Data Structure Unit 1: Data and Information

Data and Information

• Data refers to a collection of raw facts and figures that lack meaning.
• Information is processed data that has meaning, derived from data manipulation.
• Forms of data representation include images, text, numbers, audio, and video.

Difference between Data and Information

• Data is individual unrefined facts; information is refined output with logical meaning.
• Data records provide observations; information is based on analysis and context.

Data Structure

• Defined as a way to organize, manage, and store data for efficient access and modification.
• Describes relationships among data values and functions applicable to the data.
• Can be viewed as a mathematical or logical model for organizing data items.

Need for Data Structure

• Essential for effective computer operation, including data representation and access methods.
• Helps in systematic problem-solving and data manipulation.

Types of Data Structures

• Primitive: Basic data types like integers, floats, and characters.
• Non-Primitive: More complex structures such as linear and non-linear data types.

Short Description of Various Data Structures

• Array: A linear collection of a finite number of data elements; indexed for easy access.
• Linked List: A collection of nodes where each node contains a pointer to the next; useful for dynamic data.
• Stack: A collection where insertion and deletion occur at one end (top), following Last In First Out (LIFO) principle.
• Queue: A collection where insertion occurs at one end and deletion at the other, following First In First Out (FIFO).
• Tree: A non-linear structure representing hierarchical relationships, where elements can have multiple child nodes.
• Graph: A non-linear data structure representing relationships without a strict hierarchy, consisting of nodes and edges.

Characteristics of Data Structures

• Linear sequences allow straightforward traversal and easy implementation.
• Non-linear structures feature connections to multiple items, enabling more complex relationships.

Data Structure Study Topics

• Memory required to store data.
• Time required for data processing.
• Representation of data in memory.
• Operations performed on data.

Different Data Types

• Primitive Data Type: Deals with basic vales and operations.
• Abstract Data Type: Concerns set values and operations without focusing on implementation.

File Organization (FO)

• Refers to how data is stored in files, affecting access methods, efficiency, and storage flexibility.

Importance of Algorithms

• Algorithms consist of finite steps designed to solve specific problems, manipulating data structures effectively.

Characteristics of an Algorithm

• Input: Requires specific data to function.
• Process: Performs operations on input data.
• Output: Produces resulting data.
• Finiteness: Must terminate after a finite number of steps.
• Effectiveness: Each operation must contribute to solving the problem.

Algorithm Analysis Requirements

• Efficient problem-solving relies on suitable data structures and well-chosen algorithms.
• Factors affecting algorithm efficiency include programming requirements, space requirements, and time requirements.

Space and Time Requirements of an Algorithm

• Space Requirements: Dictated by data storage needs for variables and instructions, including dynamic allocations.
• Time Requirements: Estimating execution time is vital for assessing program performance against acceptable limits.

Complexity of an Algorithm

• Measures time and space requirements based on input size.
• Time complexity assesses execution time as a function of input length.
• Space complexity quantifies memory usage based on input length.
• Lower time and space requirements equate to lower algorithm complexity.

Learn about the fundamental concepts of data and information, including data representation forms such as images, text, numbers, and audio/video. Understand the difference between data and information.