The amount of new information is constantly increasing faster than our ability to fully interpret and utilize it to improve human experiences Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces By lifting the concept of time from a positive real number to a 2D complex time kime this book uncovers a connection between artificial intelligence AI data science and quantum mechanics It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data e g time series are represented as manifolds e g kime surfaces This new framework enables the development of innovative data science analytical methods for model based and model free scientific inference derived computed phenotyping and statistical forecasting The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles such as particles and wavefunctions into data science concepts such as datum and inference functions It includes many open mathematical problems that still need to be solved technological challenges that need to be tackled and computational statistics algorithms that have to be fully developed and validated Spacekime analytics provide mechanisms to effectively handle process and interpret large heterogeneous and continuously tracked digital information from multiple sources The authors propose computational methods probability model based techniques and analytical strategies to estimate approximate or simulate the complex time phases kime directions This allows transforming time varying data such as time series observations into higher dimensional manifolds representing complex valued and kime indexed surfaces kime surfaces The book includes many illustrations of model based and model free spacekime analytic techniques applied to economic forecasting identification of functional brain activation and high dimensional cohort phenotyping Specific case study examples include unsupervised clustering using the Michigan Consumer Sentiment Index MCSI model based inference using functional magnetic resonance imaging fMRI data and model free inference using the UK Biobank data archive The material includes mathematical inferential computational and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory where a few spacetime observations can be amplified by a series of derived estimated or simulated kime phases The authors extend Newton Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the problems of time The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics as well as statistical articulation of spacekime analytics in a Bayesian inference framework The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies Spacekime analytics represents one new data analytic approach which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures This book may be of interest to academic scholars graduate students postdoctoral fellows artificial intelligence and machine learning engineers biostatisticians econometricians and data analysts Some of the material may also resonate with philosophers futurists astrophysicists space industry technicians biomedical researchers health practitioners and the general public includes open mathematical problems and technological challenges that need to be solved extensive online supplementary materials datasets interactive web demonstrations analytical case studies R software code and a community support network
Data Science by Ivo D. Dinov – eBook Details
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Full Book Name: Data Science
Author Name: Ivo D. Dinov
Book Genre: Nonfiction
Edition Language: English
Date of Publication: 2021
Available Format : PDF
