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Constituent Ingredients of Data Science
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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.

#. Math
    - Probability theory
    - Statistical Learning/Inference
        - Maximum Likelihood
        - Probably Approximately Correct
        - Hypothesis Testing
    - Optimization
        - Calculus
    - Linear Algebra/Sheaf theory
        - Arrays
        - Signal Processing 

#. 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
#. 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.