Data science: An action plan for expanding the technical areas of the field of statistics

An action plan to enlarge the technical areas of statistics focuses on the data analyst. The plan sets out six technical areas of work for a university department and advocates a specific allocation of resources devoted to research in each area and to courses in each area. The value of technical work is judged by the extent to which it benefits the data analyst, either directly or indirectly. The plan is also applicable to government research labs and corporate research organizations. 1 Summary of the Plan This document describes a plan to enlarge the major areas of technical work of the field of statistics. Because the plan is ambitious and implies substantial change, the altered field will be called “data science.” The focus of the plan is the practicing data analyst. A basic premise is that technical areas of data science should be judged by the extent to which they enable the analyst to learn from data. The benefit of an area can be direct or indirect. Tools that are used by the data analyst are of direct benefit. Theories that serve as a basis for developing tools are of indirect benefit. A broad successful theory can have a wide-ranging benefit, affecting data analysis in a fundamental way. For example, the Bayesian theory of statistics affects all methods of estimation and distribution. The plan sets out six technical areas for a university department and advocates a specific allocation of resources to research and development in each area as a percent of the total resources that are available beyond those needed to teach the courses in the department’s curriculum. Furthermore, the allocation applies to the make-up of the curriculum; that is, the allocations are a guideline for the percentage of courses in each of the technical areas. The six areas and their percentages are the following: (25%) Multidisciplinary Investigations: data analysis collaborations in a collection of subject matter areas. (20%) Models and Methods for Data: statistical models; methods of model building; methods of estimation and distribution based on probabilistic inference. (15%) Computing with Data: hardware systems; software systems; computational algorithms. (15%) Pedagogy: curriculum planning and approaches to teaching for elementary school, secondary school, college, graduate school, continuing education, and corporate training. (5%) Tool Evaluation: surveys of tools in use in practice, surveys of perceived needs for new tools, and studies of the processes for developing new tools.