Data Structures Overview

Data Structures

• Choosing a good data structure enables efficient performance of various operations, minimizing memory use and execution time for processing, retrieval, and storage.
• Data structures are fundamental components in virtually every program.
• Data structure design is interdependent with algorithm design. Effective data representation is crucial for developers and users. Data structures facilitate efficient organization, retrieval, management, and storage of data.
• Modification is straightforward and less time-consuming.
• Memory usage is optimized.
• Data representation is simplified, and large databases are easily accessible.

Classification of Data Structures

• Linear Data Structures: Elements are arranged in a single dimension (linearly). Examples include lists, stacks, queues, and arrays.
• Non-Linear Data Structures: Elements are organized in multiple dimensions (e.g., one-to-many, many-to-one, many-to-many). Examples include trees and graphs.

Primitive Data Structures

• Primitive data structures store a single value at a fixed location.
• Examples include integers, floats, characters, and pointers. Values for primitive data structures are provided by the programmer.
• Integer: Stores whole numbers (positive or negative). A long is needed if the integer range is exceeded.
• Float: Stores decimal values. A double is used for greater decimal precision.
• Boolean: Stores true/false values, typically used in conditional checks.
• Character: Stores a single character (uppercase or lowercase).

Non-Primitive Data Structures

• Non-primitive data structures store multiple values. Values can be stored at contiguous or non-contiguous locations.

Linear Data Structures (Examples)

• Array: A collection of elements of the same data type, stored in contiguous memory locations with zero-based indexing. Arrays offer constant-time (O(1)) access to elements. Arrays have a fixed size.
• String: An array of character data, terminated by a null character ('\0').
• Linear Data Structure: Data arranged sequentially (linearly), where each element is linked to the prior and subsequent element. Arrays, stacks, queues, and linked lists are examples.
• Static Data Structure: Has a fixed memory size, making access to elements easier. An example is an array.
• Dynamic Data Structure: Does not have a fixed size, allowing adjustments during runtime, which can be more memory-efficient. Queues and stacks are examples.

Data Structure Classification

• Data structures have numerous applications in solving mathematical and logical problems in daily life.
• Data structures, like arrays and linked lists, organize data to improve operations.

String Data Structures

• Strings are arrays of characters; the string ends with the null character ('\0').
• String Operations (Common): Concatenation, substring extraction, length determination, character access.

Multi-dimensional Arrays

• Arrays can have multiple dimensions, allowing for structured data storage.
• 1-dimensional array is a single row of elements.
• 2-dimensional arrays create a grid of rows and columns. Elements are accessed by row and column index.

Array Operations

• Traversal: Visiting all elements sequentially.
• Insertion: Adding an element at a specific index.
• Deletion: Removing an element at a specific index.
• Search: Finding an element by its value.
• Update: Modifying an existing element at a specific index.
• Display: Printing all elements.

Operations on Arrays

• Traversal: Visiting array elements sequentially.
• Insertion: Adding a new element at a designated index.
• Deletion: Removing an element from a specified location.
• Search: Determining the index of an element within the array.

Array Operations

• Array Traversal: Accessing each array element in a predetermined order (e.g., from beginning to end).
• Array Insertion: Inserting a new element into a specific location within the array.
• Array Deletion: Removing an element from a particular location within the array.
• Array Search: Locating an element in the array based on its value.

Linear Search

• Linear Search Algorithm: Systematically checks each element in an unsorted list until the target element is found or the end of the list is reached. The primary strategy of the linear search algorithm is iterating and searching for the element one by one in a sequence until a perfect match is found.
• Applications of Linear Search: Unsorted lists, searching linked lists.

Binary Search

• Binary Search Algorithm: An efficient search technique for ordered arrays that repeatedly divides the search interval in half to find the target element. Sorted arrays are a prerequisite for binary search's effectiveness.
• Applications of Binary Search: Searching in computer graphics, algorithms used in machine learning, and databases.

Explore the essential concepts of data structures, including linear and non-linear types. This quiz covers the importance of choosing the right data structure for efficient memory use and execution time. Understand how data structures facilitate effective management and representation of data.