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    Quantitative Biosciences Companion in Python

Quantitative Biosciences Companion in Python

Joshua S. Weitz
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      A hands-on lab guide in the Python programming language that enables students in the life sciences to reason quantitatively about living systems across scalesThis lab guide accompanies the textbook Quantitative Biosciences, providing students with the skills they need to translate biological principles and mathematical concepts into computational models of living systems. This hands-on guide uses a case study approach organized around central questions in the life sciences, introducing landmark advances in the field while teaching students—whether from the life sciences, physics, computational sciences, engineering, or mathematics—how to reason quantitatively in the face of uncertainty.Draws on real-world case studies in molecular and cellular biosciences, organismal behavior and physiology, and populations and ecological communitiesEncourages good coding practices, clear and understandable modeling, and accessible presentation of resultsHelps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scaleBuilds practical expertise in a range of methods, including sampling from probability distributions, stochastic branching processes, continuous time modeling, Markov chains, bifurcation analysis, partial differential equations, and agent-based simulationsBridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their ownStand-alone computational lab guides for Quantitative Biosciences also available in R and MATLAB
      Format: CONTRIBUTORS: Joshua S. Weitz EAN: 9780691255675 COUNTRY: United States PAGES: WEIGHT: 0 g HEIGHT: 254 cm
      PUBLISHED BY: Princeton University Press DATE PUBLISHED: 2024-03-05 CITY: GENRE: COMPUTERS / Data Science / Bioinformatics, SCIENCE / Life Sciences / Biology, SCIENCE / Biotechnology, SCIENCE / Life Sciences / Cell Biology, SCIENCE / Laboratory Techniques WIDTH: 203 cm SPINE:

      Book Themes:

      Data science and analysis: general, Mathematics and Science, Biology, life sciences, Computational biology / bioinformatics

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      Joshua S. Weitz is professor and the Clark Leadership Chair in Data Analytics in the Department of Biology at the University of Maryland. Previously, he held the Tom and Marie Patton Chair in Biological Sciences at the Georgia Institute of Technology, where he founded the Interdisciplinary Graduate Program in Quantitative Biosciences. He is the author of Quantitative Viral Ecology (Princeton). Nolan English is a postdoctoral researcher at Oak Ridge National Laboratory. Alexander B. Lee is a data scientist with expertise in developing biological models in Python and MATLAB. Ali Zamani is associate data developer at Priceline.
      A hands-on lab guide in the Python programming language that enables students in the life sciences to reason quantitatively about living systems across scalesThis lab guide accompanies the textbook Quantitative Biosciences, providing students with the skills they need to translate biological principles and mathematical concepts into computational models of living systems. This hands-on guide uses a case study approach organized around central questions in the life sciences, introducing landmark advances in the field while teaching students—whether from the life sciences, physics, computational sciences, engineering, or mathematics—how to reason quantitatively in the face of uncertainty.Draws on real-world case studies in molecular and cellular biosciences, organismal behavior and physiology, and populations and ecological communitiesEncourages good coding practices, clear and understandable modeling, and accessible presentation of resultsHelps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scaleBuilds practical expertise in a range of methods, including sampling from probability distributions, stochastic branching processes, continuous time modeling, Markov chains, bifurcation analysis, partial differential equations, and agent-based simulationsBridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their ownStand-alone computational lab guides for Quantitative Biosciences also available in R and MATLAB
      Format: CONTRIBUTORS: Joshua S. Weitz EAN: 9780691255675 COUNTRY: United States PAGES: WEIGHT: 0 g HEIGHT: 254 cm
      PUBLISHED BY: Princeton University Press DATE PUBLISHED: 2024-03-05 CITY: GENRE: COMPUTERS / Data Science / Bioinformatics, SCIENCE / Life Sciences / Biology, SCIENCE / Biotechnology, SCIENCE / Life Sciences / Cell Biology, SCIENCE / Laboratory Techniques WIDTH: 203 cm SPINE:

      Book Themes:

      Data science and analysis: general, Mathematics and Science, Biology, life sciences, Computational biology / bioinformatics

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      Joshua S. Weitz is professor and the Clark Leadership Chair in Data Analytics in the Department of Biology at the University of Maryland. Previously, he held the Tom and Marie Patton Chair in Biological Sciences at the Georgia Institute of Technology, where he founded the Interdisciplinary Graduate Program in Quantitative Biosciences. He is the author of Quantitative Viral Ecology (Princeton). Nolan English is a postdoctoral researcher at Oak Ridge National Laboratory. Alexander B. Lee is a data scientist with expertise in developing biological models in Python and MATLAB. Ali Zamani is associate data developer at Priceline.

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