The Essential Algorithms and Data Structures

The Essential Algorithms and Data Structures, available at $19.99, has an average rating of 4.05, with 96 lectures, based on 33 reviews, and has 3256 subscribers.

You will learn about Leverage popular algorithms and data structures in your own programs to solve complex problems efficiently Design their own algorithms and data structures using industry standard practices Break down the time complexity of a piece of code and understand how it will scale with input size Perfectly answer the most common algorithm based interview questions This course is ideal for individuals who are Programmers who started to explore the basics of their language and want to take their knowledge to the next level or College students who want to have an additional resource for a Algorithms and Data Structures course or AP Computer Science A students to gain more knowledge on the algorithm based parts of the exam or Individuals who want to turn their programming hobby into a well-paid job It is particularly useful for Programmers who started to explore the basics of their language and want to take their knowledge to the next level or College students who want to have an additional resource for a Algorithms and Data Structures course or AP Computer Science A students to gain more knowledge on the algorithm based parts of the exam or Individuals who want to turn their programming hobby into a well-paid job.

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Summary

Title: The Essential Algorithms and Data Structures

Price: $19.99

Average Rating: 4.05

Number of Lectures: 96

Number of Published Lectures: 96

Number of Curriculum Items: 96

Number of Published Curriculum Objects: 96

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Leverage popular algorithms and data structures in your own programs to solve complex problems efficiently

Design their own algorithms and data structures using industry standard practices

Break down the time complexity of a piece of code and understand how it will scale with input size

Perfectly answer the most common algorithm based interview questions

Who Should Attend

Programmers who started to explore the basics of their language and want to take their knowledge to the next level

College students who want to have an additional resource for a Algorithms and Data Structures course

AP Computer Science A students to gain more knowledge on the algorithm based parts of the exam

Individuals who want to turn their programming hobby into a well-paid job

Target Audiences

Programmers who started to explore the basics of their language and want to take their knowledge to the next level

College students who want to have an additional resource for a Algorithms and Data Structures course

AP Computer Science A students to gain more knowledge on the algorithm based parts of the exam

Individuals who want to turn their programming hobby into a well-paid job

Imagine you walk into work and your boss says, “I just got a list of 2 billion numbers, can you sort them for me as soon as possible?”

You might be shocked and wonder where to even start the problem. Do you try and do it all by hand and spend the next couple years trying to sort even a small fraction of the values? Or is there some better way

The Essential Algorithms and Data Structures is the most comprehensive course on the topic on Udemy and together we will learn how to solve problems like these and even more complicated problems. Algorithms are a guaranteed way of solving a type of problem that works in a predictable fashion with the data. Algorithms like sorting algorithms can be used to sort 10 values or a billion values and won’t need any modifications to work with either set. Other algorithms allow us to efficiently search a set of data or find the lowest cost option to connect a series of points on a graph. Algorithms are like blueprints that we use to solve problems in our programs.

Data structures are unique ways of storing data that are optimized for certain situations. Data structures like a priority queue allow us to model how a CPU?processes requests, or how to efficiently model a set of cities and interconnecting flights. Choosing a good data structure to store data can make programs millions of times faster than a bad choice. Data structures are like the power tools of programming that let us drastically speed up our programs.

In this course we’re going to combine data structures with algorithms to create a powerful arsenal you can use to solve whatever problems show up in your code. We start by discussing time complexity and how we use it to analyze algorithms. We then cover the most important algorithms for interviews and discuss how to perfectly answer common interview questions. We then shift our focus to being able to search efficiently depending on the starting set of data. In addition, we cover the eight most essential algorithms for sorting and discuses when to use each of them. After that, we cover fundamentals for data structures like generics and recursions that are essential for almost all data structures. Then, we shift our focus to the essential data structures like maps and sets that every powerful programmer is expected to have mastered. Next, we go into detail with the three most important types of trees (Binary Search Trees, Red-Black Trees, and AVL Trees).?Finally, we wrap up our discussions with hashing and graphs, which are essential to higher-order approaches for computer science.

While other courses on the market focus entirely on theory we will place a major emphasis on being able to actually implement the algorithms and data structures we cover. We’ll go over how to modify an algorithm or data structure for your situations and will always be looking at pseudo code that helps us understand how an algorithm or data structure works. In addition, we always cover the theory in detail and focus on understanding how and why a data structure is efficient or the details of an algorithms approach, so you can implement it in your language of choice.

We will take a Java-based approach for our discussions of how to implement an algorithm but this doesn’t mean you have to know Java.?Java is a generic style language whose attributes are almost identical to other major languages like C++ or Python.

If you have any questions you can depend on prompt communication from me. I?pride myself on responding to questions within 24 hours and work hard to ensure that each student is satisfied. If you have any questions at any time my email and message box are open 24/7!

