Viser: Practical Statistics for Data Scientists - 50+ Essential Concepts Using R and Python
Practical Statistics for Data Scientists Vital Source e-bog
Peter Bruce, Andrew Bruce og Peter Gedeck
(2020)
Practical Statistics for Data Scientists
50+ Essential Concepts Using R and Python
Peter Bruce, Andrew Bruce og Peter Gedeck
(2020)
Sprog: Engelsk
om ca. 15 hverdage
Detaljer om varen
- 2. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: O'Reilly Media, Inc (April 2020)
- Forfattere: Peter Bruce, Andrew Bruce og Peter Gedeck
- ISBN: 9781492072898
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
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Print: 2 sider kan printes ad gangen
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Detaljer om varen
- 2. Udgave
- Paperback: 350 sider
- Udgiver: O'Reilly Media, Incorporated (Juni 2020)
- Forfattere: Peter Bruce, Andrew Bruce og Peter Gedeck
- ISBN: 9781492072942
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
With this book, you'll learn:
- Why exploratory data analysis is a key preliminary step in data science
- How random sampling can reduce bias and yield a higher-quality dataset, even with big data
- How the principles of experimental design yield definitive answers to questions
- How to use regression to estimate outcomes and detect anomalies
- Key classification techniques for predicting which categories a record belongs to
- Statistical machine learning methods that "learn" from data
- Unsupervised learning methods for extracting meaning from unlabeled data