Data science and analysis: general

A hands-on approach to quantitative reasoning in the life sciencesQuantitative Biosciences establishes the quantitative principles of how living systems work across scales, drawing on classic and modern discoveries to present a case study approach that links mechanisms, models, and measurements.

Focuses on insights, approaches, and techniques that are essential to designing interactive graphics and visualizations Making Sense of Data III: A Practical Guide to Designing Interactive Data Visualizations explores a diverse range of disciplines to explain how meaning from graphical representations is extracted.

Single system, or single case, design studies are a convenient method for evaluating practice, allowing professionals to track clients' response to treatment and change over time.

Every new tractor now contains built-in sensors that collect data and stream it to cloud-based infrastructure.

It humanizes high-level debates over indicators and data in development aid, showing how they are used to make life-or-death decisions.

In order to assist a hospital in managing its resources and patients, modelling the length of stay is highly important.

This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis.

A hands-on lab guide in the MATLAB programming language that enables students in the life sciences to reason quantitatively about living systems across scalesThis lab guide accompanies the textbook Quantitative Biosciences, providing students with the skills they need to translate biological principles and mathematical concepts into computational models of living systems.

Comprehensive overview of the spectroscopic, mineralogical, and geochemical techniques used in planetary remote sensing.

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

We are in an age of big data where all of our everyday interactions and transactions generate data.

A Mathematical Primer for Social Statistics, Second Edition presents mathematics central to learning and understanding statistical methods beyond the introductory level: the basic "e;language"e; of matrices and linear algebra and its visual representation, vector geometry; differential and integral calculus; probability theory; common probability distributions; statistical estimation and inference, including likelihood-based and Bayesian methods.

Tools from macroeconomics and finance to incorporate the central role that data plays in the economyThe most valuable firms in the global economy are valued largely for their data.

This compact, entry-level Handbook equips applied practitioners to choose and use core models for real-world data - with R and SAS.

Electronic Health Records (EHR) are longitudinal data that are stored in a database that captures current and new patients at different points in time.

A Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.

Textbook for students and researchers in oceanography and Earth science on theory and practice of time series analysis using MATLAB.

A year of HBR's essential thinking on techall in one place.

An ideal textbook for complete beginnersteaches from scratch R, statistics, and the fundamentals of quantitative social scienceData Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies.

A clear easy-to-read guide to presenting your message using statistical data Poor presentation of data is everywhere; basic principles are forgotten or ignored.

Analyzes scale effects across a range of political dimensions, encompassing different political levels using a multi-method approach.

The world is currently in the midst of a data revolution.

The 3rd edition of this popular textbook introduces the reader to the investigation of vegetation systems with an emphasis on data analysis.

Organizations often start their data journey by either procuring the technology or hiring the people.

This book mainly focuses on why data analytics fails in business.

'A statistical national treasure' Jeremy Vine, BBC Radio 2'Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics.

This volume highlights the ways in which recent developments in corpus linguistics and natural language processing can engage with topics across language studies, humanities and social science disciplines.

The research landscape is changing, with key global research funders now requiring institutions to demonstrate how they will preserve and share research data.

The global data market is estimated to be worth $64 billion dollars, making it a more valuable resource than oil.

The 3rd edition of this popular textbook introduces the reader to the investigation of vegetation systems with an emphasis on data analysis.

A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.

It humanizes high-level debates over indicators and data in development aid, showing how they are used to make life-or-death decisions.

An insider's guide to data librarianship packed full of practical examples and advice for any library and information professional learning to deal with data.

Explains the rising importance of emerging markets to give a better understanding of current development challenges.

Spatial Data Science will show GIS scientists and practitioners how to add and use new analytical methods from data science in their existing GIS platforms.

The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science.

Open government data (OGD) has developed rapidly in recent years due to various benefits that can be derived through transparency and public access.

Data is constantly increasing and data analysts are in higher demand than ever.

A practical introduction to network science for students across business, cognitive science, neuroscience, sociology, biology, engineering and other disciplines.