1. Bit.ly/2c3XMgd

2. Informatics underlying Data Science (ists)
Peter Fox (RPI)
SciDataCon 2016, Denver CO
Mon. Sep
“Defining Data Professionals”

3. Modern informatics methodology
Use cases
Stakeholders
Distributed authority
Access control
Ontologies
Maintaining Identity
See the Semantic eScience class that teaches this methodology at

4. But really it’s not just one field
Informatics IT
Cyber
Infrastructure (CI)
Cyber Informatics
Core Informatics
Science Informatics
Science, Benefit to others
Functional requirements
SBA=societal benefit areas
CI = Discipline neutral, e.g. web server, database, wiki
Cyberinformatics = mapping to discipline neutral aspects
Core informatics = Reasoning engine, semantics, computer science
Science (X) informatics = Use cases, science domain terms, concepts in an ontology or controlled vocabulary

5. Context Data Science Xinformatics Semantic eScience Web Science
GIS4Science
Data Analytics
Context
Data Science
Xinformatics
Semantic eScience
Experience
Data
Information
Knowledge
Creation
Gathering
Presentation
Organization
Integration
Conversation 5
Web Science

6. So who are we talking about?

7. Overused Venn diagram of the intersection of skills needed for Data Science (Drew Conway)
Anatomy
Physiology ?
Missing Anatomy

8. Data Science Anatomy (as an individual)
Data Life Cycle – Acquisition, Curation and Preservation
Data Management and Products
Forms of Analysis, Errors and Uncertainty
Technical tools and standards

9. Data Science Physiology (in a group)
Definition of Science Hypotheses, Guiding Questions
Finding and Integrating Datasets
Presenting Analyses and Viz.
Presenting Conclusions