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Practical Statistics for Data Scientists: 50+ Essential Concepts Using R & Python

By: By: Publication details: Mumbai : Shroff Publishers & Distributors, 2020Edition: 2Description: 342ISBN:
  • 9788194435006
Subject(s): DDC classification:
  • 001.422 BRU
Summary: 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.
List(s) this item appears in: New Arrivals for the Month of August - 2023 | New Arrivals for the Month of September - 2023
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
Book Book ACED Computer Science and Information Technology 001.422 BRU (Browse shelf(Opens below)) Available E12394
Book Book ACED Computer Science and Information Technology 001.422 BRU (Browse shelf(Opens below)) Available E11993
Total holds: 0

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.

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