Measuring the abundance of individuals and the diversity of species are core components of most ecological research projects and conservation monitoring.
Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope.
Presents an analysis and visualization of social networks integrating theory, applications, and professional software for performing network analysis (Pajek).
Examines a set of voter information campaigns worldwide to assess their effectiveness, and develops a new social science research model aimed at cumulative learning.
Research Data Management (RDM) has become a professional topic of great importance internationally following changes in scholarship and government policies about the sharing of research data.
Question answering (QA) systems on the Web try to provide crisp answers to information needs posed in natural language, replacing the traditional ranked list of documents.
Jump-start your career as a data scientist learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most.
Focus on Geodatabases in ArcGIS Pro introduces readers to the geodatabase, the comprehensive information model for representing and managing geographic information across the ArcGIS platform.
Although there has been a surge of interest in density estimation in recent years, much of the published research has been concerned with purely technical matters with insufficient emphasis given to the technique's practical value.
Crime research has grown substantially over the past decade, with a rise in evidence-informed approaches to criminal justice, statistics-driven decision-making and predictive analytics.
A practical introduction to network science for students across business, cognitive science, neuroscience, sociology, biology, engineering and other disciplines.
Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope.
The correct design, analysis and interpretation of plant science experiments is imperative for continued improvements in agricultural production worldwide.
This practical textbook offers a hands-on introduction to big data analytics, helping you to develop the skills required to hit the ground running as a data professional.
Shortlisted for the William Hill Sports Book of the Year Award 2022Football has always measured success by what you win, but only in the last twenty years have clubs started to think about how you win.
Jump-start your career as a data scientist learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most.
Become an expert at using Python for advanced statistical analysis of data using real-world examplesAbout This BookClean, format, and explore data using graphical and numerical summariesLeverage the IPython environment to efficiently analyze data with PythonPacked with easy-to-follow examples to develop advanced computational skills for the analysis of complex dataWho This Book Is ForIf you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you.
