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Probability and Statistics for Data Science Math + R + Data

Probability and Statistics for Data Science Math + R + Data

Probability and Statistics for Data Science: Math + R + Data covers math stat—distributions expected value estimation etc. —but takes the phrase Data Science in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications such as PCA mixture distributions random graph models Hidden Markov models linear and logistic regression and neural networks. * Leads the student to think critically about the how and why of statistics and to see the big picture. * Not theorem/proof-oriented but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus some matrix algebra and some experience in programming. Norman Matloff is a professor of computer science at the University of California Davis and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award. | Probability and Statistics for Data Science Math + R + Data

GBP 56.99
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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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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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Modern Data Science with R

Modern Data Science with R

From a review of the first edition: Modern Data Science with R… is rich with examples and is guided by a strong narrative voice. What’s more it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf purrr tidymodels and tidytext is now integrated into the text. All chapters have been revised and several have been split re-organized or re-imagined to meet the shifting landscape of best practice.

GBP 82.99
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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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Basketball Data Science With Applications in R

Basketball Data Science With Applications in R

Using data from one season of NBA games Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player's shots or doing an analysis of the impact of high pressure game situations on the probability of scoring this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge Basketball Data Science with R is suitable for students technicians coaches data analysts and applied researchers. Features: One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball Presents tools for modelling graphs and figures to visualize the data Includes real world case studies and examples such as estimations of scoring probability using the Golden State Warriors as a test case Provides the source code and data so readers can do their own analyses on NBA teams and players | Basketball Data Science With Applications in R

GBP 48.99
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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

Data Science for Water Utilities Data as a Source of Value

This addition to the Data Science Series introduces the principles of data science and the R language to the singular needs of water professionals. The book provides unique data and examples relevant to managing water utility and is sourced from the author’s extensive experience. Data Science for Water Utilities: Data as a Source of Value is an applied practical guide that shows water professionals how to use data science to solve urban water management problems. Content develops through four case studies. The first looks at analysing water quality to ensure public health. The second considers customer feedback. The third case study introduces smart meter data. The guide flows easily from basic principles through code that with each case study increases in complexity. The last case study analyses data using basic machine learning. Readers will be familiar with analysing data but do not need coding experience to use this book. The title will be essential reading for anyone seeking a practical introduction to data science and creating value with R. | Data Science for Water Utilities Data as a Source of Value

GBP 44.99
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Data Science in Practice

Data Science in Practice

Data Science in Practice is the ideal introduction to data science. With or without math skills here you get the all-round view that you need for your projects. This book describes how to properly question data in order to unearth the treasure that data can be. You will get to know the relevant analysis methods and will be introduced to the programming language R which is ideally suited for data analysis. Associated tools like notebooks that make data science programming easily accessible are included in this introduction. Because technology alone is not enough this book also deals with problems in project implementation illuminates various fields of application and does not forget to address ethical aspects. Data Science in Practice includes many examples notes on errors decision-making aids and other practical tips. This book is ideal as a complementary text for university students and is a useful learning tool for those moving into more data-related roles. Key Features: Success factors and tools for all project phases Includes application examples for various subject areas Introduces many aspects of Data Science from requirements analysis to data acquisition and visualization

GBP 54.99
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Data Science A First Introduction

Data Science A First Introduction

Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning create effective visualizations and extract insights from data using classification regression clustering and inference. The text emphasizes workflows that are clear reproducible and shareable and includes coverage of the basics of version control. All source code is available online demonstrating the use of good reproducible project workflows. Based on educational research and active learning principles the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive self-directed learning. The book will leave readers well-prepared for data science projects. The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course. | Data Science A First Introduction

GBP 48.99
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Data Science for Wind Energy

