What is “data science” and who is a “data scientist”? Data engineer? Analytics professional?
Recent years have seen a surge of interest in all things "data.” Industries have been transformed and entirely new business models conceived with the growing capability to collect, process, and understand data and operationalize data-driven products, features, and services.
Leading this transformation is a set of professional disciplines that draw on the previous generation of applied statistics, management science and computer science to unlock the value in massive data stores now available to businesses and governments worldwide. Collectively, these disciplines have been referred to as “analytics” and “data science.”
The demand for analytics and data science skills parallels the growth of interest and investment in data. Some estimates of future demand for professionals with analytics and data science skills now exceed 2-3 million in the US alone, and related degree programs number in the hundreds (Markow et al., 2017). However, the explosive growth surrounding data science has left in its wake a state of confusion on the basic definitions of related tools, methods, skills, and roles. The definition of “data science” itself is no exception. Some “data scientists” are machine learning algorithm experts, while others specialize in developing and maintaining data infrastructure. Yet another camp of “data scientists” are business-facing data strategists. The backgrounds, skills, abilities and professional relevance of these individuals vary greatly by project and by position.
The confusion stems not only from "which" skills make an analytics or data science professional; but also from “what” level of skill and knowledge qualify professionals for the various titles. Should practitioners hold advanced degrees to call themselves a scientist, or would several online courses suffice? Who is a "senior scientist" - a title that would be, in other science disciplines, only reserved for experienced scholars?
Although "data scientist" has emerged as a job title; every industry, function, and business appears to be looking for their definition of the role. This confusion leads to substantial costs for employers and professionals alike. Employers have to understand a vague taxonomy of different "data scientists" in order to find the right talents for their business needs, evaluate them, appraise their contributions, and retain them. Employees have to navigate a sea of "data science" positions, only to find that they lack a significant area of knowledge required for most of the roles advertised. We list examples of a list of titles that require skills in the analytics and data science spheres.
Speaker: Usama Fayyad, Open Insights & OODA Health
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