Data Representation: Difference between revisions

Revision as of 08:19, 6 July 2025

Data Representation

Data representation is a core concept in computer science and information technology that refers to the methods and techniques used to encode and structure information for processing and storage. It encompasses various formats through which data can be organized, interpreted, and manipulated by computational systems. Proper data representation is critical for effective data management, optimization of algorithms, and the facilitation of data communication across various platforms.

Introduction

Data representation serves as a foundation for various disciplines within computer science, including data science, databases, computer programming, and software engineering. It plays a pivotal role in converting real-world information into formats that computers can efficiently utilize. This section elucidates the fundamental principles behind data representation, the essential types of data, and the processes involved in encoding and decoding information.

The primary goals of data representation include preserving the integrity of the data, optimizing retrieval and processing speeds, and facilitating effective communication between different systems. Common forms of data representation include numerical, textual, visual, and multimedia formats, each tailored to specific applications and environments.

History or Background

The evolution of data representation dates back to the early days of computing, where the binary numeral system became foundational for digital data encoding. In the 1940s, the introduction of electronic computers marked the beginning of modern data representation methods, with early systems using punch cards and magnetic tapes to represent and store information.

As computing technology progressed, various encoding schemes were developed to enhance data representation. For instance, the ASCII (American Standard Code for Information Interchange) character encoding system emerged in the 1960s, providing a standardized way to represent text in computers. In the 1980s, the Unicode standard was introduced to accommodate a broader range of characters from diverse languages, significantly expanding the capability for textual data representation.

The rise of multimedia applications in the 1990s and 2000s led to the development of new formats for representing audio, video, and images, including JPEG, MP3, and MPEG. These advances underscored the importance of efficient data representation methods in the digital age, catering to an ever-increasing demand for high-quality multimedia content.

Design or Architecture

Data representation involves numerous architectural considerations, including the choice of encoding schemes, structures, and the underlying technologies that support data processing and storage. This section delves into the design aspects of data representation by exploring different data types and their respective structures.

Types of Data

Data can be categorized into several types, each with distinct representation needs:

Primitive Data Types: Basic data types such as integers, floats, characters, and booleans. Primitive types require simple representations and are often utilized in programming languages for variable storage.
Complex Data Types: Higher-level data types composed of one or more primitive types, including arrays, lists, tuples, and objects, which enable more sophisticated data manipulation.
Structured Data: Data organized in a fixed format, such as databases, which facilitate structured querying and reporting. Relational databases make use of tables with predefined schemas to represent data entries.
Unstructured Data: Data that lacks a predefined structure, such as text documents, images, and multimedia files, often requiring advanced techniques for parsing and analysis.
Semi-structured Data: Data that has some organizational properties but does not conform to a rigid structure, such as JSON or XML. This type of data representation maintains flexibility and allows for diverse content variations.

Encoding Schemes

Several encoding schemes are utilized to represent data accurately:

Binary Encoding: The most fundamental form of data representation, utilizing two symbols, 0 and 1, to represent all types of data within a computer system.
Character Encoding: Various character encoding systems, such as ASCII and Unicode, allow for the representation of text, providing mechanisms to encode characters into binary values.
Numerical Encoding: Methods for encoding numbers, including fixed-point and floating-point representations, which determine how real numbers are stored in binary format.

Usage and Implementation

Effective data representation is essential for the performance of computing systems and directly impacts software development, database management, and data analysis. This section outlines the practical applications and implementations of data representation across different domains.

Databases

Data representation is crucial in database management systems (DBMS). The choice of data structure has significant implications for data retrieval, integrity, and efficiency. Common database formats include:

Relational Databases: Use structured query language (SQL) to manage data organized in tables. Each table consists of rows and columns, making it easy to access and manipulate data.
NoSQL Databases: Rely on data representations such as documents, key-value pairs, or wide-column stores. These databases provide flexibility for storing and processing semi-structured and unstructured data.
Graph Databases: Represent data in graph structures, emphasizing relationships between data points. This representation is particularly useful for social networks and recommendation systems.

Programming Languages

Data representation plays a significant role in programming languages, influencing how data is created, manipulated, and utilized within an application. Different programming paradigms adopt various data structures that align with their methodologies. For instance, functional programming languages often utilize immutable data structures, whereas object-oriented languages leverage objects and classes for data representation.

