The impact of automation frameworks on today's data science competencies
Authors: Potanin Maria , Holtkemper Maike , Beecks Christian 
Published: July 2023

The digital transformation has led to a rapid advancement of today's
working environment. The increasing demand for AI solutions, especially in
skilled professions, is deepening the relationships between humans and smart
machines. These new relationships require an evolution and transformation of
the skills of today's workforce. Furthermore, technologies can be used to
close potential skills gaps and thus make the interaction and collaboration
between human and technology work in the best conceivable way. Among the
many digital competences required in today's working environment, data
scientific competencies like data-preprocessing, feature-engineering and
Model-generation are essential in order to cope and analyze small-to-big
data sets arising in various data spaces. These Data Science competencies
combine statistics, computer science and expert knowledge to gain insights
from data and to develop a data-driven solution. With the amount of data
increasing every day, there is a growing demand for subject matter experts
with Data Science skills to help companies to be competitive by making
data-based decisions, identify future trends, identify patterns, optimize
processes, or develop new products and services. The competencies needed to
accomplish these tasks are as multidisciplinary as the field of Data Science
itself. Starting with the ability to write in multiple programming
languages, to apply mathematical and statistical concepts, to practice
machine learning algorithms and techniques, to the ability to understand
industry-specific requirements, to critically question and visualize their
results, to the ability to work in a team and to interact socially. These
various requirements are met by employees, each of whom is not capable of
handling such a wide range of competencies. Since the combination of these
competencies is in high demand on the job market, but the small number of
high talented employees cannot meet this demand, intelligent solutions are
being sought that can replace some of these competencies. The goal of this
paper is therefore to investigate whether AI-based automation frameworks
have Data Science competencies in their design. It serves as an impulse for
further discourse on whether AI-based automation frameworks can empower
humans in acquiring Data Science competencies. For this purpose, a total of
four automation frameworks out of 17 were selected in an initial analysis,
which were most indicative of the following characteristics: automation,
flexibility, ease of use and interoperability. These are AutoPrep, Auto
Gluon, AutoClust and DeepEye. Based on the EDISON Data Science Framework,
these research papers were coded by means of a qualitative content analysis
and the obtained data were subsequently analyzed and discussed. Initial
results show that by using automation frameworks, needed skills for applying
Data Science competencies can be made available to companies and workers in
the short term. This concerns components from the EDISON competence groups
Data Management, Data Analysis, Data Modeling and Communication.