Data Science for Infectious Disease Data Analytics An Introduction with R
Category: eBooks
- views: 42
- date: 23 September 2026
- posted by: BeMyLove
Data Science for Infectious Disease Data Analytics An Introduction with R | 30.37 MB
Title: Data Science for Infectious Disease Data Analytics An Introduction with R
Author: Wang, Lily
Category: Science, Statistics, Biology, Database Management, Medical, Mathematics, General Computing, Computer Science, Application Software, Epidemiology
Language: English | ISBN: 9781000643084
Description:
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 dis- covered coronavirus disease (COVID-19). The primary emphasis of this book will be the data science procedures in epidemiological stud- ies, 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 transfer- able 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. Key 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. Describes 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 in- fectious diseases data with valuable insights on how different models and methods could be connected.
DOWNLOAD:
https://rapidgator.net/file/e89201eff7f49f5c50794cb4c3c80223/Data_Science_for_Infectious_Disease_Data_Analytics_An_Introduction_with_R.rar
https://nitroflare.com/view/47B3D871A1852E2/Data_Science_for_Infectious_Disease_Data_Analytics_An_Introduction_with_R.rar
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 dis- covered coronavirus disease (COVID-19). The primary emphasis of this book will be the data science procedures in epidemiological stud- ies, 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 transfer- able 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. Key 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. Describes 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 in- fectious diseases data with valuable insights on how different models and methods could be connected.
DOWNLOAD:
https://rapidgator.net/file/e89201eff7f49f5c50794cb4c3c80223/Data_Science_for_Infectious_Disease_Data_Analytics_An_Introduction_with_R.rar
https://nitroflare.com/view/47B3D871A1852E2/Data_Science_for_Infectious_Disease_Data_Analytics_An_Introduction_with_R.rar
We need your support!
Make a donation to help us stay online
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem
Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u
USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm
Information |
|||
 |
Users of GUESTS are not allowed to comment this publication. | ||