  Efficient numerical linear algebra is a core ingredient in many applications
across almost all scientific and industrial disciplines. With this survey we
want to illustrate that numerical linear algebra has played and is playing a
crucial role in enabling and improving data science computations with many new
developments being fueled by the availability of data and computing resources.
We highlight the role of various different factorizations and the power of
changing the representation of the data as well as discussing topics such as
randomized algorithms, functions of matrices, and high-dimensional problems. We
briefly touch upon the role of techniques from numerical linear algebra used
within deep learning.
