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## Data Structures and Algorithms !(#) Data structure (English: data structure) is the way data is stored and organized in computers. Data structure is a collection of data elements that have certain logical relationships, use a certain storage structure in computers, and encapsulate corresponding operations. It contains three aspects: logical relationship, storage relationship, and operations. Different types of data structures are suitable for different types of applications, and some are even specifically designed for specific tasks. For example, computer networks rely on routing tables, and B-trees are highly suitable for database encapsulation. As applications become more complex and data becomes increasingly abundant, millions, billions, or even hundreds of billions of data points will emerge. Searching, inserting, or sorting operations on such massive amounts of data become increasingly slow. Data structures are designed to solve these problems. Learning data structures and algorithms can: * **Improve code efficiency**: Good data structures and algorithms can make programs run faster and use less memory * **Solve complex problems**: Many practical problems require specific data structures and algorithms to be solved effectively * **Essential for interviews**: Almost always tested in technical interviews * **Programming foundation**: It is the cornerstone of all advanced programming technologies Before you start reading this tutorial, you must have basic programming knowledge, such as concepts of programming like Python or Java. If you don't understand these concepts yet, we recommend reading our (#) or (#) first. We recommend using Python as the learning language, for the following reasons: | Feature | Python | Java | C++ | | --- | --- | --- | --- | | Learning Difficulty | ⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | | Syntax Simplicity | High | Medium | Low | | Execution Efficiency | Medium | High | Very High | | Community Support | Rich | Rich | Rich | | Recommendation Index | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | * * * ## Data Structures and Algorithms Explanation **Data structures** are like the way we organize items. Imagine your bookshelf: * If you pile things randomly, finding a book is difficult (inefficient data structure) * If you organize by category, author, or alphabetical order, finding books is fast (efficient data structure) **Algorithms** are the steps and methods for solving problems. Like a cooking recipe: * The same ingredients (data), different cooking methods (algorithms) produce different results * Some methods are fast and good, while others are time-consuming and laborious ### Basic Terminology | Term | Life Analogy | Technical Definition | | --- | --- | --- | | Data | Items | The carrier of information, symbols that describe objective things | | Data Element | Single item | The basic unit of data | | Data Structure | Way of organizing items | A collection of data elements that have one or more specific relationships | | Algorithm | Steps for doing things | Clear instructions for solving problems, completed within a limited time | ### Common Data Structures * **Stack:** Stack is a special linear list, which can only perform node insertion and deletion operations at one fixed end of the list. * **Queue:** Queue is similar to stack, also a special linear list. Unlike stack, queue only allows insertion operations at one end of the list and deletion operations at the other end. * **Array:** Array is an aggregate data type, which is an ordered collection of several variables of the same type. * **Linked List:** Linked list is a data structure where data elements are stored in a chain-like storage structure, which has the characteristic of being physically non-contiguous. * **Tree:** Tree is a typical nonlinear structure, which includes a finite set K of 2 nodes. * **Graph:** Graph is another nonlinear data structure. In graph structure, data nodes are generally called vertices, and edges are ordered pairs of vertices. * **Heap:** Heap is a special tree-like data structure, generally referring to binary heaps. * **Hash Table:** Hash table originates from hash function. Its idea is that if there is a record in the structure where the key equals T, then the record must be found at the storage location of F(T). This way, the record can be obtained directly without comparison operations. ### Common Algorithms Research on data structures: it is about how to organize data according to a certain logical structure, and choose appropriate storage representation methods to store logically organized data into the computer's memory. The purpose of algorithm research is to process data more effectively and improve data operation efficiency. Data operations are defined on the logical structure of data, but the specific implementation of operations is done on the storage structure. Generally, the following common operations exist: * **Search:** Search means finding nodes that meet certain conditions in the data structure. Generally, given the value of a certain field, find the node with that field value. * **Insert:** Add new nodes to the data structure. * **Delete:** Remove the specified node from the data structure. * **Update:** Change one or more field values of the specified node. * **Sort:** Reorder nodes in a specified order. For example, ascending or descending. * * * ## References Hello Algorithm: [https://github.com/krahets/hello-algo](https://github.com/krahets/hello-algo) Python Algorithms: [https://github.com/TheAlgorithms/Python](https://github.com/TheAlgorithms/Python). Play-with-Algorithms: [https://github.com/liuyubobobo/Play-with-Algorithms](https://github.com/liuyubobobo/Play-with-Algorithms).