Data Serialization and Deserialization

Serialization refers to the process of converting data structures or object states into a format that can be stored or transmitted (e.g., JSON, XML, or binary format). Deserialization is the reverse process of reconstructing the original data structures from the serialized format. Both processes are critical for data exchange between different computing environments and are extensively used in web services and APIs.

Real-world Examples or Comparisons

Practical applications of data representation are ubiquitous in modern computing environments. This section examines prominent examples and compares different data representation techniques.

File Formats

File formats play a crucial role in data representation across various applications:

Image Formats: JPEG, PNG, and GIF represent visual data, each offering different compression techniques, quality, and transparency options.
Audio Formats: MP3, WAV, and AAC are commonly used audio file formats, with varying degrees of data compression and fidelity.
Video Formats: Formats such as MP4 and AVI represent motion pictures and are essential for streaming services and multimedia applications, balancing quality and file size.

Data Representation in Machine Learning

In the field of machine learning, the representation of data significantly influences model performance and accuracy. Different techniques, such as feature engineering and dimensionality reduction, aim to encode data in a manner that optimizes learning algorithms. For instance, image data can be represented as pixel values, while textual data can be encoded using techniques such as bag-of-words or word embeddings.

Comparison of Data Structures

Different data structures possess unique advantages and limitations, making them suitable for various applications:

Arrays vs. Linked Lists: Arrays offer efficient indexing and access speed, whereas linked lists facilitate dynamic resizing and reduced memory overhead.
Trees vs. Graphs: Trees provide a hierarchical representation, ideal for representing hierarchical data and quick searches, while graphs excel in representing complex relationships among datasets.

Criticism or Controversies

Despite its foundational significance, data representation is not without criticism and challenges. This section highlights some of the prominent controversies and concerns surrounding data representation.

Data Loss and Integrity Issues

One of the critical challenges of data representation is potential data loss during conversions or when utilizing lossy compression techniques. Loss of fidelity can occur when converting between formats, affecting the reliability of data analysis and decision-making.

Bias and Representation

Another concern revolves around the representation of data in machine learning models. Biased data can lead to discriminatory outcomes, influencing decisions across various sectors, including finance, healthcare, and criminal justice. It is crucial to ensure that data representation techniques include diverse data sources to mitigate this issue.

Standardization Challenges

The proliferation of various encoding schemes and data formats has led to standardization challenges in data representation. Inconsistent representations can create interoperability issues between systems, hindering data exchange and collaboration.

Influence or Impact

Data representation has had a profound impact on technology and society, shaping how information is processed, analyzed, and communicated. This section explores the influence of data representation on various domains.

Technology and Communication

The development of efficient data representation methods has revolutionized technology and communication. Innovations such as cloud computing and networked systems rely heavily on standardized data representations for seamless data interchange and collaboration across global platforms.

Data Science and Big Data

The rise of data science and big data analytics has underscored the importance of effective data representation. Data scientists leverage various data representation techniques to extract insights and patterns from vast datasets, driving decision-making processes in businesses and research.

Artificial Intelligence

Data representation is a critical factor in the success of artificial intelligence (AI) and machine learning models. The choice of representation can significantly influence model accuracy, efficiency, and performance in tasks ranging from natural language processing to computer vision.

