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(9781409599340) - Dimensions: 247 x 204 x 17 mm - Format: 96 pages - Height: 250 mm - Spine width: 18 mm - Width: 204 mm - Usborne Publishing Ltd. - London, United Kingdom - ISBN: 9781409599340 - Number of pages: 95 | Coding for Beginners
(9781409599340) - Dimensions: 247 x 204 x 17 mm - Format: 96 pages - Height: 250 mm - Spine width: 18 mm - Width: 204 mm - Usborne Publishing Ltd. - London, United Kingdom - ISBN: 9781409599340 - Number of pages: 95 | Coding for Beginners
(9780241286869) - Height: 204 mm - Spine width: 23 mm - Width: 242 mm - Dorling Kindersley Limited - Dorling Kindersley Ltd - ISBN: 9780241286869 - Number of pages: 224 - Languages: English - Weight: 706 g
(9781107623255) - Country of publication: UNITED KINGDOM - Dimensions: (H) 236mm, (W) 173mm, (D) 11mm - Cambridge University Press - In Print - Primary and secondary/elementary and high school - ISBN: 9781107623255
(9781593278571 / 48691816) - Height: 179 mm - Spine width: 27 mm - Width: 234 mm - No Starch Press - Penguin Random House Group - ISBN: 9781593278571 - Number of pages: 262 - Languages: English - Weight: 634 g
Cambridge University Press (9781107075412 / 93 b/w illus. 52 tables 150 exercises)
Cambridge University Press (9781107075412 / 93 b/w illus. 52 tables 150 exercises)
Cambridge University Press (9781107075412 / 93 b/w illus. 52 tables 150 exercises)
Cambridge University Press (9781107075412 / 93 b/w illus. 52 tables 150 exercises)
No Starch Press,US (9781593276409 / illustrations) - Height: 180mm - Spine width: 18mm - Width: 234mm - No Starch Press - xvii, 244 - Penguin Random House Group - ISBN: 9781593276409 - Languages: English | Doing math
No Starch Press,US (9781593276409 / illustrations) - Height: 180mm - Spine width: 18mm - Width: 234mm - No Starch Press - xvii, 244 - Penguin Random House Group - ISBN: 9781593276409 - Languages: English | Doing math
O′Reilly (9781492034865) - Graphical & digital media applications - First edition - O'Reilly - xxvi, 588 - O'Reilly Media - Height: 230 mm - Spine width: 32 mm - Width: 145 mm - ISBN: 9781492034865 | Practical Deep
Princeton University Press (9780691198309) | Revised throughout, Python code, Machine Learning in Astronomy, Survey Telescope, Astronomical surveys, Sets and code, Complex astronomical Data sets
Princeton University Press (9780691198309) | Revised throughout, Python code, Machine Learning in Astronomy, Survey Telescope, Astronomical surveys, Sets and code, Complex astronomical Data sets
Princeton University Press (9780691198309) | Revised throughout, Python code, Machine Learning in Astronomy, Survey Telescope, Astronomical surveys, Sets and code, Complex astronomical Data sets
Princeton University Press (9780691198309) | Revised throughout, Python code, Machine Learning in Astronomy, Survey Telescope, Astronomical surveys, Sets and code, Complex astronomical Data sets
Princeton University Press (9780691198309) | Revised throughout, Python code, Machine Learning in Astronomy, Survey Telescope, Astronomical surveys, Sets and code, Complex astronomical Data sets
Princeton University Press (9780691198309) | Revised throughout, Python code, Machine Learning in Astronomy, Survey Telescope, Astronomical surveys, Sets and code, Complex astronomical Data sets
Princeton University Press (9780691198309) | Revised throughout, Python code, Machine Learning in Astronomy, Survey Telescope, Astronomical surveys, Sets and code, Complex astronomical Data sets
McGraw-Hill Education TAB (9781259644535)
O'Reilly Media (9781491938454) | Think DSP, Signal Processing in Python
O'Reilly Media (9781491938454) | Think DSP, Signal Processing in Python
O'Reilly Media (9781491938454) | Think DSP, Signal Processing in Python
O'Reilly Media (9781491938454) | Think DSP, Signal Processing in Python
O'Reilly Media (9781491938454) | Think DSP, Signal Processing in Python
O'Reilly Media (9781491972731) | Programming with MicroPython
O'Reilly Media (9781491972731) | Programming with MicroPython
O'Reilly Media (9781491972731) | Programming with MicroPython
Learn to use Python for research in physics with this practical guide, covering best practices and tools for effective computation and data analysis in the field.
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