python
Data Archiving and Networked Services (DANS)
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About 10,000 papers for “Python”
Data Archiving and Networked Services (DANS)
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J.W van Henten · UvA-DARE (University of Amsterdam) · 1995
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Data Archiving and Networked Services (DANS)
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Oglesby, Larry C. · Scholarship @ Claremont (The Claremont Colleges) · 1990
Bunny Washington, Neepa Ved, and another Biology 51 student after surprise finding Bunny's snake.
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Larry Carbone · 2026
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Chirag Shah · Cambridge University Press eBooks · 2022
Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
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Python · Bulletin of Miscellaneous Information (Royal Gardens Kew)
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Liverpool University Press eBooks · 2013
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Tim Junk, Bernhard Zimmermann, Johannes Engels et al. · Der Neue Pauly · 2014
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Ray Toal, Rachel Rivera, Alexander Schneider et al. · 2016
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Guido van Rossum · 1998
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Milton Quarterly · 2008
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Porter, Matthew A. · Zenodo (CERN European Organization for Nuclear Research) · 2025
Equator Education with Python
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Kik, Annette · Data Archiving and Networked Services (DANS) · 2006
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Robin Engelhardt · OSF Preprints (OSF Preprints)
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Chirag Shah · Cambridge University Press eBooks · 2026
Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool Python, a new chapter on using Python for statistical analysis, and a new chapter that demonstrates how to use Python within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.
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Steven Loria · OSF Preprints (OSF Preprints) · 2015
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J.W van Henten · UvA-DARE (University of Amsterdam) · 1995
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