References

@@ Line 1: / Line 1: @@
-= Data Representation =
+== Data Representation ==
+Data representation is a core concept in computer science and information technology that refers to the methods and techniques used to encode and structure information for processing and storage. It encompasses various formats through which data can be organized, interpreted, and manipulated by computational systems. Proper data representation is critical for effective data management, optimization of algorithms, and the facilitation of data communication across various platforms.
 == Introduction ==
-Data representation is a fundamental concept in the fields of computer science and information systems. It refers to the methods and techniques employed to encode, manipulate, and interpret data within a computing environment. The primary objective of data representation is to enable efficient data storage, retrieval, and processing, which are crucial for the development of software applications, databases, and the broader field of technology. Understanding data representation is essential for both the design of computer systems and the practical application of data sciences.
-Data can take various forms, including binary, textual, numerical, graphical, and more. It is critical to choose the appropriate representation for any given task, as it affects performance, usability, and the overall integrity of information.
+Data representation serves as a foundation for various disciplines within computer science, including data science, databases, computer programming, and software engineering. It plays a pivotal role in converting real-world information into formats that computers can efficiently utilize. This section elucidates the fundamental principles behind data representation, the essential types of data, and the processes involved in encoding and decoding information.
-== History ==
+The primary goals of data representation include preserving the integrity of the data, optimizing retrieval and processing speeds, and facilitating effective communication between different systems. Common forms of data representation include numerical, textual, visual, and multimedia formats, each tailored to specific applications and environments.
-=== Early Developments ===
+== History or Background ==
-The origins of data representation can be traced back to early computing languages and binary systems, where data was primarily represented in a binary format—using combinations of 0s and 1s. This representation aligns with the foundational principles of digital computing, as electronic circuits can easily distinguish between two states: on (1) and off (0).
-In the mid-20th century, foundational programming languages emerged, such as Fortran and COBOL, which introduced more complex data structures like arrays and records. These languages allowed data representation of more than just numerical values, paving the way for structured programming and data management.
+The evolution of data representation dates back to the early days of computing, where the binary numeral system became foundational for digital data encoding. In the 1940s, the introduction of electronic computers marked the beginning of modern data representation methods, with early systems using punch cards and magnetic tapes to represent and store information.
-=== Advancements in Data Formats ===
+As computing technology progressed, various encoding schemes were developed to enhance data representation. For instance, the ASCII (American Standard Code for Information Interchange) character encoding system emerged in the 1960s, providing a standardized way to represent text in computers. In the 1980s, the Unicode standard was introduced to accommodate a broader range of characters from diverse languages, significantly expanding the capability for textual data representation.
-As computer technology advanced, specialized data formats were developed to enhance the representation of information. For example, the development of markup languages such as HTML in the late 20th century provided a way to represent structured documents on the World Wide Web, promoting interactivity and multimedia content.
-The introduction of databases also revolutionized data representation, leading to the creation of various data models like relational, hierarchical, and object-oriented models. These models enabled more efficient organization and manipulation of data within systems, accommodating a diversity of data types and queries.
+The rise of multimedia applications in the 1990s and 2000s led to the development of new formats for representing audio, video, and images, including JPEG, MP3, and MPEG. These advances underscored the importance of efficient data representation methods in the digital age, catering to an ever-increasing demand for high-quality multimedia content.
-== Types of Data Representation ==
+== Design or Architecture ==
-=== Binary Representation ===
+Data representation involves numerous architectural considerations, including the choice of encoding schemes, structures, and the underlying technologies that support data processing and storage. This section delves into the design aspects of data representation by exploring different data types and their respective structures.
-Binary is the most fundamental form of data representation in computing. In this system, all data is represented using only two digits: 0 and 1. Each binary digit (or bit) is a basic unit of information. Multiple bits can be combined to represent larger data types. For example, a byte consists of 8 bits, allowing for 256 unique values (2^8).
-In binary, integers, floating-point numbers, characters, and various data structures are represented using specific encoding schemes. Common encoding systems include:
+=== Types of Data ===
-* ASCII (American Standard Code for Information Interchange): Encodes 128 characters using 7 bits.
-* UTF-8: A variable-width character encoding that can encode every character in the Unicode character set.
-* IEEE 754: A standard for representing floating-point numbers in binary.
-=== Text Representation ===
+Data can be categorized into several types, each with distinct representation needs:
