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Data science from scratch : first principles with Python /
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you'll learn how many of the most fundamental data science tool...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
[2019]
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Grus, Joel |c (Software engineer), |e author. | |
| 245 | 1 | 0 | |a Data science from scratch : |b first principles with Python / |c Joel Grus. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Sebastopol, CA : |b O'Reilly Media, |c [2019] | |
| 300 | |a 1 online resource (xvii, 376 pages) : |b illustrations (some color) | ||
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| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, April 16, 2019). | |
| 588 | 0 | |a Online resource; title from digital title page (viewed on March 9, 2021). | |
| 505 | 0 | |a Introduction -- A crash course in Python -- Visualizing data -- Linear algebra -- Statistics -- Probability -- Hypothesis and inference -- Gradient descent -- Getting data -- Working with data -- Machine learning -- k-Nearest neighbors -- Naive bayes -- Simple linear regression -- Multiple regression -- Logistic regression -- Decision trees -- Neural networks -- Deep learning -- Clustering -- Natural language processing -- Network analysis -- Recommender systems -- Databases and SQL -- MapReduce -- Data ethics -- Go forth and do data science. | |
| 520 | 8 | |a Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Python (Computer program language) | |
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| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Python (Langage de programmation) | |
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| 650 | 6 | |a Structures de données (Informatique) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
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| 650 | 7 | |a Database management. |2 fast |0 (OCoLC)fst00888037 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
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