Exploring Data in Engineering, the Sciences, and Medicine

by Ronald Pearson

Author	: Ronald Pearson
Publisher	: Oxford University Press
Total Pages	: 794
Release	: 2011-02-03
Genre	: Mathematics
ISBN	:

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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

by Ronald K. Pearson

Author	: Ronald K. Pearson
Publisher	: CRC Press
Total Pages	: 563
Release	: 2018-05-04
Genre	: Business & Economics
ISBN	: 0429847041

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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).

Reproducibility and Replicability in Science

by National Academies of Sciences, Engineering, and Medicine

Author	: National Academies of Sciences, Engineering, and Medicine
Publisher	: National Academies Press
Total Pages	: 257
Release	: 2019-10-20
Genre	: Science
ISBN	: 0309486165

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One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.

Exploring Data and Metrics of Value at the Intersection of Health Care and Transportation

by National Academies of Sciences, Engineering, and Medicine

Author	: National Academies of Sciences, Engineering, and Medicine
Publisher	: National Academies Press
Total Pages	: 269
Release	: 2016-11-28
Genre	: Medical
ISBN	: 0309449383

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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.

Exploring the History of Medicine

by John Hudson Tiner

Author	: John Hudson Tiner
Publisher	: New Leaf Publishing Group
Total Pages	: 161
Release	: 1999-04-01
Genre	: Religion
ISBN	: 1614581517

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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.

Exploratory Data Analytics for Healthcare

by R. Lakshmana Kumar

Author	: R. Lakshmana Kumar
Publisher	: CRC Press
Total Pages	: 307
Release	: 2021-12-23
Genre	: Computers
ISBN	: 1000527018

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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.

Python for Everybody

by Charles R. Severance

Author	: Charles R. Severance
Publisher	:
Total Pages	: 242
Release	: 2016-04-09
Genre	:
ISBN	: 9781530051120

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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.

Data Matters

by National Academies of Sciences, Engineering, and Medicine

Author	: National Academies of Sciences, Engineering, and Medicine
Publisher	: National Academies Press
Total Pages	: 103
Release	: 2019-01-28
Genre	: Science
ISBN	: 030948247X

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In an increasingly interconnected world, perhaps it should come as no surprise that international collaboration in science and technology research is growing at a remarkable rate. As science and technology capabilities grow around the world, U.S.-based organizations are finding that international collaborations and partnerships provide unique opportunities to enhance research and training. International research agreements can serve many purposes, but data are always involved in these collaborations. The kinds of data in play within international research agreements varies widely and may range from financial and consumer data, to Earth and space data, to population behavior and health data, to specific project-generated dataâ€"this is just a narrow set of examples of research data but illustrates the breadth of possibilities. The uses of these data are various and require accounting for the effects of data access, use, and sharing on many different parties. Cultural, legal, policy, and technical concerns are also important determinants of what can be done in the realms of maintaining privacy, confidentiality, and security, and ethics is a lens through which the issues of data, data sharing, and research agreements can be viewed as well. A workshop held on March 14-16, 2018, in Washington, DC explored the changing opportunities and risks of data management and use across disciplinary domains. The third workshop in a series, participants gathered to examine advisory principles for consideration when developing international research agreements, in the pursuit of highlighting promising practices for sustaining and enabling international research collaborations at the highest ethical level possible. The intent of the workshop was to explore, through an ethical lens, the changing opportunities and risks associated with data management and use across disciplinary domainsâ€"all within the context of international research agreements. This publication summarizes the presentations and discussions from the workshop.

Data Mining and Exploration

by Chong Ho Alex Yu

Author	: Chong Ho Alex Yu
Publisher	: CRC Press
Total Pages	: 290
Release	: 2022-10-27
Genre	: Business & Economics
ISBN	: 1000777790

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This book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.