Books in Data

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Examen clinique et sémiologie - Macleod

1st Edition
April 3, 2019
J. Alastair Innes + 3 more
French
eBook
9 7 8 - 2 - 2 9 4 - 7 5 9 3 6 - 9

L’examen clinique est essentiel à l’identification d’une maladie et l’évaluation de son pronostic.Cet ouvrage de référence dans le monde médical anglo-saxon explique avec détail comment évaluer les symptômes d’un patient et établir un diagnostic au travers de l’anamnèse et de l’examen physique.Son objectif est d’aider l’étudiant à développer les compétences cliniques nécessaires à l’obtention d’une anamnèse claire et les compétences pratiques indispensables à la détection et l’interprétation des signes cliniques des maladies.Le livre est composé de quatre grandes parties :Anamnèse et examen physique : elle aborde les principes généraux d’une bonne interaction avec le patient, depuis les bases du recueil de l’anamnèse et de la réalisation d’un examen physique à l’utilisation de tableaux cliniques permettant des diagnostics.Sémiologie par appareil : cette partie traite des symptômes et des signes selon une approche par système.Situations spécifiques : elle illustre l’application de ces compétences à des situations cliniques particulières.Mise en pratique : cette partie s’intéresse à l’application de ces compétences à la pratique quotidienne et propose des conseils pour s’y préparer efficacement.Il est également proposé dans chaque chapitre des exemples d’Examen Clinique Objectif Structuré correspondant à une méthode d’évaluation des étudiants très prisée dans les pays anglo-saxons. Ils consistent en une mise en situation pratique face à un mannequin ou un comédien jouant un scénario clinique court.Ce livre aborde également les différentes étapes de chaque examen qui sont présentées de manière détaillée, étayées de nombreux encadrés et illustrations. Il est particulièrement adapté aux étudiants en DFGSM2-DFGSM3 dans le cadre de l’enseignement thématique « Sémiologie générale » et les enseignements intégrés par système (sémiologie clinique) mais également aux étudiants en pharmacie. Il intéressera également les étudiants en DFASM préparant les ECNi mais aussi les enseignants et praticiens désireux de mettre à jour leurs connaissances.

Meta-Analytics

1st Edition
March 10, 2019
Steven Simske
English
Paperback
9 7 8 - 0 - 1 2 - 8 1 4 6 2 3 - 1
eBook
9 7 8 - 0 - 1 2 - 8 1 4 6 2 4 - 8

Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is ‘meta’ to analytics, covering general analytics in sufficient detail for readers to engage with, and understand, hybrid or meta- approaches. The book has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance. Inn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts.

Rodak's Hematology

6th Edition
February 22, 2019
Elaine M. Keohane + 2 more
English
eBook
9 7 8 - 0 - 3 2 3 - 5 4 9 6 3 - 9

**Selected for Doody’s Core Titles® 2024 in Laboratory Technology**Make sure you are thoroughly prepared to work in a clinical lab. Rodak’s Hematology: Clinical Principles and Applications, 6th Edition uses hundreds of full-color photomicrographs to help you understand the essentials of hematology. This new edition shows how to accurately identify cells, simplifies hemostasis and thrombosis concepts, and covers normal hematopoiesis through diseases of erythroid, myeloid, lymphoid, and megakaryocytic origins. Easy to follow and understand, this book also covers key topics including: working in a hematology lab; complementary testing areas such as flow cytometry, cytogenetics, and molecular diagnostics; the parts and functions of the cell; and laboratory testing of blood cells and body fluid cells.

Data Science

2nd Edition
November 27, 2018
Vijay Kotu + 1 more
English
Paperback
9 7 8 - 0 - 1 2 - 8 1 4 7 6 1 - 0
eBook
9 7 8 - 0 - 1 2 - 8 1 4 7 6 2 - 7

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...

Principles and Practice of Big Data

2nd Edition
July 23, 2018
Jules J. Berman
English
Paperback
9 7 8 - 0 - 1 2 - 8 1 5 6 0 9 - 4
eBook
9 7 8 - 0 - 1 2 - 8 1 5 6 1 0 - 0

Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book can be implemented in a few lines of code using just about any popular programming language (Python snippets are provided). Through the use of new multiple examples, this edition demonstrates that if we understand our data, and if we know how to ask the right questions, we can learn a great deal from large and complex data collections. The book will assist students and professionals from all scientific backgrounds who are interested in stepping outside the traditional boundaries of their chosen academic disciplines.