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Exploring Modeling with Data and Differential Equations Using R provides a unique introduction to differential equations with applications to the biological and other natural sciences. Additionally, model parameterization and simulation of stochastic differential equations are explored, providing additional tools for model analysis and evaluation. This unified framework sits'at the intersection'of different mathematical subject areas, data science, statistics, and the natural sciences. The text throughout emphasizes data science workflows using the R statistical software program and the tidyverse constellation of packages. Only knowledge of calculus is needed; the text's integrated framework is a stepping stone for further advanced study in mathematics or as a comprehensive introduction to modeling for quantitative natural scientists.The text will introduce you to: modeling with systems of differential equations and developing analytical, computational, and visual solution techniques. the R programming language, the tidyverse syntax, and developing data science workflows. qualitative techniques to analyze a system of differential equations. data assimilation techniques (simple linear regression, likelihood or cost functions, and Markov Chain, Monte Carlo Parameter Estimation) to parameterize models from data. simulating and evaluating outputs for stochastic differential equation models. An associated R package provides a framework for computation and visualization of results. It can be found here: https://cran.r-project.org/web/packages/demodelr/index.html.

The didactical level of exposition, together with many astonishing images and animations, accompanied by the related simple computer programming codes (in Python and POV-Ray languages) make this book an extremely and unique useful tool to test the power of algorithmic information in generating ordered structure models (2D and 3D) like regular geometric shapes, complex shapes like fractals and cellular automata, and biological systems as the organs of a living body. Informational biologists besides mathematicians and physicists of complexity may learn to test their own capabilities in programming and modelling ordered structures starting from random initial conditions at different scale of each system: from elementary particles, to biological systems, to galaxies and the whole universe. Moreover the philosophical comments comparing some aspects of modern information theory to the Aristotelian notion of'form are very appealing also for the epistemologist and the philosopher involved in complexity matters.