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Scientific challenge

Trying to make sense of advanced statistical analysis without proper understanding of the data is not a good idea. Data science and statistics are error-prone when data exploration is ignored. 

Skipping the data exploration phase involves performing statistical analysis by trial and error, without the formulation of hypotheses and pursuing an arbitrary selection of statistical tools and techniques, leading to inefficiency and error.

Taking action to avoid the situation is now possible. Eagle Genomics supports the data exploration journey, helping teams to fathom complex and meshed data with the right visualization and analytics tools.

Key features

  • Perform statistical data exploration, mining and analysis in an intuitive, visual manner to boost understanding
  • Identify data patterns by uncovering distributions, associations, groups, and more
  • Formulate relevant data-driven hypotheses, based on proper understanding of data characteristics and behavior
  • Select the appropriate statistical tools and techniques to effectively test formulated hypotheses
  • Identify data quality and data curation issues, and resolve them before proceeding to complex statistical analysis
  • Systematize data quality assessment to turn data into actionable information
  • Understand data associations to address biases and confounding effects, in preparation for robust, reliable analysis
  • Extract valuable insights from data and identify the appropriate advanced analytical tools for statistical analysis
  • Provide a mechanism for effective communication between data scientists, life scientists and business decision makers
  • Establish contingency plans to deal with any issues, and improve protocols and research methods in the future

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