What are the main features of codebasics/data-structures-algorithms-python?

The main features of codebasics/data-structures-algorithms-python are: Data Structures and Algorithms, Data Structure Implementations, Fundamental Data Structures, Coding Interview Preparation, Iterative Traversals, Problem Solving Guides, Search Algorithms, Sorting Algorithms.

Data Structures Algorithms Python - إتقان الخ…

هذا المشروع عبارة عن مورد تعليمي يوفر منهجاً منظماً لإتقان مفاهيم علوم الكمبيوتر الأساسية، والمنطق الخوارزمي، وتنفيذ هياكل البيانات باستخدام Python. يعمل كبرنامج تعليمي شامل لفهم كيفية تنظيم المعلومات بفعالية وحل التحديات الحسابية المعقدة من خلال تقنيات البرمجة المنهجية.

يركز المستودع على التطبيق العملي لهياكل البيانات الأساسية، بما في ذلك المصفوفات، والقوائم المرتبطة، وجداول التجزئة، والمكدسات، والطوابير، والأشجار. يؤكد المشروع على تطوير مهارات حل المشكلات الخوارزمية من خلال تغطية الطرق القياسية لفرز المجموعات والبحث عن عناصر محددة، إلى جانب تقنيات تحليل تعقيد الوقت والمساحة للكود.

بعيداً عن التنفيذ الأساسي، تتناول المواد المفاهيم الحسابية التأسيسية مثل المنطق المتكرر، والاجتياز التكراري، وإدارة الذاكرة. تم تصميم هذه الموارد لدعم الإعداد الفني لمقابلات هندسة البرمجيات من خلال توفير تمارين توضح كيفية بناء أنظمة بيانات فعالة وتحسين الأداء للتطبيقات القابلة للتوسع. يتم تقديم المحتوى من خلال سلسلة من دفاتر Jupyter التي تجمع بين التفسيرات النظرية وأمثلة البرمجة العملية.

Features

Data Structures and Algorithms - Serves as a comprehensive tutorial for learning data structures and algorithms using Python.
Data Structure Implementations - Demonstrates how to build and manage fundamental data storage components through practical code examples.
Fundamental Data Structures - Provides reference implementations for fundamental data structures like linked lists, stacks, queues, and trees.
Coding Interview Preparation - Offers study materials and exercises designed to prepare developers for technical coding interviews.
Iterative Traversals - Demonstrates loop-based and stack-based traversal techniques for navigating data structures.
Problem Solving Guides - Offers a structured curriculum for mastering algorithmic and logical problem-solving techniques.
Search Algorithms - Implements systematic search algorithms to locate elements within data structures efficiently.
Sorting Algorithms - Provides educational implementations of various sorting algorithms to organize data collections.
Sorting and Searching Workflows - Provides workflows for implementing and comparing various data organization and retrieval techniques.
Computer Science Education - Provides foundational learning materials covering core computer science principles and software engineering practices.
Recursive Problem Solving - Explains recursive decomposition techniques to solve complex computational problems.
Complexity Analysis - Provides systematic methods for evaluating the time and space efficiency of algorithms and data structures.
Algorithmic Performance Optimizations - Teaches techniques for analyzing and improving the time and space complexity of code.
Dynamic Arrays - Implements resizable array structures that manage capacity and storage dynamically.
Linked Data Structures - Constructs complex data structures using pointer-based node linking for non-contiguous memory chains.

aspittel/coding-cheat-sheets

1,337عرض على GitHub

This project is a comprehensive reference guide for computer science fundamentals, providing structured summaries of essential data structures and algorithmic principles. It serves as a technical resource for developers to review core programming concepts, memory layouts, and operational characteristics required for software development and technical assessments. The collection distinguishes itself by offering concise, implementation-focused documentation for a wide range of standard techniques. It covers the mechanics of various sorting and searching algorithms, graph and tree traversal stra

theja-m/Data-Structures-and-Algorithms

1,656عرض على GitHub

This repository serves as an educational resource for computer science concepts, providing a collection of fundamental data structures and algorithmic patterns implemented in Python. It functions as a programming reference for developers seeking to understand standard software engineering patterns and data manipulation strategies. The project focuses on the construction of essential storage formats, including arrays, graphs, hash tables, linked lists, stacks, and queues. It also provides implementations for standard algorithmic techniques such as dynamic programming, recursion, sorting, and g

ua-nick/Data-Structures-and-Algorithms

2,773عرض على GitHub

This repository is a collection of fundamental data structures and classic algorithms implemented in Go, designed for educational study and technical skill development. It provides a comprehensive library of standard storage primitives and computational procedures intended to demonstrate efficient data organization and logic. The project distinguishes itself through a focus on core design principles, utilizing language-level type parameters and interface-based polymorphism to maintain type safety and modularity. Implementations rely on iterative logic and direct memory management via pointers

This project is a comprehensive knowledge base and study resource designed for mastering technical interviews. It provides structured guides, roadmaps, and curricula focused on data structures, algorithms, system design, and frontend engineering to help candidates prepare for software engineering screenings. The repository distinguishes itself by offering a holistic approach to professional advancement. Beyond technical drills, it includes a career development handbook covering resume optimization, salary benchmarking, and strategic negotiation coaching. It also provides detailed methodologie

codebasicsdata-structures-algorithms-python