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Title
Applied data science : data translators across the disciplines / Douglas G. Woolford, Donna Kotsopoulos, Boba Samuels, editors.
ISBN
9783031299377 (electronic bk.)
303129937X (electronic bk.)
3031299361
9783031299360
Publication Details
Cham, Switzerland : Springer, 2023.
Language
English
Description
1 online resource.
Item Number
10.1007/978-3-031-29937-7 doi
Call Number
QA76.9.B45
Dewey Decimal Classification
005.7
Summary
The use of data to guide action is growing. Even the public uses data to guide everyday decisions! How do we develop data acumen across a broad range of fields and varying levels of expertise? How do we foster the development of effective data translators? This book explores these questions, presenting an interdisciplinary collection of edited contributions across fields such as education, health sciences, natural sciences, politics, economics, business and management studies, social sciences, and humanities. Authors illustrate how to use data within a discipline, including visualization and analysis, translating and communicating results, and pedagogical considerations. This book is of interest to scholars and anyone looking to understand the use of data science across disciplines. It is ideal in a course for non-data science majors exploring how data translation occurs in various contexts and for professionals looking to engage in roles requiring data translation.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed May 24, 2023).
Series
Studies in big data ; v. 125.
Available in Other Form
Print version: 9783031299360
Translating science into actionable policy information a perspective on the IPCC process
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On Becoming a Data-Scientist , Beyond Translation: Discovering Best Practices for Evidence-Informed Decision Making for Public Health Practices
Concern for self-health during the COVID-19 pandemic in Canada: How to use quantitative data to tell an intersectional story?- Community-based participatory research and respondent-driven sampling: A statisticians, community partners and students perspectives on a successful partnership.
