Algorithms Data Structures in Java #1 (+INTERVIEW QUESTIONS) from Udemy

This course is about data structures and algorithms. We are going to implement the problems in Java. The course takes approximately 20 hours to complete. It is highly recommended to type out these data structures several times on your own in order to get a good grasp of it.

Section 1:

data structures and abstract data types

Section 2 - Arrays

what are arrays
what is random access and how to indexes

Section 3 - Linked Lists

linked lists and doubly linked lists
linked list related interview questions

Section 2 - Stacks and Queues:

what are stacks and queues
heap memory and stack memory
visualizing stack memory

Section 3 - Binary Search Trees (BSTs):

what are tree data structures?
how to achieve O(logN) logarithmic running time?
binary search trees

Section 4 - AVL Trees

what is the problem with binary search trees?
balanced search trees: AVL trees
rotations

Section 5 - Red-Black Trees

what are red-black trees?
what is recovering operation?
comparing AVL trees and red-black trees

Section 6 - Splay Trees

splay trees and caches
achieve O(1) running time for getting the recently visited item

Section 7 - Heaps and Priority Queues

what are priority queues?
what is heap data structure?
how to do sorting in O(NlogN) with heaps?

Section 8 - B-Trees

external memory and the main memory (RAM)
B-trees and their applications in memory
B* trees and B+ trees

Section 9 - Hashing and HashMaps:

what are hashing and hashtables (hashmaps)
what are hash-functions
how to achieve O(1) running time complexity

Section 10 - Sorting Algorithms

basic sorting algorithms
bubble sort and selection sort
insertion sort and shell sort
quicksort and merge sort
comparison based and non-comparison based approaches
string sorting algorithms
bucket sort and radix sort

Section 11 - Substring Search Algorithms

substring search algorithms
brute-force substring search
Z substring search algorithm
Rabin-Karp algorithm and hashing
Knuth-Morris-Pratt (KMP) substring search algorithm

Section 12 (BONUS):

what is LRU cache
LRU cache implementation

Section 13 (BONUS):

Fenwick trees (binary indexed trees)
binary indexed tree implementation

Section 14 - Algorithms Analysis

how to measure the running time of algorithms
running time analysis with big O (ordo), big Ω (omega) and big θ (theta) notations
complexity classes
polynomial (P) and non-deterministic polynomial (NP) algorithms
O(1), O(logN), O(N) and several other running time complexities

In each chapter you will learn about the theoretical background of each algorithm or data structure, then we are going to write the code on a step by step basis in Eclipse, Java.

Most of the advanced algorithms relies heavily on these topics so it is definitely worth understanding the basics. These principles can be used in several fields: in investment banking, artificial intelligence or electronic trading algorithms on the stock market.

Thanks for joining the course, let's get started.

What's inside

Learning objectives

Grasp the fundamentals of algorithms and data structures
Detect non-optimal code snippets
Learn about arrays and linked lists
Learn about stacks and queues
Learn about binary search trees

Learn about balanced binary search trees such as avl trees or red-black trees
Learn about priority queues and heaps
Learn about b-trees and external memory
Learn about hashing and hash tables

Grasp the fundamentals of algorithms and data structures
Detect non-optimal code snippets
Learn about arrays and linked lists
Learn about stacks and queues
Learn about binary search trees
Learn about balanced binary search trees such as avl trees or red-black trees
Learn about priority queues and heaps
Learn about b-trees and external memory
Learn about hashing and hash tables

Syllabus

Introduction

Course exercises and solutions

Data Structures Overview

Why do we need data structures?

Data structures and abstract data types

Data Structures and Abstract Data Types Quiz

Installation and Environment Setup

Installing Java

Installing Eclipse

Arrays

What are array data structures?

Arrays introduction - operations

Implementing arrays

ArrayList in Java

Arrays Quiz

Interview Questions (Arrays)

Reversing an array in-place exercise

Reversing an array in-place solution

Anagram problem exercise

Anagram problem solution

Palindrome problem exercise

Palindrome problem solution

Integer reversion exercise

Integer reversion problem solution

Linked Lists

What are linked lists?

Linked list theory - operations

Pros and cons of linked lists

Linked lists in Java !!!

Linked list implementation I

Linked list implementation II

Linked list implementation III

Linked list implementation IV

Comparing linked lists and arrays

Practical (real-world) applications of linked lists

Linked Lists Quiz

Doubly Linked Lists

What are doubly linked lists?

Doubly linked list implementation

LinkedLists in Java

Running time comparison: linked lists and arrays

Doubly Linked Lists Quiz

Interview Questions (Linked List)

Finding the middle node in a linked list exercise

Finding the middle node in a linked list solution

Reverse a linked list in-place exercise

Reverse a linked list in-place solution

Stacks

What are stacks?

