Building reliable and highly performant software requires knowledge that goes beyond a certain programming language or framework. It requires a solid understanding of how data is organized in memory, how it can be manipulated, sorted or searched into. There’s a reason why all the big tech companies such as Google, Amazon or Netflix focus their technical interviews on those topics. Whether you do mobile apps, websites, games, machine learning or any other work that involves coding, you need a good grasp of Data Structures and Algorithms.
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Building reliable and highly performant software requires knowledge that goes beyond a certain programming language or framework. It requires a solid understanding of how data is organized in memory, how it can be manipulated, sorted or searched into. There’s a reason why all the big tech companies such as Google, Amazon or Netflix focus their technical interviews on those topics. Whether you do mobile apps, websites, games, machine learning or any other work that involves coding, you need a good grasp of Data Structures and Algorithms.
Building reliable and highly performant software requires knowledge that goes beyond a certain programming language or framework. It requires a solid understanding of how data is organized in memory, how it can be manipulated, sorted or searched into. There’s a reason why all the big tech companies such as Google, Amazon or Netflix focus their technical interviews on those topics. Whether you do mobile apps, websites, games, machine learning or any other work that involves coding, you need a good grasp of Data Structures and Algorithms.
Many self taught developers and aspiring engineers often feel they lack the knowledge when having to decide on the right data structure or the right approach for solving a problem. If you ever felt that way, this material is the right choice for you. This course packs months of Computer Science subject matter to get you on the same level of proficiency as someone with a Computer Science degree.
What sets us apart ?
Every video begins with an in depth analysis of the topic at hand. At this stage we won’t write any code yet, but rather learn how to approach the problem, think of ways we could solve it and build a mental model of the solution.
We then go on to code the algorithm step by step. But we don’t stop there. We take one or more examples and walk through the code line by line. And we mean that literally. You will see how the code runs from top to bottom and how data flows and changes during execution. We find this method the absolute best way to really understand the inner workings of an algorithm.
We’ll also analyze the time performance and space utilization of every algorithm and method we write using the Big O Notation. We’ll talk about the strengths and weaknesses of each data structure and discuss their real world usage. Apart from all of that, you’ll also learn things like recursion, how computers work under the hood, problem solving techniques, common programming patterns and much more.
What does this course cover ?
How computers work under the hood
What a data structure is
What an algorithm is
Problem solving techniques
Big O Notation - how to analyze the time performance and space utilization of algorithms. This is done for every single function/algorithm we write.
Gain a deeper understanding of how code works
Recursion
Data Structures:
Arrays
Hash Tables
Singly Linked Lists
Doubly Linked Lists
Stacks
Queues
Binary Search Trees
Tree Traversal
Heaps
Graphs
Sorting Algorithms:
Insertion Sort
Selection Sort
Bubble Sort
Shell Sort
Heap Sort
Merge Sort
Quick Sort
Radix Sort
Thanks for considering, and I hope this course will help you in your journey. Happy learning.
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What's inside
Learning objectives
- Understand how computers store and work with data
- Analyze the space and memory utilization of any algorithm using the big o notation
- Dive deep into the mechanisms of function execution and demystify the concept of recursion
- Implement from scratch the most common 9 data structures
- Understand how the best known 8 sorting algorithms work
- Learn the searching / traversing algorithms for trees and graphs
- Improve your problem solving skills
- Learn the behind the scenes of the basic data structures (arrays and hash maps)
- Understand common programming patterns
- Learn everything you need to know for the technical interview
- Understand how computers store and work with data
- Analyze the space and memory utilization of any algorithm using the big o notation
- Dive deep into the mechanisms of function execution and demystify the concept of recursion
- Implement from scratch the most common 9 data structures
- Understand how the best known 8 sorting algorithms work
- Learn the searching / traversing algorithms for trees and graphs
- Improve your problem solving skills
- Learn the behind the scenes of the basic data structures (arrays and hash maps)
- Understand common programming patterns
- Learn everything you need to know for the technical interview
Syllabus
Introduction
Course objectives, structure and more
How computers work
What are algorithms
Read more
Syllabus
Good to know
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Activities
Master Insertion Sort
Show steps
Inserting a value into its correct position in a sorted list is a fundamental operation of sorting algorithms. This will familiarize you with the Insertion Sort algorithm in particular.
Browse courses on
Insertion Sort
Show steps
-
Create an array of unordered numbers.
-
Pick an element in the array.
-
Compare the element to the ones on its left.
-
Shift the sorted elements to the right until the correct position is found.
-
Insert the element into the correct position.
Explain Recursion to Yourself
Show steps
Give yourself a foundation in recursion by creating content that explains its mechanism and how it relates to functions and the call stack.
Browse courses on
Recursion
Show steps
-
Define recursion and explain how it works.
-
Explain the use of the call stack during recursive calls.
-
Illustrate recursion with a simple example, such as finding the factorial of a number.
Learn Binary Search Tree Traversal Algorithms
Show steps
BinaryTree traversal algorithms are key to the effective use of this data structure, as it helps you search, insert, and delete data. You will familiarize yourself with depth-first search and breadth-first search techniques.
Browse courses on
Binary Search Tree
Show steps
-
Create a binary search tree with sample data.
-
Implement a depth-first search algorithm and test it on the tree.
-
Implement a breadth-first search algorithm and test it on the tree.
-
Compare the results and time complexity of both algorithms.
Show all three activities
Master Insertion Sort
Show steps
Inserting a value into its correct position in a sorted list is a fundamental operation of sorting algorithms. This will familiarize you with the Insertion Sort algorithm in particular.
Browse courses on
Insertion Sort
Show steps
- Create an array of unordered numbers.
- Pick an element in the array.
- Compare the element to the ones on its left.
- Shift the sorted elements to the right until the correct position is found.
- Insert the element into the correct position.
Explain Recursion to Yourself
Show steps
Give yourself a foundation in recursion by creating content that explains its mechanism and how it relates to functions and the call stack.
Browse courses on
Recursion
Show steps
- Define recursion and explain how it works.
- Explain the use of the call stack during recursive calls.
- Illustrate recursion with a simple example, such as finding the factorial of a number.
Learn Binary Search Tree Traversal Algorithms
Show steps
BinaryTree traversal algorithms are key to the effective use of this data structure, as it helps you search, insert, and delete data. You will familiarize yourself with depth-first search and breadth-first search techniques.
Browse courses on
Binary Search Tree
Show steps
- Create a binary search tree with sample data.
- Implement a depth-first search algorithm and test it on the tree.
- Implement a breadth-first search algorithm and test it on the tree.
- Compare the results and time complexity of both algorithms.
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