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  • Bruce, Peter C.  (6)
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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
    Data mining for business intelligence : concepts, techniques, and applications in Microsoft Office Excel with XLMiner (2010)
    Hoboken, N.J. : Wiley
    Details
    Language: English
    Pages: 1 online resource (xxiv, 404 p.) , ill.
    Edition: 2nd ed.
    Parallel Title: Erscheint auch als
    Keywords: Microsoft Excel (Computer file) ; Business ; Data processing ; Data mining ; Electronic books ; local
    Abstract: Data Mining for Business Intelligence, Second Edition uses real data and actual cases to illustrate the applicability of data mining (DM) intelligence in the development of successful business models. Featuring complimentary access to XLMiner®, the Microsoft Office Excel® add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of DM techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples, now doubled in number in the second edition, are provided to motivate learning and understanding. This book helps readers understand the beneficial relationship that can be established between DM and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions. New topics include detailed coverage of visualization (enhanced by Spotfire subroutines) and time series forecasting, among a host of other subject matter.
    Note: Description based on print version record. - Includes bibliographical references and index
    URL: https://learning.oreilly.com/library/view/-/9780470526828/?ar
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    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.
  • 4
    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.
  • 5
    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
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
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  • 6
    Online Resource
    Online Resource
    Shu ju ke xue zhong de shi yong tong ji xue = : Practical statistics for data scientists (2021)
    Beijing ; : Ren min you dian chu ban she
    Details
    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
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
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