Exploring Data In Engineering The Sciences And Medicine

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Exploring Data In Engineering The Sciences And Medicine

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Author : Ronald Pearson
language : en
Publisher: Oxford University Press
Release Date : 2011-02-03

Exploring Data In Engineering The Sciences And Medicine written by Ronald Pearson and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-03 with Mathematics categories.

This book introduces various widely available exploratory data analysis methods, emphasizing those that are most useful in the preliminary exploration of large datasets involving mixed data types. Topics include descriptive statistics, graphical analysis tools, regression modeling and spectrum estimation, along with practical issues like outliers, missing data, and variable selection.

Exploratory Data Analysis Using R

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Author : Ronald K. Pearson
language : en
Publisher: CRC Press
Release Date : 2018-05-04

Exploratory Data Analysis Using R written by Ronald K. Pearson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-04 with Business & Economics categories.

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).

Exploratory Data Analysis Using R

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Author : Ronald K. Pearson
language : en
Publisher: CRC Press
Release Date : 2018-05-04

Exploratory Data Analysis Using R written by Ronald K. Pearson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-04 with Business & Economics categories.

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).

Secondary Analysis Of Electronic Health Records

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Author : MIT Critical Data
language : en
Publisher: Springer
Release Date : 2016-09-09

Secondary Analysis Of Electronic Health Records written by MIT Critical Data and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-09 with Medical categories.

This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.

Data Driven Science And Engineering

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Author : Steven L. Brunton
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-05

Data Driven Science And Engineering written by Steven L. Brunton and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-05 with Computers categories.

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Exploratory Data Science A Practical Guide For Engineering And Science Students

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Author : Pasquale De Marco
language : en
Publisher: Pasquale De Marco
Release Date : 2025-03-17

Exploratory Data Science A Practical Guide For Engineering And Science Students written by Pasquale De Marco and has been published by Pasquale De Marco this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-17 with Science categories.

"Exploratory Data Science: A Practical Guide for Engineering and Science Students" is a comprehensive and engaging introduction to the world of data analysis, probability, and statistics. Designed for students in engineering, science, and related fields, this book provides a solid foundation in the essential concepts and techniques needed to extract meaningful insights from data. With a focus on practical applications and real-world examples, "Exploratory Data Science" takes readers on a journey through the art of data exploration, visualization, and statistical inference. Learn how to uncover patterns, trends, and relationships hidden within data using graphical representations, numerical summaries, and probability distributions. Master the art of hypothesis testing to make informed decisions in the face of uncertainty. Delve into the intricacies of correlation and regression analysis to uncover relationships between variables and make predictions based on observed patterns. Explore time series analysis and forecasting to unravel patterns and trends over time. Discover the power of design of experiments and analysis of variance to optimize processes and make informed decisions. Non-parametric statistics and Bayesian statistics are also covered, providing readers with the tools to analyze data that may not conform to traditional assumptions and to incorporate prior knowledge and uncertainty into statistical models. The book concludes with a thorough exploration of statistical computing and software, equipping readers with the practical skills necessary to harness the computational power of modern statistical software packages. Written in a clear and accessible style, "Exploratory Data Science" is packed with hands-on exercises, case studies, and thought-provoking questions to reinforce understanding and encourage critical thinking. Ethical considerations in data analysis are also emphasized, ensuring that statistical methods are applied responsibly and with integrity. Whether you are a student seeking a deeper understanding of data science or a professional looking to enhance your analytical skills, "Exploratory Data Science" is the ultimate guide to unlocking the transformative potential of data. Embark on a journey of discovery and empower yourself to make informed decisions, solve complex problems, and drive innovation in your field. If you like this book, write a review!

Chemoinformatics For Drug Discovery

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Author : Jürgen Bajorath
language : en
Publisher: John Wiley & Sons
Release Date : 2013-11-18

Chemoinformatics For Drug Discovery written by Jürgen Bajorath and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-18 with Science categories.

Chemoinformatics strategies to improve drug discovery results With contributions from leading researchers in academia and the pharmaceutical industry as well as experts from the software industry, this book explains how chemoinformatics enhances drug discovery and pharmaceutical research efforts, describing what works and what doesn't. Strong emphasis is put on tested and proven practical applications, with plenty of case studies detailing the development and implementation of chemoinformatics methods to support successful drug discovery efforts. Many of these case studies depict groundbreaking collaborations between academia and the pharmaceutical industry. Chemoinformatics for Drug Discovery is logically organized, offering readers a solid base in methods and models and advancing to drug discovery applications and the design of chemoinformatics infrastructures. The book features 15 chapters, including: What are our models really telling us? A practical tutorial on avoiding common mistakes when building predictive models Exploration of structure-activity relationships and transfer of key elements in lead optimization Collaborations between academia and pharma Applications of chemoinformatics in pharmaceutical research experiences at large international pharmaceutical companies Lessons learned from 30 years of developing successful integrated chemoinformatic systems Throughout the book, the authors present chemoinformatics strategies and methods that have been proven to work in pharmaceutical research, offering insights culled from their own investigations. Each chapter is extensively referenced with citations to original research reports and reviews. Integrating chemistry, computer science, and drug discovery, Chemoinformatics for Drug Discovery encapsulates the field as it stands today and opens the door to further advances.

Nonlinear Digital Filtering With Python

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Author : Ronald K. Pearson
language : en
Publisher: CRC Press
Release Date : 2018-09-03

Nonlinear Digital Filtering With Python written by Ronald K. Pearson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Medical categories.

Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Adopting both structural and behavioral approaches in characterizing and designing nonlinear digital filters, this book: Begins with an expedient introduction to programming in the free, open-source computing environment of Python Uses results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classes Analyzes the impact of a range of useful interconnection strategies on filter behavior, providing Python implementations of the presented filters and interconnection strategies Proposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these components Illustrates the behavioral consequences of allowing recursive (i.e., feedback) interconnections in nonlinear digital filters while highlighting a challenging but promising research frontier Nonlinear Digital Filtering with Python: An Introduction supplies essential knowledge useful for developing and implementing data cleaning filters for dynamic data analysis and time-series modeling.

Exploring Data And Metrics Of Value At The Intersection Of Health Care And Transportation

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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2016-11-28

Exploring Data And Metrics Of Value At The Intersection Of Health Care And Transportation written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-28 with Medical categories.

Evidence from the public health sector demonstrates that health care is only one of the determinants of health, which also include genes, behavior, social factors, and the built environment. These contextual elements are key to understanding why health care organizations are motivated to focus beyond their walls and to consider and respond in unprecedented ways to the social needs of patients, including transportation needs. In June 2016 the National Academies of Sciences, Engineering, and Medicine held a joint workshop to explore partnerships, data, and measurement at the intersection of the health care and transportation sectors. This publication summarizes the presentations and discussions from the workshop.

Handbook Of Statistical Analysis And Data Mining Applications

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Author : Ken Yale
language : en
Publisher: Elsevier
Release Date : 2017-11-09

Handbook Of Statistical Analysis And Data Mining Applications written by Ken Yale and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-09 with Mathematics categories.

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications