Teaching Data and Computational Journalism
Teaching Data and Computational Journalism
Preface
Executive Summary
Introduction
Chapter 1: Defining the Field of Study
What's in a Name?
Four Key Areas of Data Journalism
A Brief History of Computers and Journalists
The Task at Hand: Causes for Concern and Reasons for Hope
Chapter 2: State of the Field: Our Quantitative Data
The Scope of Our Study
Our Findings
Teaching Data Fundamentals: Rows and Columns
Teaching Advanced Data Skills: Visualization and Programming
Alternative Data Instruction: The State of Online Courses
Textbooks: Little Consensus
Chapter 3: Qualitative Findings: Interviews and Observations
Identifying What to Teach
The Coding Issue
Institutional Challenges: Resources
Institutional Challenges: Faculty Expertise
Institutional Challenges: Student Engagement
Chapter 4: Model Curricula in Data and Computation
Introduction and Summary of Curricular Recommendations
Model 1: Integrating Data as a Core Class
Model 2: Integrating Data and Computation into Existing Courses and Concentrations
Model 3: Concentration in Data & Computation
Model 4: Advanced Graduate Degree: Expertise-Driven Reporting on Data & Computation
Model 5: Advanced Graduate Degree: Emerging Journalistic Techniques and Technologies
Chapter 5: Institutional Recommendations
Faculty Development and Recruitment
Trainings or Modules
Incoming Skills, Technical Literacies, and Boot Camps
Technology Infrastructure
Benefits of Distance or Online Learning
Fostering Collaboration
Note on Specialist Faculty in Data and Computation
Appendix
Acknowledgments
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Chapter 3: Qualitative Findings: Interviews and Observations
Chapter 3: Qualitative Findings: Interviews and Observations
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