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Probability and Statistics for Data Science Math + R + Data

Practitioner’s Guide to Data Science

Practitioner’s Guide to Data Science

This book aims to increase the visibility of data science in real-world which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics machine learning and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields soft and programming skills in data science projects and potential career paths. Traditional data-related practitioners such as statisticians business analysts and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: • It covers both technical and soft skills. • It has a chapter dedicated to the big data cloud environment. For industry applications the practice of data science is often in such an environment. • It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems if possible. The best way to learn data science is to do it! | Practitioner’s Guide to Data Science

GBP 52.99
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Statistical Analytics for Health Data Science with SAS and R

Statistical Analytics for Health Data Science with SAS and R

This book aims to compile typical fundamental-to-advanced statistical methods to be used for health data sciences. Although the book promotes applications to health and health-related data the models in the book can be used to analyze any kind of data. The data are analyzed with the commonly used statistical software of R/SAS (with online supplementary on SPSS/Stata). The data and computing programs will be available to facilitate readers’ learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for the most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models compilations of a variety of publicly available data and illustrations of data analytics using commonly used statistical software of SAS/R. We will have the data and computer programs available for readers to replicate and implement the new methods. The primary readers would be applied data scientists and practitioners in any field of data science applied statistical analysts and scientists in public health academic researchers and graduate students in statistics and biostatistics. The secondary readers would be R&D professionals/practitioners in industry and governmental agencies. This book can be used for both teaching and applied research. | Statistical Analytics for Health Data Science with SAS and R

GBP 74.99
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Modern Data Science with R

R for Political Data Science A Practical Guide

R for Political Data Science A Practical Guide

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 written by political scientists thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first on the use of R is for those users who are learning R or are migrating from another software. The second section on econometric models covers OLS binary and survival models panel data and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation fuzzy merge of large datasets web mining quantitative text analysis network analysis mapping spatial cluster analysis and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package including all data As the title suggests this book is highly applied in nature and is designed as a toolbox for the reader. It can be used in methods and data science courses at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD political consultants or a public official all of whom need to transform their datasets into substantive and easily interpretable conclusions. | R for Political Data Science A Practical Guide

GBP 44.99
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Spatial Statistics for Data Science Theory and Practice with R

Spatial Statistics for Data Science Theory and Practice with R

Spatial data is crucial to improve decision-making in a wide range of fields including environment health ecology urban planning economy and society. Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods modeling approaches and visualization techniques to analyze spatial data using R. The book provides a comprehensive overview of the varying types of spatial data and detailed explanations of the theoretical concepts of spatial statistics alongside fully reproducible examples which demonstrate how to simulate describe and analyze spatial data in various applications. Combining theory and practice the book includes real-world data science examples such as disease risk mapping air pollution prediction species distribution modeling crime mapping and real state analyses. The book utilizes publicly available data and offers clear explanations of the R code for importing manipulating analyzing and visualizing data as well as the interpretation of the results. This ensures contents are easily accessible and fully reproducible for students researchers and practitioners. Key Features: Describes R packages for retrieval manipulation and visualization of spatial data. Offers a comprehensive overview of spatial statistical methods including spatial autocorrelation clustering spatial interpolation model-based geostatistics and spatial point processes. Provides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches. | Spatial Statistics for Data Science Theory and Practice with R

GBP 74.99
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Data Science with Julia

Data Science with Julia

This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist. Professor Charles Bouveyron INRIA Chair in Data Science Université Côte d’Azur Nice France Julia an open-source programming language was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible intuitive and highly efficient base language with speed that exceeds R and Python makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Features: Covers the core components of Julia as well as packages relevant to the input manipulation and representation of data. Discusses several important topics in data science including supervised and unsupervised learning. Reviews data visualization using the Gadfly package which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Presents how to optimize Julia code for performance. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). The advantages of Julia for data science cannot be understated. Besides speed and ease of use there are already over 1 900 packages available and Julia can interface (either directly or through packages) with libraries written in R Python Matlab C C++ or Fortran. The book is for senior undergraduates beginning graduate students or practicing data scientists who want to learn how to use Julia for data science. This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist. Professor Charles BouveyronINRIA Chair in Data ScienceUniversité Côte d’Azur Nice France

