Data science for infectious disease data analytics : an introduction with R /
Wang, Lily (Professor of Statistics),
Data science for infectious disease data analytics : an introduction with R / Lily Wang. - 1 online resource (1 volume) : illustrations (black and white). - Chapman & Hall/CRC data science series .
Data 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.
9781003256328 1003256325 1000643069 9781000643084 1000643085 9781000643060
10.1201/9781003256328 doi
Epidemiology--Statistical methods.
Epidemiology--Data processing.
R (Computer program language)
BUSINESS & ECONOMICS / Statistics
COMPUTERS / Database Management / Data Mining
MATHEMATICS / Probability & Statistics / General
RA652.2.M3
614.4
Data science for infectious disease data analytics : an introduction with R / Lily Wang. - 1 online resource (1 volume) : illustrations (black and white). - Chapman & Hall/CRC data science series .
Data 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.
9781003256328 1003256325 1000643069 9781000643084 1000643085 9781000643060
10.1201/9781003256328 doi
Epidemiology--Statistical methods.
Epidemiology--Data processing.
R (Computer program language)
BUSINESS & ECONOMICS / Statistics
COMPUTERS / Database Management / Data Mining
MATHEMATICS / Probability & Statistics / General
RA652.2.M3
614.4