Skip to main content

Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science (2021) / Chapter Skim
Currently Skimming:

Appendix D: Skills for Data Science Mastery
Pages 120-123

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 120...
... • Mathematical foundations. Key mathematical concepts/skills that would be important for all students in their data science programs and critical for their success in the workforce are the following: -- Set theory and basic logic, -- Multivariate thinking via functions and graphical displays, -- Basic probability theory and randomness, -- Matrices and basic linear algebra, -- Networks and graph theory, and -- Optimization.
Read the entire page →
From page 121...
... Key data management and cura tion concepts/skills that would be important for all students in their data science programs and critical for their success in the workforce are the following: -- Data provenance; -- Data preparation, especially data cleansing and data transformation; -- Data management (of a variety of data types) ; -- Record retention policies; -- Data subject privacy; -- Missing and conflicting data; and -- Modern databases.
Read the entire page →
From page 122...
... Key data modeling and assessment concepts/skills that would be important for all students in their data science programs and critical for their success in the work force are the following: -- Machine learning (e.g., supervised, unsupervised, and deep learning) , -- Multivariate modeling and supervised learning, -- Dimension reduction techniques and unsupervised learning, -- Deep learning, -- Model assessment and sensitivity analysis, and -- Model interpretation (particularly for "black box" models)
Read the entire page →
From page 123...
... Key aspects of ethics needed for all data scientists (and for that matter, all educated citizens) include the following: -- Ethical precepts for data science and codes of conduct, -- Privacy and confidentiality (both in the spirit and letter of the law)
Read the entire page →

Key Terms

  • science programs
  • data scientists
  • key data
  • data modeling
  • exploratory data

  • basic data
  • data description
  • key concepts
  • linear algebra
  • model assessment

  • data preparation
  • data cleansing
  • especially data
  • data consistency
  • data subject

  • data types
  • consistency checking
  • subject privacy
  • ethical precepts
  • retention policies

  • record retention
  • domain requires
  • effective presentation
  • sensitivity analysis
  • conflict resolution

  • requires knowledge
  • deeper understanding
  • advanced linear
  • substantive contextual
  • presentation skills


    • This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
    More information on Chapter Skim is available.