-Textual data representation involves encoding character strings and symbols, which are often used in conjunction with markup languages. Text representation is vital for producing meaningful output in applications such as document creation, web pages, and user interfaces.
+* '''Primitive Data Types''': Basic data types such as integers, floats, characters, and booleans. Primitive types require simple representations and are often utilized in programming languages for variable storage.
+* '''Complex Data Types''': Higher-level data types composed of one or more primitive types, including arrays, lists, tuples, and objects, which enable more sophisticated data manipulation.
+* '''Structured Data''': Data organized in a fixed format, such as databases, which facilitate structured querying and reporting. Relational databases make use of tables with predefined schemas to represent data entries.
+* '''Unstructured Data''': Data that lacks a predefined structure, such as text documents, images, and multimedia files, often requiring advanced techniques for parsing and analysis.
+* '''Semi-structured Data''': Data that has some organizational properties but does not conform to a rigid structure, such as JSON or XML. This type of data representation maintains flexibility and allows for diverse content variations.
-In addition to ASCII and UTF-8, other encoding systems like UTF-16 and ISO/IEC 8859 are used, particularly in regions with different alphabets and characters. Complex textual representations must also consider language nuances, such as diacritics or language-specific glyphs.
+=== Encoding Schemes ===
-=== Numerical Representation ===
+Several encoding schemes are utilized to represent data accurately:
-Numerical data can be represented in different ways based on the range and precision required, particularly:
+* '''Binary Encoding''': The most fundamental form of data representation, utilizing two symbols, 0 and 1, to represent all types of data within a computer system.
-* **Integer Representation**: Can be represented in various formats, including signed and unsigned integers. The two's complement method is often used for signed integers, which involves inverting the bits and adding one to represent negative values.
+* '''Character Encoding''': Various character encoding systems, such as ASCII and Unicode, allow for the representation of text, providing mechanisms to encode characters into binary values.
-* **Floating-Point Representation**: Enables the representation of real numbers, including fractions. The IEEE 754 standard distinguishes between single-precision (32 bits) and double-precision (64 bits) representations, accommodating a wide range of values.
+* '''Numerical Encoding''': Methods for encoding numbers, including fixed-point and floating-point representations, which determine how real numbers are stored in binary format.
 == Usage and Implementation ==
-=== Data Structures ===
+Effective data representation is essential for the performance of computing systems and directly impacts software development, database management, and data analysis. This section outlines the practical applications and implementations of data representation across different domains.
-Data representation is inherently tied to data structures—the methods used to organize and store data effectively. Common data structures include:
-* **Arrays**: Fixed-size collections of elements, often of the same type, providing indexed access.
-* **Linked Lists**: Composed of nodes where each node points to the next, enabling dynamic memory allocation.
-* **Trees**: Hierarchical structures with nodes connected in parent-child relationships, widely used in application domains like databases and XML parsing.
-* **Graphs**: Consist of vertices and edges, representing a set of connections, useful in networking and pathfinding applications.
-Each of these structures influences how data is represented, stored in memory, and accessed, directly impacting performance and computational efficiency.
+=== Databases ===
-=== File Formats ===
+Data representation is crucial in database management systems (DBMS). The choice of data structure has significant implications for data retrieval, integrity, and efficiency. Common database formats include:
-Data representation extends to file formats that dictate how information is structured and stored in files. Various formats serve different purposes, including:
+* '''Relational Databases''': Use structured query language (SQL) to manage data organized in tables. Each table consists of rows and columns, making it easy to access and manipulate data.
-* **Text Files**: Store unformatted text, readable by humans. Formats include .txt and .csv.
+* '''NoSQL Databases''': Rely on data representations such as documents, key-value pairs, or wide-column stores. These databases provide flexibility for storing and processing semi-structured and unstructured data.
-* **Binary Files**: Use binary encoding, suitable for images, audio, and other multimedia content; formats such as .jpg, .png, and .mp3.
+* '''Graph Databases''': Represent data in graph structures, emphasizing relationships between data points. This representation is particularly useful for social networks and recommendation systems.
-* **Markup Files**: Structured representations such as XML and JSON, commonly used for data interchange between systems.
+=== Programming Languages ===
+Data representation plays a significant role in programming languages, influencing how data is created, manipulated, and utilized within an application. Different programming paradigms adopt various data structures that align with their methodologies. For instance, functional programming languages often utilize immutable data structures, whereas object-oriented languages leverage objects and classes for data representation.
+=== Data Serialization and Deserialization ===
+Serialization refers to the process of converting data structures or object states into a format that can be stored or transmitted (e.g., JSON, XML, or binary format). Deserialization is the reverse process of reconstructing the original data structures from the serialized format. Both processes are critical for data exchange between different computing environments and are extensively used in web services and APIs.
