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Big O Notation: The Secret to Writing Efficient Algorithms 🔻

From simple array operations to complex sorting algorithms, understanding the Big O Notation is critical for building high-performance software solutions.

O(1)

This is the constant time notation. The runtime remains steady regardless of input size. For example, accessing an element in an array by index and inserting/deleting an element in a hash table.

O(n)

Linear time notation. The runtime grows in direct proportion to the input size. For example, finding the max or min element in an unsorted array.

O(log n)

Logarithmic time notation. The runtime increases slowly as the input grows. For example, a binary search on a sorted array and operations on balanced binary search trees.

O(n^2)

Quadratic time notation. The runtime grows exponentially with input size. For example, simple sorting algorithms like bubble sort, insertion sort, and selection sort.

O(n^3)

Cubic time notation. The runtime escalates rapidly as the input size increases. For example, multiplying two dense matrices using the naive algorithm.

O(n logn)

Linearithmic time notation. This is a blend of linear and logarithmic growth. For example, efficient sorting algorithms like merge sort, quick sort, and heap sort

O(2^n)

Exponential time notation. The runtime doubles with each new input element. For example, recursive algorithms solve problems by dividing them into multiple subproblems.

O(n!)

Factorial time notation. Runtime skyrockets with input size. For example, permutation-generation problems.

O(sqrt(n))

Square root time notation. Runtime increases relative to the input’s square root. For example, searching within a range such as the Sieve of Eratosthenes for finding all primes up to n.

مسارات تعلم شاملة في مجالات البرمجة 🔻

المسارات دي هتساعدك تخطط رحلة التعلم خلال الـ 12 شهر الجايين (أو أكتر في مجال الويب)، وده من غير ما تضيع وقتك في البحث عن إيه اللي المفروض تتعلمه في كل مجال. ✅

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https://www.linkedin.com/posts/dev-alisamir_softwaredeveloper-softwaredevelopment-roadmap-activity-7250153662952456192-IM-C

مكتبة جافا سكريبت رايقة هتظبطلك الـ Scrolling...⚡️

Lenis JS Library 🔻

https://www.linkedin.com/posts/dev-alisamir_javascript-js-animations-activity-7250760742205853696-ef_x

كورس الذكاء الاصطناعي الشامل لمبادرة رواد مصر 🔻

Advanced Machine Learning 2024 Course + Microsoft Azure AI Fundamentals | Arabic

https://youtube.com/playlist?list=PLPBnj6azlABapMXzdpFXBScfZerZygcrz&si=ojtFJ_zTaJVTrwXZ

Get your codebase ready for React 19 🚀

https://thoughtbot.com/blog/get-your-codebase-ready-for-react-19

API vs SDK 🔻

API (Application Programming Interface) and SDK (Software Development Kit) are essential tools in the software development world, but they serve distinct purposes:

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API:

An API is a set of rules and protocols that allows different software applications and services to communicate with each other.


» It defines how software components should interact.

» Facilitates data exchange and functionality access between software components.

» Typically consists of endpoints, requests, and responses.

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SDK:

An SDK is a comprehensive package of tools, libraries, sample code, and documentation that assists developers in building applications for a particular platform, framework, or hardware.


» Offers higher-level abstractions, simplifying development for a specific platform.

» Tailored to specific platforms or frameworks, ensuring compatibility and optimal performance on that platform.

» Offer access to advanced features and capabilities specific to the platform, which might be otherwise challenging to implement from scratch.

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The choice between APIs and SDKs depends on the development goals and requirements of the project.