データサイエンスのための統計学入門 第2版 ―予測、分類、統計モデリング、統計的機械学習とR/Pythonプログラミング [[データ サイエンス ノ タメ ノ トウケイガク ニュウモン ―ヨソク ブンルイ トウケイ モデリング トウケイテキ キカイ ガクシュウ ト アール パイソン プログラミング]]

Format:

Book

Author/Creator:

Bruce, Peter C., 1953- author.

Bruce, Andrew, 1958- author.

Gedeck, Peter, author.

Contributor:

Kurokawa, Toshiaki, translator.

Ōhashi, Shin'ya.

Standardized Title:

Practical statistics for data scientists. Japanese

Language:

Japanese

Subjects (All):

Mathematical analysis--Statistical methods.

Mathematical analysis.

Quantitative research--Statistical methods.

Quantitative research.

R (Computer program language).

Python (Computer program language).

Statistics--Data processing.

Statistics.

Physical Description:

1 online resource (396 pages)

Edition:

Dai 2-han.

Place of Publication:

Tōkyō-to Shinjuku-ku : Orairī Japan, 2020.

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 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." -- Provided by publisher.

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1311518442