Data Science for Wind Energy

Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications near-ground wind field analysis and forecast turbine power curve fitting and performance analysis turbine reliability assessment and maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered including time series models spatio-temporal analysis kernel regression decision trees kNN splines Bayesian inference and importance sampling. More importantly the data science methods are described in the context of wind energy applications with specific wind energy examples and case studies. Please also visit the author’s book site at https://aml. engr. tamu. edu/book-dswe. FeaturesProvides an integral treatment of data science methods and wind energy applicationsIncludes specific demonstration of particular data science methods and their use in the context of addressing wind energy needsPresents real data case studies and computer codes from wind energy research and industrial practiceCovers material based on the author's ten plus years of academic research and insights

GBP 44.99
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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

Big Data for Regional Science

Recent technological advancements and other related factors and trends are contributing to the production of an astoundingly large and rapidly accelerating collection of data or ‘Big Data’. This data now allows us to examine urban and regional phenomena in ways that were previously not possible. Despite the tremendous potential of big data for regional science its use and application in this context is fraught with issues and challenges. This book brings together leading contributors to present an interdisciplinary agenda-setting and action-oriented platform for research and practice in the urban and regional community. This book provides a comprehensive multidisciplinary and cutting-edge perspective on big data for regional science. Chapters contain a collection of research notes contributed by experts from all over the world with a wide array of disciplinary backgrounds. The content is organized along four themes: sources of big data; integration processing and management of big data; analytics for big data; and higher level policy and programmatic considerations. As well as concisely and comprehensively synthesising work done to date the book also considers future challenges and prospects for the use of big data in regional science. Big Data for Regional Science provides a seminal contribution to the field of regional science and will appeal to a broad audience including those at all levels of academia industry and government.

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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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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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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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
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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

Public Policy Analytics Code and Context for Data Science in Government

Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why ultimately data science and ‘Planning’ are one and the same. A graduate-level introduction to data science this book will appeal to researchers and data scientists at the intersection of data analytics and public policy as well as readers who wish to understand how algorithms will affect the future of government. | Public Policy Analytics Code and Context for Data Science in Government

GBP 44.99
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Geospatial Data Science Techniques and Applications

Geospatial Data Science Techniques and Applications

Data science has recently gained much attention for a number of reasons and among them is Big Data. Scientists (from almost all disciplines including physics chemistry biology sociology among others) and engineers (from all fields including civil environmental chemical mechanical among others) are faced with challenges posed by data volume variety and velocity or Big Data. This book is designed to highlight the unique characteristics of geospatial data demonstrate the need to different approaches and techniques for obtaining new knowledge from raw geospatial data and present select state-of-the-art geospatial data science techniques and how they are applied to various geoscience problems. | Geospatial Data Science Techniques and Applications

GBP 44.99
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Data Driven Science for Clinically Actionable Knowledge in Diseases

Data Driven Science for Clinically Actionable Knowledge in Diseases

Data-driven science has become a major decision-making aid for the diagnosis and treatment of disease. Computational and visual analytics enables effective exploration and sense making of large and complex data through the deployment of appropriate data science methods meaningful visualisation and human-information interaction. This edited volume covers state-of-the-art theory method models design evaluation and applications in computational and visual analytics in desktop mobile and immersive environments for analysing biomedical and health data. The book is focused on data-driven integral analysis including computational methods and visual analytics practices and solutions for discovering actionable knowledge in support of clinical actions in real environments. By studying how data and visual analytics have been implemented into the healthcare domain the book demonstrates how analytics influences the domain through improving decision making specifying diagnostics selecting the best treatments and generating clinical certainty. | Data Driven Science for Clinically Actionable Knowledge in Diseases

GBP 44.99
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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

Geographic Data Science with Python

This book provides the tools the methods and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive large frequent and rapid data there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis by using geographical and computational reasoning it shows the reader how to unlock new insights hidden within data. Key Features: ● Showcases the excellent data science environment in Python. ● Provides examples for readers to replicate adapt extend and improve. ● Covers the crucial knowledge needed by geographic data scientists. It presents concepts in a far more geographic way than competing textbooks covering spatial data mapping and spatial statistics whilst covering concepts such as clusters and outliers as geographic concepts. Intended for data scientists GIScientists and geographers the material provided in this book is of interest due to the manner in which it presents geospatial data methods tools and practices in this new field.

GBP 48.99
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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