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Author: Ronald Pearson
Publisher: Oxford University Press, USA
ISBN:
Category : Mathematics
Languages : en
Pages : 794
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Book Description
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.
Author: Ronald Pearson
Publisher: Oxford University Press, USA
ISBN:
Category : Mathematics
Languages : en
Pages : 794
Get Book
Book Description
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.
Author: R. Lakshmana Kumar
Publisher: CRC Press
ISBN: 1000527018
Category : Computers
Languages : en
Pages : 312
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Book Description
Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.
Author: Ronald K. Pearson
Publisher: CRC Press
ISBN: 0429847033
Category : Business & Economics
Languages : en
Pages : 548
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Book Description
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).
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309449359
Category : Medical
Languages : en
Pages : 269
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Book Description
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.
Author: Valentina Emilia Balas
Publisher: Academic Press
ISBN: 0128183195
Category : Science
Languages : en
Pages : 320
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Book Description
Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309470641
Category : Education
Languages : en
Pages : 283
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Book Description
In the United States, broad study in an array of different disciplines â€"arts, humanities, science, mathematics, engineeringâ€" as well as an in-depth study within a special area of interest, have been defining characteristics of a higher education. But over time, in-depth study in a major discipline has come to dominate the curricula at many institutions. This evolution of the curriculum has been driven, in part, by increasing specialization in the academic disciplines. There is little doubt that disciplinary specialization has helped produce many of the achievement of the past century. Researchers in all academic disciplines have been able to delve more deeply into their areas of expertise, grappling with ever more specialized and fundamental problems. Yet today, many leaders, scholars, parents, and students are asking whether higher education has moved too far from its integrative tradition towards an approach heavily rooted in disciplinary "silos". These "silos" represent what many see as an artificial separation of academic disciplines. This study reflects a growing concern that the approach to higher education that favors disciplinary specialization is poorly calibrated to the challenges and opportunities of our time. The Integration of the Humanities and Arts with Sciences, Engineering, and Medicine in Higher Education examines the evidence behind the assertion that educational programs that mutually integrate learning experiences in the humanities and arts with science, technology, engineering, mathematics, and medicine (STEMM) lead to improved educational and career outcomes for undergraduate and graduate students. It explores evidence regarding the value of integrating more STEMM curricula and labs into the academic programs of students majoring in the humanities and arts and evidence regarding the value of integrating curricula and experiences in the arts and humanities into college and university STEMM education programs.
Author: Charles R. Severance
Publisher:
ISBN: 9781530051120
Category :
Languages : en
Pages : 242
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Book Description
Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled "Python for Informatics: Exploring Information".There are free downloadable electronic copies of this book in various formats and supporting materials for the book at www.pythonlearn.com. The course materials are available to you under a Creative Commons License so you can adapt them to teach your own Python course.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309449383
Category : Medical
Languages : en
Pages : 269
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Book Description
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.
Author: John Hudson Tiner
Publisher: New Leaf Publishing Group
ISBN: 1614581517
Category : Religion
Languages : en
Pages : 161
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Book Description
From surgery to vaccines, man has made great strides in the field of medicine. Quality of life has improved dramatically in the last few decades alone, and the future is bright. But students must not forget that God provided humans with minds and resources to bring about these advances. A biblical perspective of healing and the use of medicine provides the best foundation for treating diseases and injury. In Exploring the World of Medicine, author John Hudson Tiner reveals the spectacular discoveries that started with men and women who used their abilities to better mankind and give glory to God. The fascinating history of medicine comes alive in this book, providing students with a healthy dose of facts, mini-biographies, and vintage illustrations. Includes chapter tests and index.
Author: Kun Chang Lee
Publisher: Academic Press
ISBN: 0128193158
Category : Science
Languages : en
Pages : 298
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Book Description
Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks
