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| 001 | 9781003256328 | ||
| 003 | FlBoTFG | ||
| 005 | 20240213122827.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 221102s2022 flua ob 001 0 eng d | ||
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_aOCoLC-P _beng _erda _epn _cOCoLC-P |
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| 020 |
_a9781003256328 _q(electronic bk.) |
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_a1003256325 _q(electronic bk.) |
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| 020 |
_a1000643069 _q(electronic bk. : PDF) |
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| 020 |
_a9781000643084 _q(electronic bk. : EPUB) |
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| 020 |
_a1000643085 _q(electronic bk. : EPUB) |
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| 020 |
_a9781000643060 _q(electronic bk.) |
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| 020 | _z9781032187426 | ||
| 020 | _z1032187425 | ||
| 024 | 7 |
_a10.1201/9781003256328 _2doi |
|
| 035 | _a(OCoLC)1349718992 | ||
| 035 | _a(OCoLC-P)1349718992 | ||
| 050 | 4 | _aRA652.2.M3 | |
| 072 | 7 |
_aBUS _x061000 _2bisacsh |
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_aCOM _x021030 _2bisacsh |
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_aMAT _x029000 _2bisacsh |
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| 072 | 7 |
_aPBT _2bicssc |
|
| 082 | 0 | 4 |
_a614.4 _223 |
| 100 | 1 |
_aWang, Lily _c(Professor of Statistics), _eauthor. |
|
| 245 | 1 | 0 |
_aData science for infectious disease data analytics : _ban introduction with R / _cLily Wang. |
| 264 | 1 |
_aBoca Raton : _bChapman & Hall/CRC, _c2022. |
|
| 300 |
_a1 online resource (1 volume) : _billustrations (black and white). |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 490 | 0 | _aChapman & Hall/CRC data science series | |
| 520 | _aData Science for Infectious Disease Data Analytics: An Introduction with R provides an overview of modern data science tools and methods that have been developed specifically to analyze infectious disease data. With a quick start guide to epidemiological data visualization and analysis in R, this book spans the gulf between academia and practices providing many lively, instructive data analysis examples using the most up-to-date data, such as the newly discovered coronavirus disease (COVID-19). The primary emphasis of this book is the data science procedures in epidemiological studies, including data wrangling, visualization, interpretation, predictive modeling, and inference, which is of immense importance due to increasingly diverse and nonexperimental data across a wide range of fields. The knowledge and skills readers gain from this book are also transferable to other areas, such as public health, business analytics, environmental studies, or spatio-temporal data visualization and analysis in general. Aimed at readers with an undergraduate knowledge of mathematics and statistics, this book is an ideal introduction to the development and implementation of data science in epidemiology. Features Describes the entire data science procedure of how the infectious disease data are collected, curated, visualized, and fed to predictive models, which facilitates effective communication between data sources, scientists, and decision-makers. Explains practical concepts of infectious disease data and provides particular data science perspectives. Overview of the unique features and issues of infectious disease data and how they impact epidemic modeling and projection. Introduces various classes of models and state-of-the-art learning methods to analyze infectious diseases data with valuable insights on how different models and methods could be connected. | ||
| 588 | _aOCLC-licensed vendor bibliographic record. | ||
| 650 | 0 |
_aEpidemiology _xStatistical methods. |
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| 650 | 0 |
_aEpidemiology _xData processing. |
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| 650 | 0 | _aR (Computer program language) | |
| 650 | 7 |
_aBUSINESS & ECONOMICS / Statistics _2bisacsh |
|
| 650 | 7 |
_aCOMPUTERS / Database Management / Data Mining _2bisacsh |
|
| 650 | 7 |
_aMATHEMATICS / Probability & Statistics / General _2bisacsh |
|
| 856 | 4 | 0 |
_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003256328 |
| 856 | 4 | 2 |
_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf |
| 999 |
_c5155 _d5155 |
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