Types of Data Structures

Study Notes

Arrays: A collection of elements of the same data type stored in contiguous memory locations.
Linked Lists: A dynamic collection of elements, where each element points to the next element.
Stacks: A Last-In-First-Out (LIFO) data structure, where elements are added and removed from the top.
Queues: A First-In-First-Out (FIFO) data structure, where elements are added to the end and removed from the front.
Trees: A hierarchical data structure, where each node has a value and zero or more child nodes.
Graphs: A non-linear data structure, where nodes are connected by edges.
Hash Tables: A data structure that stores key-value pairs, using a hash function to map keys to indices.

Time Complexity: The amount of time an operation takes, measured in Big O notation.
Space Complexity: The amount of memory a data structure uses, measured in Big O notation.
Efficiency: The trade-off between time and space complexity.

Database Management: Data structures are used to store and retrieve data in databases.
File Systems: Data structures are used to organize and manage files on a computer.
Compilers: Data structures are used to parse and analyze code.
Algorithms: Data structures are used to implement algorithms, such as sorting and searching.

Time vs. Space: Many data structures trade off time complexity for space complexity, or vice versa.
Cache Efficiency: Data structures can be optimized for cache efficiency, reducing the time it takes to access elements.
Scalability: Data structures can be designed to scale to large amounts of data, or to handle high traffic.

Arrays store elements of the same data type in contiguous memory locations, allowing for efficient indexing and access.
Linked Lists are dynamic collections of elements, where each element points to the next, enabling efficient insertion and deletion.

Insertion involves adding a new element to a data structure, which can be done using various algorithms, such as shifting elements in an array or updating pointers in a linked list.
Deletion involves removing an element from a data structure, which can be done using various algorithms, such as shifting elements in an array or updating pointers in a linked list.
Traversal involves iterating through the elements of a data structure, which can be done using algorithms such as iteration or recursion.
Search involves finding a specific element in a data structure, which can be done using algorithms such as linear search or binary search.
Sorting involves arranging the elements of a data structure in a specific order, which can be done using algorithms such as bubble sort or merge sort.

Time Complexity measures the amount of time an operation takes, expressed in Big O notation, which helps analyze the efficiency of algorithms.
Space Complexity measures the amount of memory a data structure uses, expressed in Big O notation, which helps analyze the efficiency of algorithms.
Efficiency involves finding a balance between time and space complexity, as optimizing one often comes at the expense of the other.

Database Management systems use data structures to store and retrieve data efficiently.
File Systems use data structures to organize and manage files on a computer efficiently.
Compilers use data structures to parse and analyze code efficiently.
Algorithms use data structures to implement various algorithms, such as sorting and searching, efficiently.

Time vs. Space Trade-Off: Many data structures trade off time complexity for space complexity, or vice versa, requiring a balance between the two.
Cache Efficiency: Data structures can be optimized for cache efficiency, reducing the time it takes to access elements.
Scalability: Data structures can be designed to scale to large amounts of data, or to handle high traffic, requiring efficient algorithms and data structures.