Analytics and Data Science Standardization and Assessment Framework

Monday, April 27, 2020

Hosted by
Greg M. and Liana Y.


Zoom meeting:

Streaming will be available.

7:00 talk introduction
7:10 speaker starts
8:20 – 8:45 speaker finishes

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 . 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, Inc., IADSS – Co-Founder.

Usama is Co-Founder & CTO at OODA Health, Inc a VC-funded company founded in 2017 to bring AI/automation to create a retail-like experience in payments and processing to healthcare delivery. He is also Chairman at Open Insights – a technology and strategic consulting firm he founded in 2008 to help enterprises deploy data-driven solutions to grow revenue from Data assets. In addition to BigData strategy and building new business models on data assets, the company deploys data science, AI/ML, and bigData solutions for large enterprises.

Usama has published over 100 technical articles on data mining, data science, AI/ML, and databases. He holds over 30 patents and is a Fellow of both the AAAI and the ACM. Usama earned his PhD in Engineering in AI and Machine Learning from the University of Michigan. Ann Arbor. He has edited two influential books on data mining. See also

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