May 1, 2024
Updated May 10, 2025
20 minute read
Data representation, at its core, refers to the methods and formats used to symbolize information within a computer system and in various forms of digital and analog media. It is the foundational concept that allows us to convert real-world information—numbers, text, images, sounds, and more complex structures—into a form that machines can process, store, and transmit. Understanding data representation is crucial not just for computer scientists and software engineers, but for anyone interacting with technology or data in a meaningful way, as it dictates how information is structured, interpreted, and ultimately, utilized.
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Find a path to becoming a Data Representation. Learn more at:
OpenCourser.com/topic/r6xdbv/data
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Representation.
Provides an overview of data representation in the humanities, including discussions of text, images, and sound. The book is well-suited for students and practitioners interested in learning how to represent and process data in the humanities.
Provides a tutorial on data representation in the physical sciences, including discussions of units, dimensions, and uncertainties. The book is well-suited for students and practitioners interested in learning how to represent and process data in the physical sciences.
Provides a practical guide to data representation in databases, including discussions of data models, data types, and data structures. The book is well-suited for students and practitioners interested in learning how to design and implement databases.
Provides a tutorial on data representation in finance, including discussions of financial data, financial models, and financial databases. The book is well-suited for students and practitioners interested in learning how to represent and process data in finance.
Provides a guide to data-driven marketing, including discussions of customer data, marketing data, and marketing analytics. The book is well-suited for students and practitioners interested in learning how to represent and process data in marketing.
Provides a handbook on data representation in music, including discussions of musical notation, music theory, and music information retrieval. The book is well-suited for students and practitioners interested in learning how to represent and process data in music.
Provides a practical guide to data representation in medicine, including discussions of medical terminologies, medical ontologies, and medical data standards. The book is well-suited for students and practitioners interested in learning how to represent and process data in medicine.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/r6xdbv/data
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