Data Literacy

How to Make Your Experiments Robust and Reproducible

Nonfiction, Science & Nature, Science, Other Sciences, Molecular Biology, Health & Well Being, Medical, Specialties, Oncology
Cover of the book Data Literacy by Neil Smalheiser, Elsevier Science
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Author: Neil Smalheiser ISBN: 9780128113073
Publisher: Elsevier Science Publication: September 5, 2017
Imprint: Academic Press Language: English
Author: Neil Smalheiser
ISBN: 9780128113073
Publisher: Elsevier Science
Publication: September 5, 2017
Imprint: Academic Press
Language: English

Data Literacy: How to Make Your Experiments Robust and Reproducible provides an overview of basic concepts and skills in handling data, which are common to diverse areas of science. Readers will get a good grasp of the steps involved in carrying out a scientific study and will understand some of the factors that make a study robust and reproducible.The book covers several major modules such as experimental design, data cleansing and preparation, statistical analysis, data management, and reporting. No specialized knowledge of statistics or computer programming is needed to fully understand the concepts presented.

This book is a valuable source for biomedical and health sciences graduate students and researchers, in general, who are interested in handling data to make their research reproducible and more efficient.

  • Presents the content in an informal tone and with many examples taken from the daily routine at laboratories
  • Can be used for self-studying or as an optional book for more technical courses
  • Brings an interdisciplinary approach which may be applied across different areas of sciences
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Data Literacy: How to Make Your Experiments Robust and Reproducible provides an overview of basic concepts and skills in handling data, which are common to diverse areas of science. Readers will get a good grasp of the steps involved in carrying out a scientific study and will understand some of the factors that make a study robust and reproducible.The book covers several major modules such as experimental design, data cleansing and preparation, statistical analysis, data management, and reporting. No specialized knowledge of statistics or computer programming is needed to fully understand the concepts presented.

This book is a valuable source for biomedical and health sciences graduate students and researchers, in general, who are interested in handling data to make their research reproducible and more efficient.

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