Dana Center design principles: communication

eCOTS 2022 Workshop

Nicholas J. Horton and Benjamin S. Baumer

May 19, 2022

Dana Center Data Science Design Principle Framework

• The framework is copyright 2021 The Charles A. Dana Center at The University of Texas at Austin.
• The Dana Center has granted educators a nonexclusive license to reproduce and share copies of this publication to advance this work.

Design Principle: communication

The course develops students’ ability to communicate insights from their data explorations and findings in varied ways, including with words, data visualizations and numbers.

Student perspectives

• Present and explain ideas, reasoning, and representations to one another in pair, small- group, and whole-class discourse using discipline- specific terminology, language constructs, and symbols.
• Seek to understand the approaches used by peers by asking clarifying questions, trying out others’ strategies, and describing the approaches used by others.
• Listen carefully to and critique the reasoning of peers using data to support arguments or counterexamples to refute arguments.

Student perspectives (cont.)

• Develop the skills to communicate data-based arguments with clarity and precision.
• Practice constructing data-based arguments with specific audiences in mind.
• Consider matters of accessibility in designing and executing their communications.
• Consider the pros and cons of various types of data visualizations for communicating with data in different situations.

Faculty perspectives

• Introduce concepts in a way that connects students’ experiences to course content and that bridges from informal contextual descriptions to formal definitions.
• Clarify the use of data science terminology and symbols, especially those also used in different contexts or different disciplines.
• Engage students in purposeful sharing of data explorations and approaches using varied representations.

Faculty perspectives (cont.)

• Support students in developing active listening skills and in asking clarifying questions to their peers in a respectful manner that deepen understanding.
• Facilitate discourse by positioning students as authors of ideas who explain and defend their approaches.

Faculty perspectives (cont.)

• Provide regular opportunities for students to communicate with data using a variety of data visualizations.
• Scaffold instruction to support students in developing the required reading and writing skills.

How to operationalize?

Case study: MDSR data viz

• Data Viz in MDSR 1 of 2
• Data Viz in MDSR 2 of 2
• Data Viz in SDS192
• Deconstructing Data Graphics
• Data Visualization Project

Some notes

• The NASEM 2018 report described the following components of data acumen that are important for undergraduate data science:
• Communication and teamwork
  • Ability to understand client needs
  • Clear and comprehensive reporting
  • Conflict resolution skills
  • Well-structured technical writing without jargon
  • Effective presentation skills

Some notes

NASEM 2018 also highlighted:

• Data description and visualization
  • Data consistency checking
  • Exploratory data analysis
  • Grammar of graphics
  • Attractive and sound static and dynamic visualizations
  • Dashboards