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Initial report outline, open for comment

Initial Outline for Comments Regarding Structure

  • Executive summary
  • Introduction
    • Background
    • Key goals and motivation
  • Charge to the participants and results of the pre-workshop work
  • Structure of the workshop and key topics from each session
  • Thematic findings, not bound by the structure of the event, including the following:
    • Language
      • What is a common language for discussing this?
    • Motivation and momentum of the group
      • This is the time when we have an opportunity to work with something new and exciting.
    • Inherent interdisciplinary nature
      • Not just a combination of courses.  Maybe something new, rather than a combination of disciplines.
    • No clarity regarding whether or not we should define a core
      • Nervousness about defining a core that seemed to box things in.  Generally agreeable that some things are definitely part of data science.
      • Core competences vs. core topics
    • Significant differences in how people see the role of domains in data science
      • Expertise that can be applied to many domains
      • Domain drives the need that data science addresses; data science changes the domain
      • Bioinformatics, business analytics (sometimes data analytics)
        • Evolved in the context of a specific domain
    • Initial thoughts regarding curriculum content
      • See Heikki’s slides that summarize the topics that people identified before the meeting.  Was there any indication of movement of those ideas during the meeting? 
    • Competence requirements vary depending on the stages in the data science process. 
      • Some very abstract thinkers who advance the algorithms and the techniques; others work on data munging, perhaps using tools.  Others do visualization and presentation. 
      • Need or desirability of a deeper understanding of the potential contributions of data science, regardless of the limitations of the eventual role?
    • Changing status quo within universities. 
      • To do it well we need a new structure/environment in the university.  This is the nature of truly interdisciplinary work.
    • Role of ethics and the ability to understand implications for data scientists
    • Integrative conclusion
  • Follow-up actions and recommendations
    • Curriculum
      • Guidance, not constraining
        • Lots of variation supported
        • Some guidance about the elements that any program in data science must include
      • Core competencies?  Expected outcomes?
      • Core topics (Core topics à core competencies)
      • Vote on curriculum recommendation document: all but 2 said yes, 2 said not sure. 
      • Inherently interdisciplinary
        • Not just a combination of courses from a number of disciplines
        • Is it really something new, or a particular combination of existing things?
        • Defining a core may be too much a model of thinking that does not fit this phenomenon.
    • Learning outcomes/objectives for undergraduates who major in data science
    • Building mechanisms to support faculty development 
    • Developing models for collaborative teaching that work with the university scheduling requirements
    • Platform(s) for sharing
      • Exchange of ideas.  Ensemble?  <Umbrella theme to connect multiple sites? Part of our umbrella plan?>
    • Enabling communication regarding things that did not work
      • Establish some sort of venue for publishing stories of lessons learned from efforts that did not achieve their intended goals. 
      • Ethics, implications.
    • Organizing follow-up meetings
      • Involving industry representatives
      • A broader academic/industry group
  • Summary and conclusions
  • Appendices
    • List of attendees and affiliations
    • Agenda
      • List of breakout sessions
      • Summary of results from each group
    • Copies of slides
    • The ASA statement on the role of statistics in data science