Data Sciences

The Data Sciences practice comprises Exponent’s core capabilities in statistics, data analytics, and dedicated data collection. Drawing on experience in a breadth of engineering, science, health, and environmental applications, we assist clients with their most complex data challenges at all stages of the product or process life cycle.

Our team of interdisciplinary scientists and engineers design sampling plans, surveys, and experiments to create, manage, and analyze data sets of all sizes and varieties. User-focused visualizations support data-driven decision-making and help clients measure risks and benefits to determine appropriate courses of action. Utilizing rigorous statistical methods, our team can help assess and improve quality and reliability and mitigate risk.

Dedicated data collection teams design and execute data acquisition strategies focused on generating high-quality, diverse data sets. Our teams can source human-centered data using both off-the-shelf hardware application programming interfaces (APIs) and custom sensors from global populations.

The Data Sciences practice draws on a breadth of experience across industries and practice areas to help clients with:

  • The design and analysis of product development studies.
  • The development, improvement, and control of manufacturing processes and product quality.
  • Monitoring the safety, reliability, and performance of products in use by customers.
  • Finding technology solutions for labor-intensive processes using advanced sensors, image analysis, and natural language processing.
  • The evaluation and improvement of predictive models to better forecast future workflows, yield, or consumer behavior.
  • Building custom machine learning/artificial intelligence tools and improving diversity and reducing bias in existing algorithms.

Our consultants are nationally recognized leaders who assist clients in the extraction of information from data to address questions pertinent to their organization’s mission and key relationships.



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