-Understanding the implications of different file formats is crucial when considering data integrity, conversion, and compatibility across platforms and applications.
+== Real-world Examples or Comparisons ==
-=== Data Serialization ===
+Practical applications of data representation are ubiquitous in modern computing environments. This section examines prominent examples and compares different data representation techniques.
-Data serialization is the process of converting data structures or object state into a format that can be easily stored or transmitted and then reconstructed later. This is particularly relevant in network communication and persistent storage.
-Common serialization formats include:
+=== File Formats ===
-* **JSON (JavaScript Object Notation)**: A lightweight and human-readable representation of structured data, widely used for web APIs.
-* **XML (Extensible Markup Language)**: A flexible format that uses tags to encode documents, allowing for complex data structures and hierarchical relationships.
-* **Protocol Buffers**: Developed by Google, it is a method for serializing structured data, offering efficient storage and transmission, particularly for large-scale applications.
-== Real-world Examples ==
+File formats play a crucial role in data representation across various applications:
+* '''Image Formats''': JPEG, PNG, and GIF represent visual data, each offering different compression techniques, quality, and transparency options.
+* '''Audio Formats''': MP3, WAV, and AAC are commonly used audio file formats, with varying degrees of data compression and fidelity.
+* '''Video Formats''': Formats such as MP4 and AVI represent motion pictures and are essential for streaming services and multimedia applications, balancing quality and file size.
-=== Data Representation in Programming ===
+=== Data Representation in Machine Learning ===
-Data representation is crucial in programming languages, influencing how developers interact with data. For example, Python's data types (e.g., lists, tuples, dictionaries) provide developers with high-level abstractions to work with data, while C allows for fine-tuning through direct memory manipulation with structs and unions. This diversity reflects different paradigms, such as object-oriented, functional, and procedural programming, underscoring how data representation shapes coding practices.
-=== Database Management Systems (DBMS) ===
+In the field of machine learning, the representation of data significantly influences model performance and accuracy. Different techniques, such as feature engineering and dimensionality reduction, aim to encode data in a manner that optimizes learning algorithms. For instance, image data can be represented as pixel values, while textual data can be encoded using techniques such as bag-of-words or word embeddings.
-In databases, data representation is critical for optimizing data access and manipulation through structured query language (SQL) in relational databases like MySQL and PostgreSQL. Different types of databases, such as NoSQL databases like MongoDB and Cassandra, choose alternative representation strategies based on specific application requirements, focusing on document-oriented and wide-column stores.
-For instance, NoSQL databases often represent data as key-value pairs or as documents, which enables scalability and flexibility for large datasets and unstructured data.
+=== Comparison of Data Structures ===
-=== User Interfaces and Data Visualization ===
+Different data structures possess unique advantages and limitations, making them suitable for various applications:
-Data representation is vital in user interfaces and data visualization, where the visual encoding of information governs how effectively users comprehend and interact with data. Graphs, charts, and tables serve as primary tools for representing statistical information.
+* '''Arrays vs. Linked Lists''': Arrays offer efficient indexing and access speed, whereas linked lists facilitate dynamic resizing and reduced memory overhead.
+* '''Trees vs. Graphs''': Trees provide a hierarchical representation, ideal for representing hierarchical data and quick searches, while graphs excel in representing complex relationships among datasets.
-For example, bar charts represent categorical data, while line graphs showcase trends over time. Tools like Tableau and Power BI are employed widely in businesses to convert raw data into actionable insights through visual representation.
+== Criticism or Controversies ==
-== Criticism and Controversies ==
+Despite its foundational significance, data representation is not without criticism and challenges. This section highlights some of the prominent controversies and concerns surrounding data representation.
-=== Privacy and Security Concerns ===
+=== Data Loss and Integrity Issues ===
-The representation of data often raises critical issues regarding privacy and security, particularly with the increasing amounts of personal information being collected and stored. Poor data representation can lead to vulnerabilities, making systems exposed to data breaches, unauthorized access, and misuse of sensitive information.
-For instance, inadequate data anonymization practices can allow malicious actors to re-identify individuals from supposedly anonymized datasets, undermining privacy efforts. Legal frameworks, such as the General Data Protection Regulation (GDPR) in Europe, emphasize the importance of secure data representation and management to safeguard individuals' privacy rights.
+One of the critical challenges of data representation is potential data loss during conversions or when utilizing lossy compression techniques. Loss of fidelity can occur when converting between formats, affecting the reliability of data analysis and decision-making.
-=== Misrepresentation and Bias ===
+=== Bias and Representation ===
-Data can be misrepresented or manipulated, leading to biased interpretations that can influence decision-making in businesses, politics, and society. For example, selective data representation, such as cherry-picking data points to support a specific argument, can distort reality.
