Computing Skills for Biologists

Download or Read eBook Computing Skills for Biologists PDF written by Stefano Allesina and published by Princeton University Press. This book was released on 2019-01-15 with total page 440 pages. Available in PDF, EPUB and Kindle.
Computing Skills for Biologists
Author :
Publisher : Princeton University Press
Total Pages : 440
Release :
ISBN-10 : 9780691182759
ISBN-13 : 0691182752
Rating : 4/5 (59 Downloads)

Book Synopsis Computing Skills for Biologists by : Stefano Allesina

Book excerpt: A concise introduction to key computing skills for biologists While biological data continues to grow exponentially in size and quality, many of today’s biologists are not trained adequately in the computing skills necessary for leveraging this information deluge. In Computing Skills for Biologists, Stefano Allesina and Madlen Wilmes present a valuable toolbox for the effective analysis of biological data. Based on the authors’ experiences teaching scientific computing at the University of Chicago, this textbook emphasizes the automation of repetitive tasks and the construction of pipelines for data organization, analysis, visualization, and publication. Stressing practice rather than theory, the book’s examples and exercises are drawn from actual biological data and solve cogent problems spanning the entire breadth of biological disciplines, including ecology, genetics, microbiology, and molecular biology. Beginners will benefit from the many examples explained step-by-step, while more seasoned researchers will learn how to combine tools to make biological data analysis robust and reproducible. The book uses free software and code that can be run on any platform. Computing Skills for Biologists is ideal for scientists wanting to improve their technical skills and instructors looking to teach the main computing tools essential for biology research in the twenty-first century. Excellent resource for acquiring comprehensive computing skills Both novice and experienced scientists will increase efficiency by building automated and reproducible pipelines for biological data analysis Code examples based on published data spanning the breadth of biological disciplines Detailed solutions provided for exercises in each chapter Extensive companion website


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