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Browsing Library of Congress Call Numbers : "QA76.9 .D3 D255" to "QA76.9 .D37 F68 2009" (Overview; Include extended shelves)

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:
Call number Item
Q Science (Go to start of category)
QA Mathematics and Computer Science (Go to start of category)
QA76.9 .D3 D255 [Info] Bulletin of the IEEE Computer Society Technical Committee on Data Engineering (full serial archives)
QA76.9.D3 F43 [Info] SIGMOD Record (partial serial archives)
QA76.9.D3 I599 2008 [Info] Introduction to Metadata (online edition 3.0, 2008), ed. by Murtha Baca, contrib. by Tony Gill, Anne Gilliland, Maureen Whalen, and Mary S. Woodley (HTML with commentary at getty.edu)
QA76.9.D3 J38 2014 [Info] Data Science at the Command Line (free online edition; originally published 2014), by Jeroen Janssens (illustrated HTML at datascienceatthecommandline.com)
QA76.9 .D3 L55 2010 [Info] Data-Intensive Text Processing With MapReduce (prepublication version, 2010), by Jimmy Lin and Chris Dyer (PDF at umd.edu)
QA76.9 .D3 M33 1983 [Info] The Theory of Relational Databases (Rockville, MD: Computer Science Press, c1983), by David Maier (PDF files at pdx.edu)
QA76.9 .D3 M647 2000 [Info] PostgreSQL: Introduction and Concepts, by Bruce Momjian (HTML at postgresql.org)
QA76.9 .D3 R87 2014 [Info] Cloudera Impala (Sebastopol, CA et al: O'Reilly, c2014), by John Russell (page images at cloudera.com)
QA76.9.D3 W67 [Info] Practical PostgreSQL (c2002), by John Worsley and Joshua Drake, ed. by Andrew Brookins and Michael Holloway (HTML at faqs.org)
QA76.9 .D3 2015 [Info] Readings in Database Systems (fifth edition, 2015), ed. by Peter Bailis, Joseph M. Hellerstein, and Michael Stonebraker (HTML and PDF at redbook.io)
QA76.9 .D33 L5 [Info] Word Hy-phen-a-tion by Com-put-er (Stanford PhD thesis, 1983), by Franklin Mark Liang (PDF files with commentary at tug.org)
QA76.9 .D343 [Info] Bisociative Knowledge Discovery: An Introduction to Concept, Algorithms, Tools, and Applications (c2012), ed. by Michael R. Berthold (PDF files with commentary at SpringerLink)
QA76.9 .D343 [Info] Ethics and Data Science (c2019), by Michael Kosta Loukides, Hilary Mason, and DJ Patil (HTML at oreilly.com)
QA76.9 .D343 .B54 2017 [Info] Machine Learning for Data Streams, With Practical Examples in MOA (Cambridge, MA and London: MIT Press, c2017), by Albert Bifet, Ricard Gavaldà, Geoffrey Holmes, and Bernhard Pfahringer (HTML with commentary in New Zealand)
QA76.9 .D343 D54 2019 [Info] digitalSTS: A Field Guide for Science and Technology Studies (Princeton: Princeton University Press, c2019), ed. by Janet Vertesi and David Ribes (PDF files and multimedia with commentary at digitalsts.net)
QA76.9 .D343 K5 2014 [Info] Terms of Service: Understanding Our Role in the World of Big Data (Al Jazeera America, c2014), by Michael Keller and Josh Neufeld (multiple formats at aljazeera.com)
QA76.9 .D343 L387 2023 [Info] Learning Data Science (c2023), by Sam Lau, Joseph Gonzalez, and Deborah Ann Nolan (illustrated HTML with commentary at learningds.org)
QA76.9 .D343 R35 2014 [Info] Mining of Massive Datasets (version 2.1, c2014), by Jurij Leskovec, Anand Rajaraman, and Jeffrey D. Ullman (PDF files with commentary at mmds.org)
QA76.9 .D343 S5935 2019 [Info] Text Mining with R: A Tidy Approach (Creative Commons online edition; ca. 2019), by Julia Silge and David Robinson (illustrated HTML with commentary at tidytextmining.com)
QA76.9 .D343 Z34 2014 [Info] Social Media Mining: An Introduction (prepublication draft, with supplementary materials; 2014), by Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu (PDF with commentary at asu.edu)
QA76.9 D35 [Info] Open Data Structures (multiple editions; c2013), by Pat Morin
QA76.9 .D35 D48 1986 [Info] Lecture Notes on Bucket Algorithms (1986), by Luc Devroye (PDF at devroye.org)
QA76.9 .D35 O35 1996 [Info] Purely Functional Data Structures (dissertation version, 1996), by Chris Okasaki (PDF at CMU)
QA76.9.D35 W58 2004 [Info] Algorithms and Data Structures (based on the 1985 edition; updated for Oberon in 2004), by Niklaus Wirth (PDF in Switzerland)
QA76.9 .D37 F68 2009 [Info] The Fourth Paradigm: Data-Intensive Scientific Discovery (Redmond, WA: Microsoft Research, c2009), ed. by Anthony J. G. Hey, Stewart Tansley, and Kristin Michele Tolle (PDF files with commentary at Microsoft)

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