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  • 1
    Book
    Book
    Oxford [u.a.] :Oxford Univ. Press,
    ISBN: 0-19-857224-7 , 0-19-857225-5
    Language: English
    Pages: XV, 299 S. : , graph. Darst.
    Edition: Repr.
    DDC: 519.50711
    RVK:
    RVK:
    RVK:
    Keywords: Statistik. ; Studium. ; Unterrichtsmethode. ; Unterricht. ; Statistik ; Studium ; Unterrichtsmethode ; Statistik ; Unterricht
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Book
    Book
    Oxford [u.a.] :Oxford Univ. Press,
    ISBN: 978-0-19-857224-4
    Language: English
    Pages: XV, 299 S. : , graph. Darst.
    Edition: Repr.
    RVK:
    RVK:
    RVK:
    Keywords: Statistik. ; Studium. ; Unterrichtsmethode. ; Unterricht. ; Statistik ; Studium ; Unterrichtsmethode ; Statistik ; Unterricht
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    ISBN: 9781482234824 , 1482234823
    Language: English
    Pages: 1 online resource (xxiii, 505 pages) , illustrations.
    Series Statement: Chapman & Hall/CRC the R series
    Keywords: R (Computer program language) ; Statistics ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book's collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including: Non-standard, complex data formats, such as robot logs and email messages Text processing and regular expressions Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes Visualization and exploratory data analysis Relational databases and Structured Query Language (SQL) Simulation Algorithm implementation Large data and efficiency Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data. Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers' computational reasoning of real-world data analyses.
    Note: "A Chapman & Hall book."--T.p. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 6, 2015)
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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 (70 pages)
    Edition: 1st edition
    DDC: 004
    Keywords: Information technology ; Information retrieval ; Databases ; Data mining ; Electronic books
    Abstract: As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas. Refine a question of interest to one that can be studied with data Pursue data collection that may involve text processing, web scraping, etc. Glean valuable insights about data through data cleaning, exploration, and visualization Learn how to use modeling to describe the data Generalize findings beyond the data
    Note: Online resource; Title from title page (viewed May 25, 2023) , Mode of access: World Wide Web.
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