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Practical Healthcare Statistics With Examples In Python And R

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Practical Healthcare Statistics With Examples In Python And R

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Author : Michael Korvink
language : en
Publisher: CRC Press
Release Date : 2025-08-13

Practical Healthcare Statistics With Examples In Python And R written by Michael Korvink and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-13 with Mathematics categories.

Practical Healthcare Statistics with Examples in Python and R provides a clear and straightforward introduction to statistical methods in healthcare. Designed for recent graduates, new analysts, and professionals transitioning into healthcare analytics, it offers practical guidance on tackling real-world problems using statistical concepts and programming. The book is divided into three primary sections. The first section provides an introduction to healthcare data and measures. In these chapters, readers will learn about the nuances of administrative claims and electronic health records, as well as common industry measures related to quality and efficiency of care. The second section will cover foundational techniques, such as hypothesis testing and regression analysis, as well as more advanced approaches, like generalized additive models and hierarchical models. In the last section, readers will be introduced to epidemiological techniques such as direct and indirect standardization, measures of disease frequency and association, and time-to-event analysis. The book emphasizes interpretable methods that are both effective and easy to communicate to clinical and non-technical stakeholders. Each technique presented in the book is accompanied by statistical notation described in plain English, as well as a self-contained example implemented in both Python and R. These examples help readers connect statistical methods to real healthcare scenarios without requiring extensive programming experience. By working through these examples, readers will build technical skills and a practical understanding of how to analyze healthcare data. These methods are not only central to improving patient care but are also adaptable to other areas within and beyond healthcare. This book is a practical resource for analysts, data scientists, health researchers, and others looking to make informed, data-driven decisions in healthcare.

Practical Data Science With R

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Author : Nina Zumel
language : en
Publisher: Manning Publications
Release Date : 2014-04-10

Practical Data Science With R written by Nina Zumel and has been published by Manning Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-10 with Computers categories.

Summary Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics. Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels. This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed. What's Inside Data science for the business professional Statistical analysis using the R language Project lifecycle, from planning to delivery Numerous instantly familiar use cases Keys to effective data presentations About the Authors Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com. Table of Contents PART 1 INTRODUCTION TO DATA SCIENCE The data science process Loading data into R Exploring data Managing data PART 2 MODELING METHODS Choosing and evaluating models Memorization methods Linear and logistic regression Unsupervised methods Exploring advanced methods PART 3 DELIVERING RESULTS Documentation and deployment Producing effective presentations

Introduction To Statistical Computing And Visualization Using R

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Author : Megha Rathi
language : en
Publisher: CRC Press
Release Date : 2025-05-01

Introduction To Statistical Computing And Visualization Using R written by Megha Rathi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-01 with Computers categories.

The book provides a foundational guide to statistical computing and visualisation Using R programming with an emphasis on practical data analysis skills that are directly applicable to diverse fields like finance, defence, health, and education. It uniquely combines a thorough explanation of basic constructs with advanced topics such as data visualisation, statistical modeling, and probability, making it accessible yet comprehensive for learners across disciplines. This approach allows readers not only to build essential R skills but also to apply them to real-world scenarios, equipping students and professionals from various disciplines with versatile analytical tools. It offers a comprehensive yet approachable introduction for students and scholars from various disciplines using R. Includes practical and interactive elements such as quizzes, coding exercises, and hands-on projects can provide an engaging and effective learning experience for readers Provides complete code solutions to every problem presented, including detailed answers to even the most complex questions Presents case studies that can help contextualize the concepts covered in the book by showing how they are used in specific industries, fields, or contexts Offers application-based practical data analysis with cases in various fields and sectors, such as finance, healthcare, and marketing Focuses on best practices and efficient coding techniques, improving productivity and maintainability of R code

The Transformative Power Of Ai Applications In Healthcare

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Author : Ayman Elmassarawy
language : en
Publisher: Ayman Elmassarawy
Release Date : 2025-01-24

The Transformative Power Of Ai Applications In Healthcare written by Ayman Elmassarawy and has been published by Ayman Elmassarawy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-24 with Computers categories.

