Practical ststistics for data scientist : 50 essentials concepts
By: Bruce, Peter
.
Contributor(s): Bruce, Andrew.
Publisher: Navi Mumbai 2017Edition: 1st.Description: xvi, 298p. | Binding - Paperback | 23.5*18 cm.ISBN: 9789352135653.Subject(s): EXTC EngineeringDDC classification: 001.422 Summary: Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various 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 programming language, 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
| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Text Books
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School of Engineering & Technology General Stacks | Circulation | 001.422 BRU/BRU (Browse shelf) | Checked out to FAHIM AHMED MUNAWWAR HUSAIN ANSARI (16ET09) | 20/03/2020 | E15036 | |
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Text Books
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School of Engineering & Technology Reference Section | Reference | 001.422 BRU/BRU (Browse shelf) | Checked out to Asif Indravadan Gandhi (MEF004) | 05/06/2024 | E14535 |
Browsing School of Engineering & Technology Shelves , Shelving location: Reference Section , Collection code: Reference Close shelf browser
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| 001.42 KOT/GAR Research methodology: Methods and techniques | 001.422 BRU Practical statistics for data scientists | 001.422 BRU/BRU Practical ststistics for data scientist | 003 BOR/NAA Operations Research | 003 DEN/WIX Systems analysis and design | 003 GUP/HIR Operations Research |
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various 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 programming language, 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
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