge (with solution)
Section 11: Trie
Lecture 53 Introduction to tries
Lecture 54 Trie implementation in Java
Lecture 55 Space and time complexity of the trie's operations
Lecture 56 Trie's coding challenge (with solution)
Section 12: Searching algorithms
Lecture 57 Introduction to searching algorithms
Lecture 58 Linear search
Lecture 59 Binary search
Lecture 60 Linear search vs Binary search
Lecture 61 Searching algorithms coding challenge (with solution)
Section 13: Sorting algorihtms
Lecture 62 Introduction to sorting algorithms
Lecture 63 Bubble sort
Lecture 64 Quick sort
Lecture 65 Selection sort
Lecture 66 Insertion sort
Lecture 67 Merge sort
Lecture 68 Sorting algorithms coding challenge (with solution)
Section 14: Recursion
Lecture 69 Introduction to recursion
Lecture 70 Recursion's coding challenge (with solution)
Section 15: What's next ?
Lecture 71 Conclusion and next steps
Programmers who want to master data structures and algorithms and implement/use them to develop efficient applications,Programmers that want to improve their programming skills and become better at programming,Programmers who want to become better at solving coding problems and writing more efficient code,Computer science students,Self-taught programmers,Programmers who are preparing for a coding interviews


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