Artificial intelligence (AI) is revolutionizing the healthcare industry, reshaping how medicine is practiced and how care is delivered. The Transformative Power of AI Applications in Healthcare delves into the dynamic intersection of AI and medicine, offering readers a profound understanding of the innovations driving change and the challenges that accompany them. With its compelling blend of technological insight and real-world applications, this book serves as an essential resource for medical professionals, technologists, policymakers, and curious minds. Key Themes and Innovations The book begins by laying the foundational concepts of AI in healthcare, tracing its evolution from early data visualization techniques to today’s advanced machine learning algorithms. Readers are introduced to the transformative potential of predictive analytics, where wearable devices, early warning systems, and disease modeling enable the detection and prevention of illnesses long before symptoms manifest. Revolutionizing Diagnostics: One of the standout themes is AI’s role in enhancing diagnostic accuracy and efficiency. Chapters illustrate how deep learning algorithms, such as those used in radiology and pathology, are empowering clinicians with faster, more reliable diagnoses. Case studies, including AI’s application in detecting diabetic retinopathy and breast cancer, reveal its ability to expand diagnostic capabilities, particularly in underserved regions. Personalized Medicine and Genomics: The exploration of genomics and pharmacogenomics highlights how AI is personalizing care to unprecedented levels. The book unpacks the shift from generic treatment protocols to precision therapies tailored to individual genetic profiles, citing examples like targeted cancer treatments and optimized drug dosing strategies. Surgical Innovations and Robotics: AI’s integration into robotic surgery systems is revolutionizing operating rooms worldwide. From minimally invasive procedures to real-time decision-making tools, readers learn how AI enhances surgical precision, reduces risks, and expands access to complex surgeries. The potential for remote surgeries and autonomous systems is also explored, showcasing the far-reaching impact of this technology. Public Health and Crisis Management: The role of AI in addressing global health crises takes center stage, with detailed discussions on how platforms like BlueDot and HealthMap provided early warnings during the COVID-19 pandemic. The book also examines AI’s contributions to vaccine development, resource allocation, and public health strategies, highlighting its transformative role in safeguarding global populations. Ethical Considerations and Challenges The book does not shy away from critical discussions on the ethical implications of AI. Topics such as algorithmic bias, data privacy, and equitable access to AI-driven innovations are examined with rigor. Readers are invited to consider how these challenges can be addressed through transparent, inclusive practices that prioritize fairness and human welfare. Vision for the Future Looking ahead, the book speculates on emerging frontiers of AI in healthcare. From mental health applications and autonomous diagnostics to advancements in bioprinting and chronic disease management, the potential for future breakthroughs is boundless. The narrative challenges readers to envision a healthcare system where AI seamlessly integrates with human expertise to deliver smarter, more compassionate care. Why This Book Matters At its core, The Transformative Power of AI Applications in Healthcare is not just about technology—it’s about the human impact. By offering an in-depth look at the advancements and ethical dilemmas surrounding AI, the book equips readers with the knowledge and inspiration to participate in shaping the future of medicine. It is both a roadmap and a call to action, urging readers to engage with the transformative power of AI and to champion its responsible implementation. This book is an indispensable guide for anyone interested in understanding how AI is reshaping healthcare. It illuminates the path forward, bridging the gap between innovation and humanity, and fostering a vision of a healthcare system that is smarter, more equitable, and profoundly impactful.

Practical Time Series Analysis

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Author : Aileen Nielsen
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-09-20

Practical Time Series Analysis written by Aileen Nielsen and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-20 with Computers categories.

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance

Data Mining Exam Preparation

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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date : 2024-10-26

Data Mining Exam Preparation written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-26 with Study Aids categories.

Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com

Data Science And Analytics With Python R And Spss Programming

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Author : V.K. Jain
language : en
Publisher: KHANNA PUBLISHING HOUSE
Release Date :

Data Science And Analytics With Python R And Spss Programming written by V.K. Jain and has been published by KHANNA PUBLISHING HOUSE this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

The Book has been written completely as per AICTE recommended syllabus on "Data Sciences". SALIENT FEATURES OF THE BOOK: Explains how data is collected, managed and stored for data science. With complete courseware for understand the key concepts in data science including their real-world applications and the toolkit used by data scientists. Implement data collection and management. Provided with state of the arts subjectwise. With all required tutorials on R, Python and Bokeh, Anaconda, IBM SPSS-21 and Matplotlib.

Practical Predictive Analytics

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Author : Ralph Winters
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-06-30

Practical Predictive Analytics written by Ralph Winters and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-30 with Computers categories.

Make sense of your data and predict the unpredictable About This Book A unique book that centers around develop six key practical skills needed to develop and implement predictive analytics Apply the principles and techniques of predictive analytics to effectively interpret big data Solve real-world analytical problems with the help of practical case studies and real-world scenarios taken from the world of healthcare, marketing, and other business domains Who This Book Is For This book is for those with a mathematical/statistics background who wish to understand the concepts, techniques, and implementation of predictive analytics to resolve complex analytical issues. Basic familiarity with a programming language of R is expected. What You Will Learn Master the core predictive analytics algorithm which are used today in business Learn to implement the six steps for a successful analytics project Classify the right algorithm for your requirements Use and apply predictive analytics to research problems in healthcare Implement predictive analytics to retain and acquire your customers Use text mining to understand unstructured data Develop models on your own PC or in Spark/Hadoop environments Implement predictive analytics products for customers In Detail This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data. Style and Approach This book takes a practical hands-on approach wherein the algorithms will be explained with the help of real-world use cases. It is written in a well-researched academic style which is a great mix of theoretical and practical information. Code examples are supplied for both theoretical concepts as well as for the case studies. Key references and summaries will be provided at the end of each chapter so that you can explore those topics on their own.

The Shape Of Data

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Author : Colleen M. Farrelly
language : en
Publisher: No Starch Press
Release Date : 2023-09-12

The Shape Of Data written by Colleen M. Farrelly and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-12 with Computers categories.

This advanced machine learning book highlights many algorithms from a geometric perspective and introduces tools in network science, metric geometry, and topological data analysis through practical application. Whether you’re a mathematician, seasoned data scientist, or marketing professional, you’ll find The Shape of Data to be the perfect introduction to the critical interplay between the geometry of data structures and machine learning. This book’s extensive collection of case studies (drawn from medicine, education, sociology, linguistics, and more) and gentle explanations of the math behind dozens of algorithms provide a comprehensive yet accessible look at how geometry shapes the algorithms that drive data analysis. In addition to gaining a deeper understanding of how to implement geometry-based algorithms with code, you’ll explore: Supervised and unsupervised learning algorithms and their application to network data analysis The way distance metrics and dimensionality reduction impact machine learning How to visualize, embed, and analyze survey and text data with topology-based algorithms New approaches to computational solutions, including distributed computing and quantum algorithms

Data Science Exam Preparation

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Author : cybellim
language : en
Publisher: Cybellium Ltd
Release Date : 2024-10-26

Data Science Exam Preparation written by cybellim and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-26 with Study Aids categories.

Practical Healthcare Statistics With Examples In Python And R

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