Constituent Ingredients of Data Science

The following is my eccentric opinion on what makes up the cross disciplinary subject of data science, along with a non-exhaustive list of subconstituents.

  1. Math

    • Probability theory

    • Statistical Learning/Inference

      • Maximum Likelihood

      • Probably Approximately Correct

      • Hypothesis Testing

    • Optimization

      • Calculus

    • Linear Algebra/Sheaf theory

      • Arrays

      • Signal Processing

  2. Software Development

    • Documentation

    • Text Editing

      • IDE, Vim, etc.

    • Fluently reading/writing in high Level programming language(s)

    • Using/creating libraries, APIs, open source software, etc.

    • Development practices

      • Test Driven Development

      • AGILE

      • Continuous integration/delivery

    • Version control

    • Dependency management

    • Effective communication

    • Identification/construction of key performance indicators

    • Product sense

  3. Subject Matter Expertise

    • “Know the data”

Note

I used to have decision making as a section. This has been subsumed under the software development.

Note

I used to have a section on the experimental method. I’ve subsumed this into the category of statistics, which fits into math. This is questionable. What I mean is that the aspects of the experimental method which are relevant to data science fit into statistics, and therefore math.