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  • 1
    ISBN: 9788328374287 , 8328374285
    Language: Polish
    Pages: 1 online resource (296 pages) , illustrations
    Edition: Wydanie II.
    Uniform Title: Practical statistics for data scientists
    DDC: 001.4/226
    Keywords: Mathematical analysis Statistical methods ; Quantitative research Statistical methods ; Big data Mathematics
    Abstract: Metody statystyczne są kluczowym narzędziem w data science, mimo to niewielu analityków danych zdobyło wykształcenie w ich zakresie. Może im to utrudniać uzyskiwanie dobrych efektów. Zrozumienie praktycznych zasad statystyki okazuje się ważne również dla programistów R i Pythona, którzy tworzą rozwiązania dla data science. Kursy podstaw statystyki rzadko jednak uwzględniają tę perspektywę, a większość podręczników do statystyki w ogóle nie zajmuje się narzę̜dziami wywodzącymi się̜ z informatyki. To drugie wydanie popularnego podrę̜cznika statystyki przeznaczonego dla analityków danych. Uzupełniono je o obszerne przykłady w Pythonie oraz wyjaśnienie, jak stosować poszczególne metody statystyczne w problemach data science, a także jak ich nie używać. Skoncentrowano się też na tych zagadnieniach statystyki, które odgrywają istotną rolę w data science. Wyjaśniono, które koncepcje są ważne i przydatne z tej perspektywy, a które mniej istotne i dlaczego. Co ważne, poszczególne koncepcje i zagadnienia praktyczne przedstawiono w sposób przyswajalny i zrozumiały również dla osób nienawykłych do posługiwania się statystyką na co dzień.
    Note: Includes bibliographical references
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  • 2
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : dpunkt | Boston, MA : Safari
    Language: English , German
    Pages: 1 online resource (374 pages)
    Edition: 2nd edition
    Keywords: Electronic books ; local
    Abstract: Statistische Methoden sind ein zentraler Bestandteil der Arbeit mit Daten, doch nur wenige Data Scientists haben eine formale statistische Ausbildung. In Kursen und Büchern über die Grundlagen der Statistik wird das Thema aber selten aus der Sicht von Data Scientists behandelt. Viele stellen daher fest, dass ihnen eine tiefere statistische Perspektive auf ihre Daten fehlt. Dieses praxisorientierte Handbuch mit Beispielen in Python und R erklärt Ihnen, wie Sie verschiedene statistische Methoden speziell in den Datenwissenschaften anwenden. Es zeigt Ihnen auch, wie Sie den falschen Gebrauch von statistischen Methoden vermeiden können, und gibt Ratschläge, welche statistischen Konzepte für die Datenwissenschaften besonders relevant sind. Wenn Sie mit R oder Python vertraut sind, ermöglicht diese zugängliche, gut lesbare Referenz es Ihnen, Ihr statistisches Wissen für die Praxis deutlich auszubauen.
    Note: Online resource; Title from title page (viewed March 1, 2021) , Mode of access: World Wide Web.
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  • 3
    Title: 数据科学中的实用统计学 = : Practical statistics for data scientists /
    Publisher: O'Reilly Media ;
    ISBN: 9787115569028 , 7115569029
    Language: Chinese
    Pages: 1 online resource (289 pages) , illustrations
    Edition: Second edition, simplied Chinese edition.
    Uniform Title: Practical statistics for data scientists
    DDC: 001.4/22
    Keywords: Mathematical analysis Statistical methods ; Quantitative research Statistical methods ; R (Computer program language) ; Python (Computer program language) ; Statistics Data processing ; Analyse mathématique ; Méthodes statistiques ; Recherche quantitative ; Méthodes statistiques ; R (Langage de programmation) ; Python (Langage de programmation) ; Statistique ; Informatique ; Electronic books
    Abstract: Detailed summary in vernacular field.
    Note: Includes bibliographical references
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  • 4
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (93 pages)
    Edition: 2nd edition
    Keywords: Electronic books ; local
    Abstract: 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
    Note: Online resource; Title from title page (viewed May 25, 2020)
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  • 5
    Orig.schr. Ausgabe: 第 2版.
    Title: データサイエンスのための統計学入門 : : 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 /
    Publisher: オライリー・ジャパン,
    ISBN: 9784873119267 , 487311926X
    Language: Japanese
    Pages: 1 online resource (396 pages)
    Edition: Dai 2-han.
    Uniform Title: Practical statistics for data scientists
    DDC: 001.4/22
    Keywords: Mathematical analysis Statistical methods ; Quantitative research Statistical methods ; R (Computer program language) ; Python (Computer program language) ; Statistics Data processing ; Analyse mathématique ; Méthodes statistiques ; Recherche quantitative ; Méthodes statistiques ; R (Langage de programmation) ; Python (Langage de programmation) ; Statistique ; Informatique ; Python (Computer program language) ; R (Computer program language) ; Statistics ; Data processing ; Electronic books
    Abstract: "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." --
    Note: Includes bibiographical references (pages 345-349) and index. - Online resource; title from title details screen (O’Reilly, viewed April 20, 2022)
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