Counting: Permutations & Combinations

The mathematics of counting is important in data science with respect to the organization and analysis of discrete components of information. For the task of determining total possible outcomes for various scenarios, counting is considered fundamental for studies in statistics and probability. Applications of counting relevant to data science include: Choosing the best path between points in the design of a system. Minimizing algorithmic complexity with various data structures. Analysis of symbols for encoding and decoding. And minimizing principle component combinations in highly dimensional data. Computing the number of possible combinations of elements of a system is called combinatorial analysis and involves the mathematical principles of permutations and combinations.