Data science and analysis: general Books

Since the original publication of the bestselling Modelling Binary Data, a number of important methodological and computational developments have emerged, accompanied by the steady growth of...

Complex Survey Data Analysis with SAS® is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal...

As the amount of information recorded and stored electronically grows ever larger, it becomes increasingly useful, if not essential, to develop better and more efficient ways to summarize and extract...

Suitable for a compact course or self-study, Computational Statistics: An Introduction to R illustrates how to use the freely available R software package for data analysis, statistical programming,...

Since most datasets contain a number of variables, multivariate methods are helpful in answering a variety of research questions. Accessible to students and researchers without a substantial...

Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the...

Business students need the ability to think statistically about how to deal with uncertainty and its effect on decision-making in business and management. Traditional statistics courses and textbooks...

Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed. However, many researchers lack the statistical skills or resources that would allow...

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more...

Building from the perspective of reproductive and developmental biology, Computational Methods for Reproductive and Developmental Toxicology provides a timely and comprehensive overview of approaches...

Sensory characterization is one of the most powerful, sophisticated, and extensively applied tools in sensory science. Descriptive analysis with trained assessors has been traditionally used for...

Security Analytics for the Internet of Everything compiles the latest trends, technologies, and applications in this emerging field. It includes chapters covering emerging security trends, cyber...

Data Science for Effective Healthcare Systems has a prime focus on the importance of data science in the healthcare domain. Various applications of data science in the health care domain have been...

Quantitative Data Analysis for Language Assessment Volume II: Advanced Methods demonstrates advanced quantitative techniques for language assessment. The volume takes an interdisciplinary approach...

The advent of "Big Data" has brought with it a rapid diversification of data sources, requiring analysis that accounts for the fact that these data have often been generated and recorded for...

Shortlisted for the Business Book of the Year Awards in the Sales and Marketing category.As jobs become increasingly similar, there are two skills that everyone needs if they’re going to thrive...

The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse...

R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was...

Statistics for Finance develops students’ professional skills in statistics with applications in finance. Developed from the authors’ courses at the Technical University of Denmark and Lund...

This book provides an accessible yet rigorous introduction to topology and homology focused on the simplicial space. It presents a compact pipeline from the foundations of topology to biomedical...

Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools,...

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern...

Hands-on Data Science for Biologists using Python has been conceptualized to address the massive data handling needs of modern-day biologists. With the advent of high throughput technologies and...

Spatial predictive modeling (SPM) is an emerging discipline in applied sciences, playing a key role in the generation of spatial predictions in various disciplines. SPM refers to preparing relevant...

Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main...

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and...

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data...

"Data Science Foundations is most welcome and, indeed, a piece of literature that the field is very much in need of…quite different from most data analytics texts which largely ignore foundational...

Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large...

Interactive Graphics for Data Analysis: Principles and Examples discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new...

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis,...

As the amount of information recorded and stored electronically grows ever larger, it becomes increasingly useful, if not essential, to develop better and more efficient ways to summarize and extract...

By presenting the conditions, methods and techniques of monetisation of business models in the digital economy, this book combines implementation of the theoretical aspects of monetisation with the...

Modelling Transitions shows what computational, formal and data-driven approaches can and could mean for sustainability transitions research, presenting the state-of-the-art and exploring what lies...

Winner of the 2025 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)Focusing on Bayesian approaches and computations using analytic and simulation-based methods for...

The challenges faced by 21st-century businesses, organizations and governments are characterized as being fundamentally different in nature, scope and levels of impact from those of the past. As...

This book provides multifaceted components and full practical perspectives of systems engineering and risk management in security and defense operations with a focus on infrastructure and manpower...

The unprecedented amount of data produced with high-throughput experimentation forces biologists to employ mathematical representation and computation methods to glean meaningful information in...

For increasingly data-savvy clients, lawyers can no longer give "it depends" answers rooted in anecdata. Clients insist that their lawyers justify their reasoning, and with more than a limited set of...

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes...

Investigative Reporting provides a step-by-step approach for tackling any investigative story, teaching reporters the skills they need to overcome common obstacles during investigative work...

Investigative Reporting provides a step-by-step approach for tackling any investigative story, teaching reporters the skills they need to overcome common obstacles during investigative work...

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to...

Big Data is everywhere. It shapes our lives in more ways than we know and understand. This comprehensive introduction unravels the complex terabytes that will continue to shape our lives in ways...

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding...

This reference text covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and engages in a discussion on deep learning-based ultra-dense cell...

This book explores the critical issue of fairness in human-machine interfaces. It delves into the integration of technology and cognitive science to develop AI systems that are unbiased, reliable,...

Industry 4.0 is a revolutionary concept that aims to enhance productivity and profitability in various industries through the implementation of smart manufacturing techniques. This book discusses the...