Clinical Case Studies for the Family Nurse Practitioner
Clinical Case Studies for the Family Nurse Practitioner

Author: Leslie Neal-Boylan

Publisher: John Wiley & Sons

Published: 2011-11-28

Total Pages: 432

ISBN-13: 1118277856

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Clinical Case Studies for the Family Nurse Practitioner is a key resource for advanced practice nurses and graduate students seeking to test their skills in assessing, diagnosing, and managing cases in family and primary care. Composed of more than 70 cases ranging from common to unique, the book compiles years of experience from experts in the field. It is organized chronologically, presenting cases from neonatal to geriatric care in a standard approach built on the SOAP format. This includes differential diagnosis and a series of critical thinking questions ideal for self-assessment or classroom use.

Indianapolis Monthly
Indianapolis Monthly

Author:

Publisher:

Published: 2001-12

Total Pages: 216

ISBN-13:

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Indianapolis Monthly is the Circle City’s essential chronicle and guide, an indispensable authority on what’s new and what’s news. Through coverage of politics, crime, dining, style, business, sports, and arts and entertainment, each issue offers compelling narrative stories and lively, urbane coverage of Indy’s cultural landscape.

A History of Weber County
A History of Weber County

Author: Richard C. Roberts

Publisher:

Published: 1997

Total Pages: 488

ISBN-13:

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The Utah Centennial COunty History Series was funded by the Utah State Legislature under the administration of the Utah State Historical Society in cooperation with Utah's twenty-nine county governments.

Introduction to Data Science
Introduction to Data Science

Author: Laura Igual

Publisher: Springer

Published: 2017-02-22

Total Pages: 227

ISBN-13: 3319500171

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This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.