Geographical Data Science and Spatial Data Analysis: An Introduction in R (Comber Lex)(Paperback)

Geographical Data Science and Spatial Data Analysis: An Introduction in R (Comber Lex)(Paperback)

We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial - it is collected some-where - and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges

Geographical Data Science and Spatial Data Analysis: An Introduction in R (Comber Lex)(Pevná vazba)

We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial - it is collected some-where - and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges

Applied Spatial Statistics and Econometrics: Data Analysis in R (Kopczewska Katarzyna)(Paperback)

This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcases key topics, including unsupervised learning, causal

Introduction to Spatial Data Analysis (Wegmann Martin)(Paperback)

Quantitative Social Science: An Introduction (Imai Kosuke)(Paperback)

An introductory textbook on data analysis and statistics written especially for students in the social sciences and allied fields Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little

Doing Data Science in R: An Introduction for Social Scientists (Andrews Mark)(Paperback)

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. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling

An Introduction to Data Science (Saltz Jeffrey S.)(Paperback)

An Introduction to Data Science by Jeffrey S. Saltz and Jeffrey M. Stanton is an easy-to-read, gentle introduction for people with a wide range of backgrounds into the world of data science. Needing no prior coding experience or a deep understanding of statistics, this book uses the R programming

An Introduction to Data Analysis (Bergin Tiffany)(Paperback)

Covering the general process of data analysis to finding, collecting, organizing, and presenting data, this book offers a complete introduction to the fundamentals of data analysis. Using real-world case studies as illustrations, it helps readers understand theories behind and develop techniques

Quantitative Social Science: An Introduction in Stata (Imai Kosuke)(Paperback)

The Stata edition of the groundbreaking textbook on data analysis and statistics for the social sciences and allied fieldsQuantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in

Luminescence: Data Analysis and Modeling Using R (Pagonis Vasilis)(Paperback)

1. Introduction.- 2. Analysis and Modeling of TL Data.- 3. Analysis of Experimental OSL Data.- 4. Dose Response of Dosimetric Materials.- 5. Monte Carlo Simulations With Fixed Time Interval.- 6. Luminescence as a Stochastic Life-and-Death Process.- 7. Delocalized Transitions: The R Package

An Introduction to Statistical Genetic Data Analysis (Mills Melinda C.)(Paperback)

A comprehensive introduction to modern applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics.Human genetic research is now relevant beyond biology, epidemiology, and the medical sciences, with applications in such fields as psychology,

Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks (Villalobos Alva Jalil)(Paperback)

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book will introduce you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and

Data Science Foundations Tools and Techniques: Core Skills for Quantitative Analysis with R and Git (Freeman Michael)(Paperback)

The Foundational Hands-On Skills You Need to Dive into Data Science Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills

Analysis in Qualitative Research (Boeije Hennie R.)(Paperback)

This book helps readers prepare and successfully finish qualitative data analysis. It sets out an easy to grasp model--the spiral of analysis--that shows how to break up the data with coding and how to subsequently integrate the data to yield a meaningful picture of the phenomenon under study. It

R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (Long Jd)(Paperback)

Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R

Foundations of Data Science (Blum Avrim)(Pevná vazba)

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques

Qualitative and Mixed Methods Data Analysis Using Dedoose: A Practical Approach for Research Across the Social Sciences (Salmona Michelle Suzanne)(Paperback)

Qualitative and Mixed Methods Data Analysis Using Dedoose(R) A Practical Approach for Research Across the Social Sciences by Michelle Salmona, Eli Lieber, Dan Kaczynski provides both new and experienced researchers with a guided introduction to dealing with the methodological complexity of mixed

Model-Based Clustering and Classification for Data Science: With Applications in R (Bouveyron Charles)(Pevná vazba)

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified

Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Igual Laura)(Paperback)

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel

Spatial Regression Models (Ward Michael D.)(Paperback)

Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including: mapping

Spatial Analysis Methods and Practice: Describe - Explore - Explain Through GIS (Grekousis George)(Paperback)

This is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing

Spatial Data Mining: Theory and Application (Li Deren)(Pevná vazba)

- This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial information

Data Management in R: A Guide for Social Scientists (Elff Martin)(Paperback)

An invaluable, step-by-step guide to data management in R for social science researchers. This book shows students how to recode data, combine data from different sources, document data, and import data from statistical packages other than R. It explores both qualitative and quantitative data and

An Introduction to R for Spatial Analysis and Mapping (Brunsdon Chris)(Pevná vazba)

This is a new edition of the accessible and student-friendly ′how to′ for anyone using R for the first time, for use in spatial statistical analysis, geocomputation and digital mapping. The authors, once again, take readers from 'zero to hero', updating the now standard text to further enable