– An emphasis on software engineering and coding skills, which play a significant role in most real data science problems.
– Extensive sample code, detailed discussions of important libraries, and a solid grounding in core concepts from computer science (computer architecture, runtime complexity, and programming paradigms).
– A broad overview of important mathematical tools, including classical techniques in statistics, stochastic modeling, regression, numerical optimization, and more.
– Extensive tips about the practical realities of working as a data scientist, including understanding related jobs functions, project life cycles, and the varying roles of data science in an organization.
– Exactly the right amount of theory. A solid conceptual foundation is required for fitting the right model to a business problem, understanding a tool’s limitations, and reasoning about discoveries.
Data science is a quickly evolving field, and this 2nd edition has been updated to reflect the latest developments, including the revolution in AI that has come from Large Language Models and the growth of ML Engineering as its own discipline. Much of data science has become a skillset that anybody can have, making this book not only for aspiring data scientists, but also for professionals in other fields who want to use analytics as a force multiplier in their organization.
Field, Carnegie Mellon) Cady en Stanford University.
- Auteur(s) : Field, Carnegie Mellon) Cady, Stanford University
- Uitgeverij: John Wiley & Sons Inc
- ISBN : 9781394234493
- Taal : Engels
- Uitvoering : Hardcover
- Aantal pagina’s : 368
- Verschijningsdatum : oktober 2024
- Gewicht : 700
- Afmetingen : 215 x 140 x 27 mm.
- Thema : Data analyse: algemeen; Dataverzameling en analyse; Gegevensanalyse; Waarschijnlijkheid en statistieken




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