# 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.

- 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.