-Critics have underscored the ethical implications of data visualization, especially when dealing with complex datasets. Well-designed visualizations should strive for impartiality and accuracy to ensure they do not mislead stakeholders or the public.
+Another concern revolves around the representation of data in machine learning models. Biased data can lead to discriminatory outcomes, influencing decisions across various sectors, including finance, healthcare, and criminal justice. It is crucial to ensure that data representation techniques include diverse data sources to mitigate this issue.
-=== Obsolescence and Compatibility Issues ===
+=== Standardization Challenges ===
-As technology evolves, older data representation methods may become obsolete or incompatible with newer systems, leading to challenges in data migration and integration. For instance, legacy systems that utilize outdated binary formats or proprietary data structures can impede modernization efforts, ultimately resulting in increased costs and reduced efficiency.
-The shift from older file formats like .doc to more versatile formats like .docx or .odt illustrates the necessity of adapting data representation strategies to keep pace with technological advancements.
+The proliferation of various encoding schemes and data formats has led to standardization challenges in data representation. Inconsistent representations can create interoperability issues between systems, hindering data exchange and collaboration.
-== Influence and Impact ==
+== Influence or Impact ==
-Data representation has profound implications across various domains, from technology and business to scientific research and social initiatives. Its influence can be observed in several key areas:
+Data representation has had a profound impact on technology and society, shaping how information is processed, analyzed, and communicated. This section explores the influence of data representation on various domains.
-=== Advancements in Artificial Intelligence ===
+=== Technology and Communication ===
-In artificial intelligence (AI) and machine learning, effective data representation is pivotal for training models and improving outcomes. High-dimensional data representations, like embeddings used in natural language processing, enable systems to comprehend context and meaning, facilitating tasks such as sentiment analysis and chatbots.
-The quality of the chosen data representation directly affects model performance, influencing the results of AI applications across industries, from healthcare diagnostics to autonomous vehicles.
+The development of efficient data representation methods has revolutionized technology and communication. Innovations such as cloud computing and networked systems rely heavily on standardized data representations for seamless data interchange and collaboration across global platforms.
-=== Innovation in Data Analytics ===
+=== Data Science and Big Data ===
-Data analytics relies heavily on effective data representation to uncover insights, trends, and patterns. Organizations utilize advanced data representation techniques, such as dimensionality reduction methods (like PCA) and clustering algorithms, to visualize and interpret large datasets efficiently.
-The increasing availability of big data emphasizes the importance of innovative representation strategies to extract valuable knowledge while minimizing noise and redundancy.
+The rise of data science and big data analytics has underscored the importance of effective data representation. Data scientists leverage various data representation techniques to extract insights and patterns from vast datasets, driving decision-making processes in businesses and research.
-=== Role in Education and Research ===
+=== Artificial Intelligence ===
-In academia, data representation is integral to research methodologies, where the accuracy and effectiveness of data presentation can enhance scientific communication. Research papers, databases, and educational materials heavily rely on standardized data representations to share findings, promote reproducibility, and foster collaboration among different disciplines.
-The continuous evolution of data representation techniques contributes to the advancement of knowledge across fields, from computational biology to social sciences.
+Data representation is a critical factor in the success of artificial intelligence (AI) and machine learning models. The choice of representation can significantly influence model accuracy, efficiency, and performance in tasks ranging from natural language processing to computer vision.
-== See Also ==
+== See also ==
-* [[Data Structure]]
+* [[Information Theory]]
-* [[File Format]]
+* [[Data Structures]]
-* [[Serialization]]
+* [[Data Compression]]
-* [[Data Visualization]]
+* [[Database Management Systems]]
+* [[Machine Learning]]
 * [[Big Data]]
-* [[Artificial Intelligence]]
-* [[Database Management Systems]]
 == References ==
-* [https://www.w3.org/standards/ - World Wide Web Consortium (W3C)]
+* [https://www.owasp.org/index.php Data Representation and Cryptographic Storage]
-* [https://www.ietf.org/rfc/rfc3629.txt - UTF-8 Encoding Specification]
+* [https://www.unicode.org Unicode Consortium]
-* [https://en.wikipedia.org/wiki/IEEE_754 - IEEE 754 Standard for Floating-Point Arithmetic]
+* [https://www.w3.org/standards/xml XML Standards and the World Wide Web Consortium]
-* [https://www.dataversity.net/ - Dataversity - Data Strategy and Education]
+* [https://www.sqlite.org/index.html SQLite Database Management System]
-* [https://gdpr.eu/ - General Data Protection Regulation (GDPR) Resources]
+* [https://json.org JSON Data Interchange Format]
-* [https://www.tableau.com/ - Tableau Software for Data Visualization]
+* [https://www.iso.org/iso/home.html International Organization for Standardization (ISO)]
-* [https://powerbi.microsoft.com/ - Microsoft Power BI]
-* [https://www.tensorflow.org/ - TensorFlow - Machine Learning Framework]
-* [https://www.kdnuggets.com/ - KDnuggets - Data Science and Machine Learning Resources]
-[[Category:Data structures]]
+[[Category:Data]]
 [[Category:Computer science]]
 [[Category:Information theory]]