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  • 1
    Online Resource
    Online Resource
    Statystyka praktyczna w data science : 50 kluczowych zagadnień w językach R i Python (2021)
    Gliwice : Helion
    Details
    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
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Improve the outcome of your data experiments with A-B testing : practical statistics for data science (2016)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Mathematical statistics ; Sampling (Statistics) ; Electronic books ; Electronic books ; local
    Abstract: Data scientists are faced with the need to conduct continual experiments, particularly regarding user interface and product marketing. Designing experiments is a cornerstone of the practice of statistics, with clear application to data science. In this lesson, you'll learn about A-B testing and hypothesis , or significance tests -critical aspects of experimental design for data science. What you'll learn-and how you can apply it You will learn the central concepts of A-B testing, understand its role in designing and conducting data science experiments, and the characteristics of a proper A-B test. Through a series of sample tests, you'll learn how to interpret results, and apply that insight to your analysis of the data. Since A-B tests are typically constructed with a hypothesis in mind, you'll also learn how to conduct various hypothesis , or significance tests , enabling you to avoid misinterpreting randomness. This lesson is for you because You are a data scientist or analyst working with data, and want to gain beginner-level knowledge of key statistical concepts to improve the design, and outcome of your experimental tests with data. Prerequisites: Basic familiarity with coding in R Materials or downloads needed: n/a
    Note: "From Practical statistics for data scientists by Peter Bruce and Andrew Bruce"--Cover. - Date of publication from resource description page. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 16, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491978351/?ar
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Improve your data quality using sampling distribution : practical statistics for data science (2016)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Mathematical statistics ; Sampling (Statistics) ; Electronic books ; Electronic books ; local
    Abstract: Statistical methods are central to the techniques involved in capturing value from data. While there are many resources that teach basic statistics, it's not as common to find statistics approached through a data science perspective. In this lesson, you'll learn about key statistical concepts-sampling distribution and variability, bootstrapping, and confidence intervals-as they relate directly to data science. What you'll learn-and how you can apply it You'll learn how sampling distribution and sampling variability impact the results of statistical and machine learning models and impact your data quality. You'll also learn the bootstrap procedure-an easy and effective way to estimate the sampling distribution of a statistic, or of model parameters. Discover how to utilize confidence intervals-a method to express the potential error in a sample estimate, and present your estimates as an interval range, to communicate the potential error in an estimate, and learn whether you need a larger sample of data. This lesson is for you because: You're a data scientist or analyst working with data, and want to gain beginner-level knowledge of key statistical concepts to improve your data models and data quality. Prerequisites: Basic familiarity with coding in R Materials or downloads needed: None
    Note: "From Practical statistics for data scientists by Peter Bruce and Andrew Bruce"--Cover. - Date of publication from resource description page. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 16, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491978337/?ar
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Langford's basic photography : the guide for serious photographers (2015)
    New York : Focal Press
    Details
    ISBN: 9781317964728 , 1317964721
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: 10th edition.
    Parallel Title: Erscheint auch als
    Keywords: Photography ; Electronic books ; Electronic books ; local
    Abstract: This seminal photography text, now in its 10th edition and celebrating its 50th anniversary, has been revamped, reorganized, and modernized to include the most up-to-date, need to know information for photographers. Ideal for students, beginners, and advanced users wanting to brush up on the fundamentals of photography, this book is a must have for any photographer's bookcase. The heart of this text, however, retains the same comprehensive mix of scholarly and practical information. The new edition has been fully updated to reflect dynamic changes in the industry. These changes include: an expansion and overhaul of the information on digital cameras and digital printing; an emphasis on updating photographs to include a wider range of international work; replacement of many diagrams with photos; overhaul of the analogue sections to give a more modern tone (ie exposure measurement and film and filters with some more dynamic photo illustrations).
    Note: Originally published under title: Basic photography. - Includes index. - Description based on print version record
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 5
    Online Resource
    Online Resource
    Practical Statistics for Data Scientists, 2nd Edition (2020)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    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)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    MPI Ethno. Forsch.
  • 6
    Online Resource
    Online Resource
    ESTATÍSTICA PRÁTICA PARA CIENTISTAS DE DADOS (2019)
    [Erscheinungsort nicht ermittelbar] : Alta Books | Boston, MA : Safari
    Details
    Language: English , Portuguese
    Pages: 1 online resource (392 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Métodos estatísticos são uma parte crucial da ciência de dados; ainda assim, poucos cientistas de dados têm formação estatística.Os cursos e livros sobre estatística básica raramente abordam os tópicos sob a perspectiva da ciência de dados.Este guia prático explica como aplicar diversos métodos estatísticos em ciência de dados, ensina a evitar seu mau uso e aconselha sobre o que é importante e o que não é.Muitos recursos da ciência de dados incorporam métodos estatísticos, mas carecem de uma perspectiva estatística aprofundada.Se você está familiarizado com a linguagem de programação R e tem algum conhecimento estatístico, este guia fará a ponte de forma fácil e acessível.
    Note: Online resource; Title from title page (viewed April 30, 2019) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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  • 7
    Online Resource
    Online Resource
    面向数据科学家的实用统计学 (2018)
    [Erscheinungsort nicht ermittelbar] : Posts & Telecom Press | Boston, MA : Safari
    Details
    ISBN: 9787115493668
    Language: English , Chinese
    Pages: 1 online resource (238 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: 统计学方法是数据科学的重要组成部分，但极少有数据科学家接受过正规的统计学教育或培训，而关于统计学基础的课程和教材也很少从数据科学的角度进行讲解。本书专门从数据科学的角度阐释重要且实用的统计学概念，重点介绍如何将各种统计学方法应用于数据科学。 为什么探索性数据分析是数据科学关键的第一步 随机抽样如何降低偏差、生成高质量数据集 实验设计原则如何针对问题生成确定性答案 如何使用回归方法估计结果并检测异常 用于预测记录所属类别的主要分类方法 从数据中“学习”的统计机器学习方法 从未标记数据中提取有意义信息的无监督学习方法
    Note: Online resource; Title from title page (viewed October 1, 2018) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 8
    Online Resource
    Online Resource
    Praktische Statistik für Data Scientists, 2nd Edition (2021)
    [Erscheinungsort nicht ermittelbar] : dpunkt | Boston, MA : Safari
    Details
    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.
    URL: lizenzpflichtig
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  • 9
    Online Resource
    Online Resource
    Practical statistics for data scientists : 50 essential concepts (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    ISBN: 9781491952931 , 1491952938
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Statistics ; Data processing ; Statistics ; Electronic books ; Electronic books ; local
    Abstract: 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
    Note: Includes bibliographical references and index. - Description based on online resource; title from title page (viewed May 22, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491952955/?ar
    Library Location Call Number Volume/Issue/Year Availability
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  • 10
    Online Resource
    Online Resource
    Dēta saiensu no tame no tōkeigaku nyūmon : yosoku, bunrui, tōkei moderingu, tōkeiteki kikai gakushū to R/Python puroguramingu (2020)
    Tōkyō-to Shinjuku-ku : Orairī Japan
    Details
    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)
    URL: lizenzpflichtig
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