GBP 51.99
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Data Science for Water Utilities Data as a Source of Value

Basketball Data Science With Applications in R

Data Science A First Introduction

Data Science in Practice

Data Science for Wind Energy

Hands-On Data Science for Librarians

Hands-On Data Science for Librarians

Librarians understand the need to store use and analyze data related to their collection patrons and institution and there has been consistent interest over the last 10 years to improve data management analysis and visualization skills within the profession. However librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping working with maps creating interactive reports machine learning and others. While there’s a place for theory ethics and statistical methods librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work no matter what type of library they work at (academic public or special). By walking through each skill and its application to library work before walking the reader through each line of code this book will support librarians who want to apply data science in their daily work. Hands-On Data Science for Librarians is intended for librarians (and other information professionals) in any library type (public academic or special) as well as graduate students in library and information science (LIS). Key Features: Only data science book available geared toward librarians that includes step-by-step code examples Examples include all library types (public academic special) Relevant datasets Accessible to non-technical professionals Focused on job skills and their applications

GBP 52.99
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Big Data for Regional Science

GBP 44.99
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Introduction to Data Science Data Analysis and Prediction Algorithms with R

Introduction to Data Science Data Analysis and Prediction Algorithms with R

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability statistical inference linear regression and machine learning. It also helps you develop skills such as R programming data wrangling data visualization predictive algorithm building file organization with UNIX/Linux shell version control with Git and GitHub and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary although some experience with programming may be helpful. The book is divided into six parts: R data visualization statistics with R data wrangling machine learning and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state self-reported student heights trends in world health and economics the impact of vaccines on infectious disease rates the financial crisis of 2007-2008 election forecasting building a baseball team image processing of hand-written digits and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course. | Introduction to Data Science Data Analysis and Prediction Algorithms with R

GBP 82.99
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Big Data and Social Science Data Science Methods and Tools for Research and Practice

Big Data and Social Science Data Science Methods and Tools for Research and Practice

Big Data and Social Science: Data Science Methods and Tools for Research and Practice Second Edition shows how to apply data science to real-world problems covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences statistics and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data apply data science methods and tools to the data and recognize and respond to data errors biases and limitations. Features: Takes an accessible hands-on approach to handling new types of data in the social sciences Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes Illustrates social science and data science principles through real-world problems Links computer science concepts to practical social science research Promotes good scientific practice Provides freely available workbooks with data code and practical programming exercises through Binder and GitHub New to the Second Edition: Increased use of examples from different areas of social sciences New chapter on dealing with Bias and Fairness in Machine Learning models Expanded chapters focusing on Machine Learning and Text Analysis Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner. | Big Data and Social Science Data Science Methods and Tools for Research and Practice

GBP 52.99
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Geographic Data Science with R Visualizing and Analyzing Environmental Change

Geographic Data Science with R Visualizing and Analyzing Environmental Change

The burgeoning field of data science has provided a wealth of techniques for analysing large and complex geospatial datasets including descriptive explanatory and predictive analytics. However applying these methods is just one part of the overall process of geographic data science. Other critical steps include screening for suspect data values handling missing data harmonizing data from multiple sources summarizing the data and visualizing data and analysis results. Although there are many books available on statistical and machine learning methods few encompass the broader topic of scientific workflows for geospatial data processing and analysis. The purpose of Geographic Data Science with R is to fill this gap by providing a series of tutorials aimed at teaching good practices for using geospatial data to address problems in environmental geography. It is based on the R language and environment which currently provides the best option for working with diverse spatial and non-spatial data in a single platform. Fundamental techniques for processing and visualizing tabular vector and raster data are introduced through a series of practical examples followed by case studies that combine multiple types of data to address more complex problems. The book will have a broad audience. Both students and professionals can use it as a workbook to learn high-level techniques for geospatial data processing and analysis with R. It is also suitable as a textbook. Although not intended to provide a comprehensive introduction to R it is designed to be accessible to readers who have at least some knowledge of coding but little to no experience with R. Key Features: Focus on developing practical workflows for processing and integrating multiple sources of geospatial data in R Example-based approach that teaches R programming and data science concepts through real-world applications related to climate land cover and land use and natural hazards. Consistent use of tidyverse packages for tabular data manipulation and visualization. Strong focus on analysing continuous and categorical raster datasets using the new terra package Organized so that each chapter builds on the topics and techniques covered in the preceding chapters Can be used for self-study or as the textbook for a geospatial science course. | Geographic Data Science with R Visualizing and Analyzing Environmental Change

