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]]

"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-e...

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Detalles Bibliográficos
Otros Autores: Bruce, Peter C., 1953- author (author), Bruce, Andrew, 1958- author (translator), Gedeck, Peter, author (-), Kurokawa, Toshiaki, translator, Ōhashi, Shin'ya
Formato: Libro electrónico
Idioma:Japonés
Publicado: Tōkyō-to Shinjuku-ku : Orairī Japan 2020.
Edición:Dai 2-han
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009740931106719
Descripción
Sumario:"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." --
Descripción Física:1 online resource (396 pages)