Data Structures and Algorithms Notes

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📚 Resources to learn Data Structures & Algorithms

The following is a list of online courses, YouTube videos, and websites to help learn Data Structures & Algorithms. Note that these are not in order of completion.

All Resources in One Place	Resource Type	Links	Notes
LeetCode	website	Website Link
NeetCode	website	Website Link
AlgoMap	website	Website Link
TopSWE	website	Website Link
LeetCode Patterns	website	Website Link
NeetCode Roadmap	website	Website Link
VisuAlgo	website	Website Link
Data Structure Visualizations	website	Website Link
Big-O CheatSheet	website	Website Link
Data Structure Visualization	website	Website Link
NeetCode Courses	course	Course Link	Notes
Python Data Structures & Algorithms + LEETCODE Exercises	course	Course Link	Notes
The Last Algorithms Course You'll Need	course	Course Link
Algorithms and Data Structures Tutorial - Full Course for Beginners - FreeCodeCamp	youtube-videos	Video Link	Notes
NeetCode YouTube Videos	youtube-videos	Channel Link	Notes
Coding Interview University	github-repo	Repository Link
Awesome Algorithms	github-repo	Repository Link

🌐 Websites

• Roadmap to learn DSA by Neetcode
• VisuAlgo
• Data Structure Visualization

🎓 Courses

• Neetcode Courses
  • Algorithms & Data Structures for Beginners Notes
  • Advanced Algorithms Notes
  • System Design for Beginners
  • System Design Interview Notes
• Python Data Structures & Algorithms + LEETCODE Exercises
  • Notes from the course
• The Last Algorithms Course You'll Need
  • Notes from the course

🎥 YouTube Playlists

• Algorithms and Data Structures Tutorial - Full Course for Beginners - FreeCodeCamp
  • Notes from the video
• Neetcode Videos on YouTube
  • Notes from the videos

👾 Github Repositories

• Coding Interview University
• Awesome Algorithms

📚 Books

• Cracking the Coding Interview
• Introduction to Algorithms

🏋 Practicing

• Leetcode
• Neetcode
• TopSWE
• LeetCode Patterns

📝 Notes

Algorithms & Data Structures for Beginners - NeetCode

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Arrays

What is Data Structure?

• Data Structure is a way of structuring data inside of RAM of a computer.

How do ew store an array in RAM?

• RAM is measured in bytes. One byte is 8 bits. A bit can be thought of as a position that can store a digit, which has to be either 0 or 1.

NOTE!

• Arrays are always stored contiguously in RAM, meaning that they are stored one next to another (there is nothing between them.

NOTE!

• Static arrays are Fixed arrays. The biggest limitation of Static Arrays is that we cannot add / delete elements after creation. Technically, we can remove a value, but removing here only means overriding. We cannot actaully delete the value in memory. But, we can override by putting, let's say, 0 in the index location of it.

Big O Time Complexity of Static Arrays Operations

Operation	Big O Time
Read / Write i-th element	O(1)
Insert / Remove End	O(1)
Insert Middle	O(n)
Remove Middle	O(n)

NOTE!

• In Python and JavaScript, Dynamic Arrays are the default. In Java, we can use an Array List, and in C++, we can use a Vector.

What is Amortized Time Complexity?

• Amortized Time Complexity

Linked List

Recursion

Sorting

Binary Search

Trees

Backtracking

Heap & Priority Queue

Hashing

Graphs

Dynamic Programming

Bit Manipulation

Advanced Algorithms - NeetCode

Arrays

Linked Lists

Trees

Heaps

Backtracking

Graphs

Dynamic Programming

System Design for Beginners - NeetCode

Note

Coming Soon.

System Design Interview - NeetCode

Note

Coming Soon.

Python Data Structures & Algorithms + LEETCODE EXERCISES

Note

Coming Soon.

The Last Algorithms Course You'll Need

Note

Coming Soon.

Algorithms and Data Structures Tutorial - FreeCodeCamp

Introduction to Algorithms

Introduction to Algorithms

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• What is an Algorithm - An Algorithm is a set of steps or instructions for completing a certain task. For example, a recipe is an algorithm. To-Do List for a Morning Routine is an algorithm. However, in the context of Computer Science, an algorithm more specifically means a set a steps a program takes to finish a certain task.

• Time Complexity - Time Complexity is a measure of how long it takes the algorithm to run.

• Space Complexity - Space Complexity deals with the amount of memory taken up on the computer.

• Balance between Time & Space Complexity - A good algorithm needs to balance between Time and Space Complexity to be useful. For example, you can have a very fast algorithm but it may not matter if the algorithm consumes more memory than you have available.

• Running Time of an Algorithm - How long the algorithm runs for a given set of values until the output is called The Running Time or The Running Time of an Algorithm and is used to define Time Complexity.

• Linear Search (Sequential Search) - Linear Search is a sequential searching algorithm where we start from one end and check every element of the list until the desired element is found. It is the simplest searching algorithm.

• Binary Search (Hald Interval Search) - Binary Search is a searching algorithm for finding an element's position in a sorted way. In this approach, the element is always searched in the middle of a portion of an array. Binary Search can be implemented only on a sorted list of items. If the elements are not sorted already, we need to sort them first.

• Guidelines for defiing an Algorithm:

  • The steps in the Algorithm need to be in a specific order.
  • The steps also need to be distinct.
  • The algorithm should produce a result.
  • The algorithm should complete in a finite amount of time.

Introduction to Data Structures

Alogithms: Sorting and Searching

NeetCode YouTube Videos

Note

Coming Soon.