Understanding the Data Science Technical Landscape:
Primary challenges in and technical approaches to complex workflow components of Big Data systems, including ETL, lifecycle management, analytics, visualization & human-system interaction.
Major forms of analytics employed in data science.
Improving Analytic System Performance via Measurement Science
Generation of ground truth for large datasets and performance measurement with limited or no ground truth.
Methods to measure the performance of data analytic workflows where there are multiple subcomponents, decision points, and human interactions.
Methods to measure the flow of uncertainty across complex data analytic systems.
Approaches to formally characterizing end-to-end analytic workflows.
Datasets to Enable Rigorous Data Science Research
Useful properties for data science reference datasets.
Leveraging simulated data in data science research.
Efficient approaches to sharing research data.
http://www.nist.gov/itl/iad/data-science-symposium-2014.cfm