Data science and analysis: general

Computational approaches offer exciting opportunities for us to do social science differently.

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.

This handbook offers everything students and scholars need to master the craft of developing insightful and delightful data visualisations.

This is a book about how ecologists can integrate remote sensing and GIS in their research.

Presents an analysis and visualization of social networks integrating theory, applications, and professional software for performing network analysis (Pajek).

This book showcases the different ways in which contemporary forms of data analysis are being used in urban planning and management.

This beginning graduate textbook teaches data science and machine learning methods for modeling, prediction, and control of complex systems.

Businesses who can make sense of the huge influx and complexity of data will be the big winners in the information economy.

Computational approaches offer exciting opportunities for us to do social science differently.

Examines a set of voter information campaigns worldwide to assess their effectiveness, and develops a new social science research model aimed at cumulative learning.

Introduces network inspired approaches for the analysis and integration of large, heterogeneous data sets in the life sciences.

'I couldn't imagine a better guidebook for making sense of a tragic and momentous time in our lives.

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.

Leaders, don't let AI get the best of you.

This beginning graduate textbook teaches data science and machine learning methods for modeling, prediction, and control of complex systems.

R is a freely available, open-source statistical programming environment which provides powerful statistical analysis tools and graphics outputs.

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.

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.

This is a book about the scientific process and how you apply it to data in ecology.

Statistical analysis is common in the social sciences, and among the more popular programs is R.

This volume introduces a new framework for conceptualizing and achieving corpus representativeness in a rigorous, yet practical way.

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.

Achieve successful digital transformation with this authoritative guide designed specifically for established organizations.

A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.

Don't simply show your data tell a story with it!

Discusses methods available for longitudinal data analysis in non-technical language, allowing readers to apply techniques to their work.

This is an integrated introduction to methods, research design, and data analysis tailored to the challenges of cross-cultural research.

Minds Alive explores the enduring role and intrinsic value of libraries, archives, and public institutions in the digital age.

The correct design, analysis and interpretation of plant science experiments is imperative for continued improvements in agricultural production worldwide.

This exciting new textbook offers an accessible, business-focused overview of the key theoretical concepts underpinning modern data analytics.

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.

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.

A comprehensive introduction to flexible, efficient tools for describing massive data sets to improve the scalability of data analysis.

A methodological toolkit for social scientists to create their own infrastructure for collecting and analyzing social media big data.

The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time.

Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples.

This 2001 book presents important and practically relevant quantitative models used for marketing research.

In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys and phone logs.