03433nam a22005415i 4500 978-3-031-46383-9 DE-He213 20251030112034.0 cr nn 008mamaa 240510s2024 sz | s |||| 0|eng d 9783031463839 978-3-031-46383-9 10.1007/978-3-031-46383-9 doi QH323.5 PBT bicssc MED090000 bisacsh PBT thema MacFarland, Thomas W. author. aut http://id.loc.gov/vocabulary/relators/aut 76598 Introduction to Data Science in Biostatistics [electronic resource] : Using R, the Tidyverse Ecosystem, and APIs / by Thomas W. MacFarland. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XVII, 528 p. 97 illus., 91 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 1. Emergence of Data Science as a Critical Discipline in Biostatistics -- 2. Data Sources in Biostatistics -- 3. Role of Statistics for Decision Making in Biostatistics -- 4. Data Science and R, Base R, and the tidyverse Ecosystem -- 5. Statistical Analyses and Graphical Presentations in Biostatistics Using Base R and the tidyverse Ecosystem -- 6. Use of R-Based APIs to Obtain Data -- 7. Putting It All Together: R, the tidyverse Ecosystem, and APIs. Introduction to Data Science in Biostatistics: Using R, the Tidyverse Ecosystem, and APIs defines and explores the term "data science" and discusses the many professional skills and competencies affiliated with the industry. With data science being a leading indicator of interest in STEM fields, the text also investigates this ongoing growth of demand in these spaces, with the goal of providing readers who are entering the professional world with foundational knowledge of required skills, job trends, and salary expectations. The text provides a historical overview of computing and the field's progression to R as it exists today, including the multitude of packages and functions associated with both Base R and the tidyverse ecosystem. Readers will learn how to use R to work with real data, as well as how to communicate results to external stakeholders. A distinguishing feature of this text is its emphasis on the emerging use of APIs to obtain data. Biometry. 65981 Artificial intelligence Data processing. Statistics . 65982 Quantitative research. Biostatistics. 65985 Data Science. Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 66911 Data Analysis and Big Data. SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031463822 Printed edition: 9783031463846 Printed edition: 9783031463853 https://doi.org/10.1007/978-3-031-46383-9 ZDB-2-SMA ZDB-2-SXMS 59749 59749