Data Modeling for the Sciences

'Data Modeling for the Sciences, co-written by a mathematician and molecular scientist, manages to be rigorous, state-of-the-art, and yet accessible all at the same time. Experimentalists faced with complex data sets who need to take their data science to the next level will find this indispensable, and the book forms a great basis for a data science course in physics, chemistry, or biology departments.' Martin Gruebele, James R. Eiszner Chair, University of Illinois at Urbana-Champaign
'This book fills a vacuum that has been growing in the last two decades due to the increasing challenges faced by scientists in the analysis of larger and more complex sets of data. Readers will find the foundations of statistical inference, simulation, and computational modeling formulated in a rigorous yet extremely clear manner. In particular, they will learn how much more powerful a data-driven approach to data analysis can be.' Carlos Bustamante, University of California, Berkeley
'This impressive mathematical treatise lays out a rigorous approach for data analysis and modeling of complex physical systems based on a modern data-centric approach, where noisy measurements are used to extract models for stochastic behavior. Presse and Sgouralis are to be congratulated on the breadth and depth of their presentation.' W. E. Moerner, Stanford University
'This textbook is a foundational treatise that will change how we address our data by educating a generation of students in data-driven tools available nowhere else. A must/required text for the single molecule biophysics field; I'll definitely require my research students to use it.' Shimon Weiss, Department of Chemistry and Biochemistry, University of California, Los Angeles