Practical statistics for data scientists : 50+ essential concepts using R and Python / Peter Bruce, Andrew Bruce, and Peter Gedeck.
Material type:
TextCopyright date: ©2020Edition: Second editionDescription: xvi, 342 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781492072942; 149207294XSubject(s): Mathematical analysis -- Statistical methods | Quantitative research -- Statistical methods | R (Computer program language) | Python (Computer program language) | Statistics -- Data processing | Python (Computer program language) | R (Computer program language) | Statistics -- Data processing | E-BOOKBANK.SEECSTEXTBOOKDDC classification: 001.422 LOC classification: QA276.4 | .B78 2020Online resources: Click here to access online | Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Book
|
Central Library (CL) | Central Library (CL) | 280 SCB (Browse shelf) | Available | SCB-1563 | ||
|
Book
|
Central Library (CL) | Central Library (CL) | 001.422 BRU (Browse shelf) | Checked out | 02/21/2025 | CL-1563 |
Includes bibliographical references (pages 327-328) and index.
Exploratory Data Analysis -- Data and Sampling Distributions -- Statistical Experiments and Significance Testing -- Regression and Prediction -- Classification -- Statistical Machine Learning -- Unsupervised Learning.
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 practical guide-now including examples in Python as well as R-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 scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.-- Source other than the Library of Congress.
Click on an image to view it in the image viewer
There are no comments on this title.