Authors:
- With minimal prerequisites (Intro CS or Foundations of Python Programming), students in computer science or data science/data analytics are introduced to data systems, and develop data-aptitude early in their undergraduate career, so that such understanding and skills can be utilized in downstream courses, projects, and external endeavors, such as REUs and internships
- Strikes a good balance between the conceptual underpinnings and the practical skills and application that help to keep students engaged. Use of real-world datasets and projects also make the approach appealing
- Progressive nature of chapters offers a a rich set of orderings for covering the material in the book for a given course.
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Table of contents (24 chapters)
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Front Matter
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Foundation
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Front Matter
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Data Systems: The Data Models
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Front Matter
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About this book
Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form.
The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the “data-aptitude” built by the material in this book.
Authors and Affiliations
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Mathematics and Computer Science, Denison University, Granville, USA
Thomas Bressoud, David White
About the authors
David White is Associate Professor in computer science, data analytics, and mathematics at Denison University. After his undergraduate degree at Bowdoin College, David carried out applied data analysiswork for the Department of Defense. He went on to earn his MS in computer science, and PhD in mathematics from Wesleyan University in 2014. His research has resulted in over fifteen publications in mathematics, applied statistics, computer science, economics, and data science. In addition to publications on data science pedagogy, and a chapter for the book Data Science for Mathematicians, he has applied data science techniques to carry out research related to the opioid epidemic, gun violence, and biomedical treatments.
Bibliographic Information
Book Title: Introduction to Data Systems
Book Subtitle: Building from Python
Authors: Thomas Bressoud, David White
DOI: https://doi.org/10.1007/978-3-030-54371-6
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-54370-9Published: 05 December 2020
Softcover ISBN: 978-3-030-54373-0Published: 05 December 2021
eBook ISBN: 978-3-030-54371-6Published: 04 December 2020
Edition Number: 1
Number of Pages: XXIX, 828
Number of Illustrations: 16 b/w illustrations, 65 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Data Structures and Information Theory, Artificial Intelligence, Information Systems and Communication Service, Big Data, Python