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Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools. This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data. Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user. Discusses data simplification principles, methods, and tools that must be studied and mastered Provides open source tools, free utilities, and snippets of code that can be reused and repurposed to simplify data Explains how to best utilize indexes to search, retrieve, and analyze textual data Shows the data scientist how to apply ontologies, classifications, classes, properties, and instances to data using tried and true methods

Database management. --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Electronic data processing

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Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain. Most of the data stored worldwide is legacy data-data created some time in the past, for a particular purpose, and left in obsolete formats. As with keepsakes in an attic, we retain this information thinking it may have value in the future, though we have no current use for it. The case studies in this book, from such diverse fields as cosmology, quantum physics, high-energy physics, microbiology,

File organization (Computer science) --- Database management. --- Information storage and retrieval systems. --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Computer systems --- Electronic information resources --- Data libraries --- Digital libraries --- Information organization --- Information retrieval --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Electronic data processing --- File management (Computer science) --- File systems (Computer science) --- Organization, File (Computer science) --- Database management --- Data centers

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"Evolution's Clinical Guidebook: Translating Ancient Genes into Precision Medicine demonstrates, through well-documented examples, how an understanding of the phylogenetic ancestry of humans allows us to make sense out of the flood of genetic data streaming from modern laboratories and how it can lead us to new ways to prevent, diagnose and treat diseases. Topics cover evolution and human genome, meiosis and other recombinants events, embryology, speciation, phylogeny, rare and common diseases, and the evolution of aging. This book is a valuable source for bioinformaticians and those in the biomedical field who need knowledge, down to gene level, to fully comprehend currently available data." --Amazon.ca.

Genetics --- Data processing. --- Personalized medicine. --- Bioinformatics. --- Genomics. --- Genetic Phenomena. --- Biological Evolution. --- Precision Medicine. --- P Health --- P-Health --- Personalized Medicine --- Individualized Medicine --- Medicine, Individualized --- Medicine, Personalized --- Medicine, Precision --- P-Healths --- Pharmacogenomic Variants --- Pharmacogenetics --- Patient-Specific Modeling --- Evolution, Biological --- Sociobiology --- Genetic Concepts --- Genetic Phenomenon --- Genetic Process --- Genetic Processes --- Concept, Genetic --- Concepts, Genetic --- Genetic Concept --- Phenomena, Genetic --- Phenomenon, Genetic --- Process, Genetic --- Processes, Genetic --- Molecular Biology --- Genome research --- Genomes --- Molecular genetics --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Individualized medicine --- Medical care --- Embryology --- Mendel's law --- Adaptation (Biology) --- Breeding --- Chromosomes --- Heredity --- Mutation (Biology) --- Variation (Biology) --- Research --- Data processing --- Theranostics --- Predictive Medicine --- Medicine, Predictive --- Theranostic --- Precision medicine. --- Personalized medicine

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Biology --- Philosophy. --- Vitalism

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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. Presents new methodologies that are widely applicable to just about any project involving large and complex datasets Offers readers informative new case studies across a range scientific and engineering disciplines Provides insights into semantics, identification, de-identification, vulnerabilities and regulatory/legal issues Utilizes a combination of pseudocode and very short snippets of Python code to show readers how they may develop their own projects without downloading or learning new software

Big data. --- Data sets, Large --- Large data sets --- Data sets