Data Science

by John D. Kelleher

or Hadoop. All of the data in these various databases and data sources will need to be integrated, cleansed, transformed, normalized, and so on. These tasks go by many names, such as extraction, transformation, and load, “data munging,” “data wrangling,” “data fusion,” “data crunching,” and so on. Like source data, the data generated from data science activities also need to be stored and managed. Again, a database is the typical storage location for the data generated by these activities because they can then be easily distributed and shared with different parts of the organization. As a consequence, data scientists need to have the skills to interface with and manipulate data in databases. A range of computer science skills and tools allows data scientists to work with big data and to process it into new, meaningful information. High-performance computing (HPC) involves aggregating computing power to deliver higher performance than one can get from a stand-alone computer. Many data science projects work with a very large data set and ML algorithms that are computationally expensive. In these situations, having the skills required to access and use HPC resources is important. Beyond HPC, we have already mentioned the need for data scientists to be able to scrap, clean, and integrate web data as well as handle and process unstructured text and images. Furthermore, a data scientist may also end up writing in-house applications to perform a specific task or altering an exist...