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This content will become publicly available on April 27, 2018

Title: Scaling up to address data science challenges

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithms and procedures for efficient processing.
Authors:
ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-16-29638
Journal ID: ISSN 0933-2480
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Chance
Additional Journal Information:
Journal Volume: 30; Journal Issue: 2; Journal ID: ISSN 0933-2480
Publisher:
Springer
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; computer science; mathematics; data science; sampling; visualization; selection; bit maps
OSTI Identifier:
1358169