I hope you’re as excited as I am to begin this journey through the most interesting parts of Computer Science.?Personally I can say that once I learned about algorithms and data structures I was pleasantly surprised with how many new problems I could solve and how fluently I?could understand professional level discussions of programming. If you want to go from being a simple programming student to a true software engineer, then join the tens of thousands of satisfied students and enroll today!

Course Curriculum

Chapter 1: Introduction to Algorithms

Lecture 1: Algorithm Introduction

Lecture 2: Algorithm Design

Lecture 3: Algorithm Notation and Vocabulary

Lecture 4: Growth Rate Comparison

Chapter 2: Interview Algorithms

Lecture 1: GCD Algorithms: Brute Force

Lecture 2: GCD Algorithms: Stein

Lecture 3: GCD Algorithms: Euclid

Lecture 4: FizzBuzz

Lecture 5: String Reversal

Lecture 6: Prime Number Generation

Lecture 7: Prime Number Generation Part 2

Lecture 8: Palindromes

Chapter 3: Searching and Order Statistics

Lecture 1: Linear Search

Lecture 2: Binary Search

Lecture 3: Exponential Search

Lecture 4: Interpolation Search

Lecture 5: Jump Search

Lecture 6: Finding the Min/Max/Nth Lowest

Lecture 7: Finding the Min/Max/Nth Lowest Part 2

Lecture 8: Min/Max/Nth Lowest Part 3

Chapter 4: Sorting Algorithms

Lecture 1: Sorting Introduction

Lecture 2: Insertion Sort Principles

Lecture 3: Insertion Sort Complexity

Lecture 4: Selection Sort Principles

Lecture 5: Selection Sort Complexity

Lecture 6: Bubble Sort Principles

Lecture 7: Bubble Sort Complexity

Lecture 8: Quicksort Principles

Lecture 9: Quicksort Implementation

Lecture 10: Quicksort Complexity

Lecture 11: Mergesort Basics

Lecture 12: Mergesort Implementation

Lecture 13: Mergesort Complexity

Lecture 14: Heapsort Part 1

Lecture 15: Heapsort Part 2

Lecture 16: Heapsort Part 3

Lecture 17: Heapsort Part 4

Lecture 18: Heapsort Part 5

Lecture 19: Heapsort Part 6

Lecture 20: Counting Sort Part 1

Lecture 21: Counting Sort Part 2

Lecture 22: Counting Sort Part 3

Lecture 23: Counting Sort Part 4

Lecture 24: Radix Sort Part 1

Lecture 25: Radix Sort Part 2

Lecture 26: Radix Sort Part 3

Chapter 5: Basics of Data Structures

Lecture 1: Data Structures Basics

Lecture 2: Recursion Part 1

Lecture 3: Recursion Expanded and Tail Recursion

Lecture 4: Generics

Chapter 6: Fundamental Data Structures

Lecture 1: Collections Introduction

Lecture 2: Collections Expanded

Lecture 3: ArrayLists Basics

Lecture 4: ArrayLists Pros and Cons

Lecture 5: LinkedLists Basics

Lecture 6: LinkedLists Expanded

Lecture 7: Stacks Basics

Lecture 8: Stacks Expanded

Lecture 9: Queues

Lecture 10: Priority Queues Part 1

Lecture 11: Priority Queues Part 2

Lecture 12: Priority Queues Part 3

Lecture 13: Priority Queues Part 4

Lecture 14: Maps Part 1

Lecture 15: Maps Part 2

Lecture 16: Maps Part 3

Lecture 17: Sets Part 1

Lecture 18: Sets Part 2

Lecture 19: Sets Part 3

Chapter 7: Trees

Lecture 1: Binary Search Trees Part 1

Lecture 2: Binary Search Trees Part 2

Lecture 3: Binary Search Trees Part 3

Lecture 4: Binary Search Trees Part 4

Lecture 5: Binary Search Trees Part 5

Lecture 6: AVL Trees Part 1

Lecture 7: AVL Trees Part 2

Lecture 8: AVL Trees Part 3

Lecture 9: AVL Trees Part 4

Lecture 10: AVL Trees Part 5

Lecture 11: Red-Black Trees Part 1

Lecture 12: Red-Black Trees Part 2

Lecture 13: Red-Black Trees Part 3

Lecture 14: Red-Black Trees Part 4

Lecture 15: Red-Black Trees Part 5

Chapter 8: Advanced Data Structures

Lecture 1: Hashing Part 1

Lecture 2: Hashing Part 2

Lecture 3: Hashing Part 3

Lecture 4: Hashing Part 4

Lecture 5: Weighted and Unweighted Graphs Part 1

Lecture 6: Weighted and Unweighted Graphs Part 2

Lecture 7: Weighted and Unweighted Graphs Part 3

Lecture 8: Weighted and Unweighted Graphs Part 4

Instructors

Anirudh Balasubramanian
Engineer and Teacher

Rating Distribution

1 stars: 2 votes

2 stars: 1 votes

3 stars: 6 votes

4 stars: 12 votes

5 stars: 12 votes

Frequently Asked Questions

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