TY - BOOK UR - http://lib.ugent.be/catalog/ebk01:4100000005958190 ID - ebk01:4100000005958190 ET - 1st ed. 2018. LA - eng TI - Data Science and Predictive Analytics Biomedical and Health Applications using R PY - 2018 SN - 9783319723471 AU - Dinov, Ivo D. author. (role)aut (role)http://id.loc.gov/vocabulary/relators/aut AB - 1 Introduction -- 2 Foundations of R -- 3 Managing Data in R -- 4 Data Visualization -- 5 Linear Algebra & Matrix Computing -- 6 Dimensionality Reduction -- 7 Lazy Learning: Classification Using Nearest Neighbors -- 8 Probabilistic Learning: Classification Using Naive Bayes -- 9 Decision Tree Divide and Conquer Classification -- 10 Forecasting Numeric Data Using Regression Models -- 11 Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines -- 12 Apriori Association Rules Learning -- 13 k-Means Clustering -- 14 Model Performance Assessment -- 15 Improving Model Performance -- 16 Specialized Machine Learning Topics -- 17 Variable/Feature Selection -- 18 Regularized Linear Modeling and Controlled Variable Selection -- 19 Big Longitudinal Data Analysis -- 20 Natural Language Processing/Text Mining -- 21 Prediction and Internal Statistical Cross Validation -- 22 Function Optimization -- 23 Deep Learning Neural Networks -- 24 Summary -- 25 Glossary -- 26 Index -- 27 Errata. AB - Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall. First, the volume of data is increasing much faster than the corresponding rise of our computational processing power (Kryder’s law > Moore’s law). Second, traditional discipline-bounds inhibit expeditious progress. Third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. There are very few rigorous instructional resources, interactive learning materials, and dynamic training environments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pillars for the urgently needed bridge to close that supply and demand predictive analytic skills gap. Exposing the enormous opportunities presented by the tsunami of Big data, this textbook aims to identify specific knowledge gaps, educational barriers, and workforce readiness deficiencies. Specifically, it focuses on the development of a transdisciplinary curriculum integrating modern computational methods, advanced data science techniques, innovative biomedical applications, and impactful health analytics. The content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. Big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies. ER -Download RIS file
05324nam a22005055i 4500 | |||
001 | 978-3-319-72347-1 | ||
003 | DE-He213 | ||
005 | 20191028151350.0 | ||
007 | cr nn 008mamaa | ||
008 | 180827s2018 gw | s |||| 0|eng d | ||
020 | a 9783319723471 9 978-3-319-72347-1 | ||
024 | 7 | a 10.1007/978-3-319-72347-1 2 doi | |
050 | 4 | a QA76.9.B45 | |
072 | 7 | a UN 2 bicssc | |
072 | 7 | a COM021000 2 bisacsh | |
072 | 7 | a UN 2 thema | |
082 | 4 | a 005.7 2 23 | |
100 | 1 | a Dinov, Ivo D. e author. 4 aut 4 http://id.loc.gov/vocabulary/relators/aut | |
245 | 1 | a Data Science and Predictive Analytics h [electronic resource] : b Biomedical and Health Applications using R / c by Ivo D. Dinov. | |
250 | a 1st ed. 2018. | ||
264 | 1 | a Cham : b Springer International Publishing : b Imprint: Springer, c 2018. | |
300 | a XXXIV, 832 p. 1443 illus., 1245 illus. in color. b online resource. | ||
336 | a text b txt 2 rdacontent | ||
337 | a computer b c 2 rdamedia | ||
338 | a online resource b cr 2 rdacarrier | ||
347 | a text file b PDF 2 rda | ||
505 | a 1 Introduction -- 2 Foundations of R -- 3 Managing Data in R -- 4 Data Visualization -- 5 Linear Algebra & Matrix Computing -- 6 Dimensionality Reduction -- 7 Lazy Learning: Classification Using Nearest Neighbors -- 8 Probabilistic Learning: Classification Using Naive Bayes -- 9 Decision Tree Divide and Conquer Classification -- 10 Forecasting Numeric Data Using Regression Models -- 11 Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines -- 12 Apriori Association Rules Learning -- 13 k-Means Clustering -- 14 Model Performance Assessment -- 15 Improving Model Performance -- 16 Specialized Machine Learning Topics -- 17 Variable/Feature Selection -- 18 Regularized Linear Modeling and Controlled Variable Selection -- 19 Big Longitudinal Data Analysis -- 20 Natural Language Processing/Text Mining -- 21 Prediction and Internal Statistical Cross Validation -- 22 Function Optimization -- 23 Deep Learning Neural Networks -- 24 Summary -- 25 Glossary -- 26 Index -- 27 Errata. | ||
520 | a Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall. First, the volume of data is increasing much faster than the corresponding rise of our computational processing power (Kryder’s law > Moore’s law). Second, traditional discipline-bounds inhibit expeditious progress. Third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. There are very few rigorous instructional resources, interactive learning materials, and dynamic training environments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pillars for the urgently needed bridge to close that supply and demand predictive analytic skills gap. Exposing the enormous opportunities presented by the tsunami of Big data, this textbook aims to identify specific knowledge gaps, educational barriers, and workforce readiness deficiencies. Specifically, it focuses on the development of a transdisciplinary curriculum integrating modern computational methods, advanced data science techniques, innovative biomedical applications, and impactful health analytics. The content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. Big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies. | ||
650 | a Big data. | ||
650 | a Health informatics. | ||
650 | a Mathematical statistics. | ||
650 | a Data mining. | ||
650 | 1 | 4 | a Big Data. 0 http://scigraph.springernature.com/things/product-market-codes/I29120 |
650 | 2 | 4 | a Big Data/Analytics. 0 http://scigraph.springernature.com/things/product-market-codes/522070 |
650 | 2 | 4 | a Health Informatics. 0 http://scigraph.springernature.com/things/product-market-codes/H28009 |
650 | 2 | 4 | a Probability and Statistics in Computer Science. 0 http://scigraph.springernature.com/things/product-market-codes/I17036 |
650 | 2 | 4 | a Data Mining and Knowledge Discovery. 0 http://scigraph.springernature.com/things/product-market-codes/I18030 |
710 | 2 | a SpringerLink (Online service) | |
773 | t Springer eBooks | ||
776 | 8 | i Printed edition: z 9783319723464 | |
776 | 8 | i Printed edition: z 9783319723488 | |
776 | 8 | i Printed edition: z 9783030101879 | |
856 | 4 | u https://doi.org/10.1007/978-3-319-72347-1 | |
912 | a ZDB-2-SCS | ||
950 | a Computer Science (Springer-11645) |
All data below are available with an Open Data Commons Open Database License. You are free to copy, distribute and use the database; to produce works from the database; to modify, transform and build upon the database. As long as you attribute the data sets to the source, publish your adapted database with ODbL license, and keep the dataset open (don't use technical measures such as DRM to restrict access to the database).
The datasets are also available as weekly exports.