Data Structures Overview and Operations

Data Structures Overview

• Data is optimized information for processing and movement, including facts and figures stored in computers.
• Data items are single units of value.
• Group items are divisible into sub-items, while elementary items cannot be split.
• Attributes describe the qualities of entities, forming entity sets.
• Fields represent individual entity attributes.
• Records hold all the field values for a specific entity.
• Files are collections of records for entities in a set.

Data Structures and Operations

• Data structures are methods to organize data, allowing fast processing, storage, and retrieval.
• Algorithms are rules and instructions that transform data into useful programming elements.
• Operations on data structures include:
  • Searching: Locating a specific piece of data.
  • Sorting: Ordering data elements.
  • Insertion: Adding new data items.
  • Deletion: Removing data elements.
  • Updating: Modifying existing data parts.

Characteristics of Data Structures

• Linear structures organize data sequentially (e.g., arrays, linked lists).
• Non-linear structures allow for various pathways between elements (e.g., trees, graphs).
• Static structures have fixed sizes and formats in memory.
• Dynamic structures adjust their size and format as needed.
• Time complexity measures execution time for an operation. A lower number is faster.
• Space complexity measures memory usage. Less occupied space is better.
• Correctness refers to accurate implementation within the defined interface.

Execution Time Cases

• Worst case scenario: longest possible execution time for an operation.
• Average case: the average execution time of an operation.
• Best case scenario: the shortest possible execution time for an operation.

Linear Data Structures

• Linear data structures arrange elements sequentially, connecting each to its previous and next element.
• Types of linear data structures:
  • Stack: Last-In, First-Out (LIFO)
  • Array: Contiguous memory locations for similar data types.
  • Queue: First-In, First-Out (FIFO)
  • Linked List: Stores data in separate blocks linked together.

Non-Linear Data Structures

• The elements are arranged randomly in non-linear structures.
• They have various paths to reach other elements unlike linear models.
• Types of non-linear data structures:
  • Trees: Hierarchical structures with a root node, edges and child nodes.
  • Graphs: Nodes connected by edges for representing relationships.

Tree Data Structure

• Tree structures display a hierarchical arrangement, where nodes are linked through edges.
• Tree structures have the concept of a root node which is the top level of the tree .

Graph Data Structures

• In graph structures, nodes are linked to other nodes through edges.
• Graph structures do not employ any sequential or pre-determined order format for their data.
• Graphs are also referred to as nodes with edges.
• Graph structures may include types like undirected, unweighted, directed, or weighted graphs.

Linear vs Non-linear Data Structures

• Summarizes essential characteristics of linear and non-linear data structures in a table format.

Data Types

• Data types categorize data elements and are communicated between programmers and compilers.
• Types include:
  • Boolean: True or False values.
  • Integer: Positive, negative, and zero whole numbers.
  • Floating-point: Real numbers that use a formula for approximation.
  • Fixed-point: Fixed-length string of binary numbers.
  • Character: Strings of letters, numbers or special characters.
  • Pointers: Manage dynamically allocated memory blocks.
  • String: Sequence of characters.

How Data Structures Are Used

• Data structures support the physical representation of abstract data types used in software development.
• They are fundamental to algorithm design and software implementation.
• Data structures have various crucial functionalities.

Storing Data

• Data structures are used to efficiently store and access data in database management systems.

Managing Resources and Services

• Data structures like linked lists, file structure trees, and process scheduling queues are used in operating systems.

Data Exchange

• Data structures facilitate communication across different applications, such as TCP/IP packets.

Ordering and Sorting

• Data structures like binary search trees simplify ordering and sorting processes.

Indexing

• Complex structures like B-trees provide efficient indexing for items in storage systems like databases.

Searching

• Common search techniques leverage data structures like B-trees, hash tables, and binary search trees.

Scalability

• Data structures support data allocation and management across distributed systems in big data applications.

Choosing a Data Structure

• Analyze the problem requirements and choose the most suitable data structure for efficient operations.

Supported Operations

• Operations on supported underlying data types can often be transferred to other unrelated data types.
• Data structures may convert one data type to another as needed.

Computational Complexity

• Computational complexity measures an algorithm's time and memory consumption.
• This measure helps in planning, understanding and utilizing data structures correctly.

Programming Elegance

• Elegant programs are simple to understand, use effective words, and are explicit about their underlying logic.
• Clear, explicit coding strategies lead to reusable and maintainable code.

Data Types and Their Relationship

• Abstract data types are defined by their behavior, incorporating structures like graphs, queues, stacks, and sets.
• Composite data structures consist of primitive data types combined in specific ways (e.g., arrays, classes, records, strings, structs).

Advantages of Data Structures

• Key advantages of using data structures include enhanced data storage and retrieval, efficient processing of data.

This quiz covers the fundamentals of data structures, including their definitions, operations, and how data is organized for efficient processing. You'll learn about data items, records, files, and the various algorithms used for searching, sorting, inserting, deleting, and updating data. Perfect for those studying computer science or data management.