Stacks in memory management (stacks and heaps )

Stack memory visualization

Stack implementation with linked list

Stack implementation with arrays

Stacks in Java

Practical (real-world) applications of stacks

Stacks Quiz

Queues

What are queues?

Queue implementation with linked list

Queues in Java

ArrayDeque in Java

ArrayDeque and Stack performance comparison

Queues Quiz

Interview Questions (Stacks and Queues)

Max in a stack problem overview

Max in a stack problem solution

Stack with queue overview

Stack with queue solution

Stack with queue solution - recursion

Binary Search Trees

What are binary search trees?

How to search and insert items into a binary search tree?

Implementing insertion I - Node and Tree classes

Implementing insertion II

How to visit all the items of a binary search trees? (in-order traversal)

Finding the minimum item exercise

Binary search tree implementation - max, min and traversal

Stack memory visualization - finding max (min) items

Stack memory visualization - tree traversal

How to remove an item from a binary search tree?

Remove operation implementation I

Remove operation implementation II

Pros and cons of binary search trees

Practical (real-world) applications of trees

Binary Search Trees Quiz

Interview Questions (Trees)

Compare binary trees exercise

Compare binary trees solution

k-th smallest element exercise

k-th smallest element in a search tree solution

Family age problem exercise

Family age problem solution

Balanced Trees: AVL Trees

Motivation behind balanced binary search trees

What are AVL trees?

AVL trees introduction - height

AVL trees introduction - rotations

AVL trees introduction - illustration

AVL tree implementation I

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops non-optimal code snippets that may hinder code quality and efficiency

Introduces fundamental data structures and algorithms essential for software development

Explores advanced data structures such as binary search trees, AVL trees, and heaps, which are widely used in industry

Provides practical applications of data structures and algorithms in various fields, including investment banking and electronic trading

Delves into sorting algorithms and substring search algorithms, which are crucial for data manipulation tasks

Incorporates bonus sections on LRU cache and Fenwick trees, extending the scope of the course

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Algorithms Data Structures in Java #1 (+INTERVIEW QUESTIONS) with these activities:

Review notes, slides, and assignments from previous courses related to data structures and algorithms

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Refresh your prior knowledge and strengthen your foundation before starting the course.

Browse courses on Data Structures

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Gather notes, slides, and assignments from previous courses.
Review the materials to recall key concepts and techniques.
Identify areas where you need additional reinforcement.
Consider re-attempting practice problems or exercises from the previous courses.

Read 'Introduction to Algorithms' by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein

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Review the fundamentals of data structures and algorithms by reading a foundational text in the field.

View Introduction to Algorithms, fourth edition on Amazon

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Acquire the book and read the preface and introduction.
Read and understand each chapter.
Attempt practice problems and exercises for each chapter.
Review and summarize the key concepts and techniques discussed in the book.

Follow tutorials on data structures and algorithms from reputable sources

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Supplement your understanding by following structured and guided tutorials from experts in the field.

Browse courses on Data Structures

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Identify reputable sources for tutorials, such as Coursera, Udemy, or YouTube channels of experts.
Select tutorials that align with your learning objectives.
Follow the tutorials step-by-step and take notes.
Practice implementing the concepts covered in the tutorials.

Four other activities

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Participate in a study group or online forum for data structures and algorithms

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Engage with peers to discuss concepts, ask questions, and collaborate on problem-solving.

Browse courses on Data Structures

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Identify or join a study group or online forum.
Actively participate in discussions.
Ask questions and provide insights.
Help other members with their understanding.

Solve LeetCode problems tagged with 'Data Structures' and 'Algorithms'

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Reinforce your understanding of data structures and algorithm concepts by solving coding problems on LeetCode.

Browse courses on Data Structures

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Create a LeetCode account.
Filter problems by difficulty and topic.
Attempt to solve problems on your own.
Refer to hints and solutions if needed.

Implement a specific data structure or algorithm in a programming language of your choice

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Apply your knowledge by implementing a data structure or algorithm from scratch, testing its functionality thoroughly.

Browse courses on Data Structures

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Choose a data structure or algorithm to implement.
Design and code the implementation.
Write unit tests to verify the correctness of the implementation.
Document the code and explain the implementation choices.

Write a blog post or article summarizing a specific data structure or algorithm

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Solidify your understanding of a particular data structure or algorithm by explaining it to others in a written format.

Browse courses on Data Structures

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Choose a data structure or algorithm to write about.
Research and gather information about the chosen topic.
Organize your thoughts and create an outline.
Write the blog post or article, including clear explanations and examples.

Career center

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Data Analyst

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