Applied Data Science: Data Translators Across the Disciplines (Studies in Big Data, 125)

Description

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. Preface Acknowledgments Contents Translating Science into Actionable Policy Information—A Perspective on the Intergovernmental Panel on Climate Change Process References Data in Observational Astronomy 1 Why Do Astronomers Collect Data? 2 What Are Astronomy Data? 2.1 Where Do Astronomy Data Come from? 2.2 How Are Astronomy Data Processed? 2.3 How Are Astronomy Data Visualized and Used? 3 Unique Aspects of Astronomy Data 4 Sharing Astronomy Data and Results 4.1 Astronomy Data Sharing Between Researchers 4.2 Astronomy Data Sharing with the Public 4.3 Astronomy Data and Data-Sharing Example 5 Data Challenges in Astronomy 6 Conclusions References Beyond Translation: An Overview of Best Practices for Evidence-Informed Decision Making for Public Health Practice 1 Introduction 2 Scientific Writing and Author Guidelines for Scientific Journals 3 Reporting Guidelines 4 Guidelines for Development of Health Policy Recommendations: Grading the Evidence 5 Post-Decision Consensus Building and Public Health Messaging 6 Summary References Concern for Self-Health During the COVID-19 Pandemic in Canada: How to Tell an Intersectional Story Using Quantitative Data? 1 Introduction 2 Intersectionality Theory 3 Quantitative Intersectionality Approaches 4 What Does Quantitative Intersectionality Add to Public Health? 5 Case Study: Concerns During the COVID-19 Pandemic in Canada 5.1 Research Objective and Questions 5.2 Methods 5.3 Statistical Analyses 5.4 Findings 6 Discussion 7 Discussion Questions References Community-Based Participatory Research and Respondent-Driven Sampling: A Statistician’s, Community Partner’s and Students’ Perspectives on a Successful Partnership 1 Introduction 2 A Statistical Lead’s Keys for a Successful Partnership 2.1 Commit to Furthering Your Understanding of the Community and Their Experiences 2.2 Seek Support of a “Subject Translator” 2.3 Respectful Engagement of Partners 2.4 Understand the “Bigger Picture” 2.5 Recognize When Methodological Details Are Not Needed 2.6 The Science of Uncertainty 3 Students’ Perspectives on Working with Indigenous Community Research Partners and RDS in a Quantitative Discipline 3.1 Indigenous Reproductive Justice and Community Wellness, by Danette Jubinville 3.2 The Determinants of Indigenous Peoples’ Health as Predictors for Diabetes and Unmet Health Needs, by Stephanie McConkey 3.3 Risk and Protective Factors for Major Depressive Disorder in Urban Indigenous Communities, by Octavia Wong 4 A Community Partner’s Perspective on the Importance of Working with Statisticians 5 Future Directions References Operationalizing Learning Processes Through Learning Analytics 1 Types of Learner Data 2 Using Learner Data to Operationalize Procrastination 2.1 Combining Data Sources 3 Using Learner Data to Generate Feedback 4 Future Directions and Conclusion References Improving Data Literacy in Management Education Through Experiential Learning: A Demonstration Using Tableau Software 1 Introduction 2 Data Literacy 3 What is Big Data? 4 Data Analytics 5 The Six Stages of the Data Analytics Workflow 5.1 Business Problem Identification 5.2 Data Collection 5.3 Data Processing 5.4 Data Analysis 5.5 Data Visualization 5.6 Data Translation 5.7 Lesson Plan 6 Conclusion References Understanding Players and Play Through Game Analytics 1 Introduction 2 A Method for Game Analytics 3 Understanding My Singing Monsters 4 Preparing the Data 5 Constructing the Models 5.1 Two Clustering Algorithms 6 Analyzing the Results 6.1 Clustering Metrics 6.2 k-Means Versus Archetypal Analysis 7 Interpreting the Analysis 7.1 Visualizing High-Dimensional Data 7.2 Cluster Visualization 7.3 Day 120 LTVs 7.4 Acquisition Source 7.5 Representative Analysis 8 Concluding Remarks References Language Corpora and Principal Components Analysis 1 Introduction 2 Statistical Analyses in Linguistics 2.1 Exploratory Statistical Analyses in Linguistic Research 2.2 Principal Components Analysis 2.3 Current Study 3 Analysis and Interpretation 3.1 Verify Assumptions 3.2 Conduct Analysis 4 Data Translation and Conclusion References A Tutorial of Analyzing Accuracy in Conceptual Change 1 Introduction 2 Quantitative Conceptual Change in Science Education 3 A Binomial Distribution of Binary Responses 4 Description of the Dataset 5 Analyzing Accuracy Data 5.1 Repeated-Measures ANOVA Over Untransformed Proportions Correct 5.2 Logistic Regression-Based Modelling of Accuracy Data 6 Discussion 7 Summary References Transforming Data on the Boundaries of Science and Policy: The Council of Canadian Academies’ Rhetorical Repertoire 1 Introduction 2 Related Research and Theories 2.1 Rhetoric of Science 2.2 Data Translation as a Semiotic Process of Uptake and Recontextualization 3 Methodology 3.1 Data Collected 3.2 Analytical Approaches 4 Findings 4.1 Data Uptake 4.2 Recontextualizations of Data 4.3 Brief Discussion of Findings 5 Conclusion References A Conceptual Framework for Knowledge Exchange in a Wildland Fire Research and Practice Context 1 Introduction 2 Knowledge Exchange (KE) 3 Knowledge, Researchers and Practitioners 4 The Role of Knowledge Exchange from Problem Identification to Implementation 4.1 Problem Identification 4.2 The Process 4.3 Inquiry 4.4 Synthesis 4.5 Application 4.6 Implementation 4.7 Processes of Progression and Retrogression 4.8 Research and Development Cycle 4.9 Barriers and Facilitators 5 Training the Next Generation in Knowledge Exchange 6 Closing References Pedagogical and Future Implications for the Training of Data Translators References