GBP 74.99
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Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry academia and government. It introduces the tidyverse suite of R packages including the ggplot2 package for data visualization and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses the book covers traditional introductory statistics topics like confidence intervals hypothesis testing and multiple regression modeling while focusing on visualization throughout. Features: ● Assumes minimal prerequisites notably no prior calculus nor coding experience ● Motivates theory using real-world data including all domestic flights leaving New York City in 2013 the Gapminder project and the data journalism website FiveThirtyEight. com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for tidy and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive. com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics at both the undergraduate and graduate levels.

GBP 66.99
1

Hands on Data Science for Biologists Using Python

Hands on Data Science for Biologists Using Python

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 consequent availability of omics data biological science has become a data-intensive field. This hands-on textbook has been written with the inception of easing data analysis by providing an interactive problem-based instructional approach in Python programming language. The book starts with an introduction to Python and steadily delves into scrupulous techniques of data handling preprocessing and visualization. The book concludes with machine learning algorithms and their applications in biological data science. Each topic has an intuitive explanation of concepts and is accompanied with biological examples. Features of this book: The book contains standard templates for data analysis using Python suitable for beginners as well as advanced learners. This book shows working implementations of data handling and machine learning algorithms using real-life biological datasets and problems such as gene expression analysis; disease prediction; image recognition; SNP association with phenotypes and diseases. Considering the importance of visualization for data interpretation especially in biological systems there is a dedicated chapter for the ease of data visualization and plotting. Every chapter is designed to be interactive and is accompanied with Jupyter notebook to prompt readers to practice in their local systems. Other avant-garde component of the book is the inclusion of a machine learning project wherein various machine learning algorithms are applied for the identification of genes associated with age-related disorders. A systematic understanding of data analysis steps has always been an important element for biological research. This book is a readily accessible resource that can be used as a handbook for data analysis as well as a platter of standard code templates for building models. | Hands on Data Science for Biologists Using Python

GBP 69.99
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Public Policy Analytics Code and Context for Data Science in Government

Geospatial Data Science Techniques and Applications

Data Driven Science for Clinically Actionable Knowledge in Diseases

Data Science and Analytics with Python

Data Science and Analytics with Python

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python such as SciKit-learn Pandas Numpy and others. The use of Python is of particular interest given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are from the point of view of the process and results obtained. Important features of Python are also covered including a Python primer. The basic elements of machine learning pattern recognition and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python clustering techniques and classification algorithms are covered in the second part of the book. Hierarchical clustering decision trees and ensemble techniques are also explored along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA IBM Data Science Studio Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London UK and a member of the School of Physics Astronomy and Mathematics at the University of Hertfordshire UK He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave also published by CRC Press. His interests include mathematical modelling data science and optimization in a wide range of applications including optics quantum mechanics data journalism and finance.

GBP 52.99
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Geographic Data Science with Python

Foundations of Statistics for Data Scientists With R and Python

Foundations of Statistics for Data Scientists With R and Python

Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar including probability distributions descriptive and inferential statistical methods and linear modeling. The book assumes knowledge of basic calculus so the presentation can focus on why it works as well as how to do it. Compared to traditional mathematical statistics textbooks however the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software with an appendix showing the same analyses with Python. Key Features: Shows the elements of statistical science that are important for students who plan to become data scientists. Includes Bayesian and regularized fitting of models (e. g. showing an example using the lasso) classification and clustering and implementing methods with modern software (R and Python). Contains nearly 500 exercises. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists such as Bayesian inference generalized linear models for non-normal responses (e. g. logistic regression and Poisson loglinear models) and regularized model fitting. The nearly 500 exercises are grouped into Data Analysis and Applications and Methods and Concepts. Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website (http://stat4ds. rwth-aachen. de/) has expanded R Python and Matlab appendices and all data sets from the examples and exercises. | Foundations of Statistics for Data Scientists With R and Python

GBP 82.99
1