
Applied ontology
Katherine Munn
2008 · 2013 edition
342 pages
Data science books show you how to learn from data. Statistics, databases, visualization and machine learning are taught through practical examples, usually with code. Beginners will find gentle introductions and working analysts will find deeper references. Good reading for a career change, too. This list shows the ones you can read in full for free.

Katherine Munn
2008 · 2013 edition
342 pages

Zhiyuan Liu, Yankai Lin, Maosong Sun
Published 2020
334 pages

Doug Dechow
Published 2015
150 pages

Annika Richterich
Published 2018
148 pages

MIT Critical Data, MIT Critical
Published 2016
427 pages

Karol Jan Borowiecki
Published 2016
322 pages

Scott Krig
Published 2014
498 pages

T. Rob Wyatt, Glenn Baddeley, Neil Casey, Long Nguyen, Jørgen H. Pedersen, Morten Sætra
Published 2012
364 pages

Huadong Guo
Published 2019
852 pages

Steve Scargall
Published 2020
438 pages

Hirokazu Handa, Yasunari Ogihara, Shigeo Takumi
Published 2016
257 pages

E. Mathieu
2017 · 2018 edition
332 pages

Emanuel Deutschmann
Published 2019
264 pages

Martin Bentancour
Published 2011
1014 pages

Matthew J. Gray
Published 2015
246 pages

Wei-Dong Jackie Zhu
Published 2014
598 pages

Sören Auer
Published 2014
215 pages

Wieslaw Ostachowicz, Malcolm McGugan, Jens-Uwe Schröder-Hinrichs
Published 2018
310 pages

Margaret Ticknor
Published 2011
488 pages

Krisztian Balog
Published 2018
351 pages

Anastasius Gavras
Published 2013
369 pages

Mats Fridlund, Mila Oiva, Petri Paju
Published 2020
360 pages

IBM Redbooks
Published 2015
424 pages

IBM Redbooks
Published 2014
378 pages