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Data exploration in Python /
"If you're a fledgling data scientist with only cursory statistical training and little experience with real world data sets, you may feel like you're stumbling around in the dark when you're asked to interpret and present data to decision makers. How do you validate the data? Wh...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly Media,
2015.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Downey, Allen B., |e on-screen presenter. | |
| 245 | 1 | 0 | |a Data exploration in Python / |c Allen B. Downey. |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly Media, |c 2015. | |
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| 500 | |a Title from resource description page (viewed December 3, 2015). | ||
| 511 | 0 | |a On-screen presenter, Allen B. Downey. | |
| 520 | |a "If you're a fledgling data scientist with only cursory statistical training and little experience with real world data sets, you may feel like you're stumbling around in the dark when you're asked to interpret and present data to decision makers. How do you validate the data? What analytic model should you use? How do you differentiate between correlation and causation? How do you ensure that your data is solid and your conclusions are on target? Allen Downey, Professor of Computer Science at Olin College of Engineering, author of Think Stats, Think Python, and Think Complexity, provides safe passage around the common pitfalls of exploratory data analysis, so you can manage, analyze, and present data with confidence."--Resource description page. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
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