If you have been coming to Data Science programs, like me, to learn about the technology in detail, then you are probably looking for a method to incorporate machine learning in your Coding Studio and become the engine that is behind the analytics and processing of data in front of your eyes. This shall require a learning philosophy, but one must understand that this studying philosophy must be pursued with firm dedication; a philosophy that talks of the intrinsic importance of developing good principles in the quest for tangible. It is here that the role of Data Engineer, along with Data Science itself is discussed.
Data Engineering
- Creating and applying algorithms to gather and process data.
- A predictive tool to help identify the relevant trends and patterns.
- A meta-tool for interpretation functions.
Data engineering is concerned with the
process of developing intelligence (verbal and non-verbal) into the data-based.
And it can be a fun and structured pursuit at the same time. It gives us a
chance to learn fast, and gather new algorithms through that process, and even
collaborate with the learning process. It can be a career that challenges your
imagination and creativity, as you develop new applications of the technology
you are developing, with the development of your analytical skills being the
main point.
Data Engineering’s Purpose in Modern Society
Data Engineering’s Role in Data Science
For me in addition to the research and
development, Data Engineering allows me to do more in my toolbox of abilities
to understand how the automation process runs and how to be part of it. Working
as a Data Engineer, I can adapt and expand the tools within the wider machine
learning technologies and provide the machine learning backend platform that
will allow me to create ML models, that would be better and impactful.
Data Engineering as a major part of Data
Science
And in general, Data Engineering will
support the entire Data Science process, that is to support the development of
machine learning models, that will later be implemented in front of your eyes
to be able to see the real impact of machine learning across.
It also can act as a major factor when
the development of new features or solutions are implemented by the Data
Scientist; improving and maximizing the performance of the systems.
More importantly, being a Data Engineer,
I have to work in a domain area where the need for machine learning models are
being tested. Machine learning models are undergoing a process of discovery and
production within the development and transformation processes. But working
with data engineers can help support the data science teams, increase the
quality of the work being produced, and ultimately gives us a major impetus to
help us develop new technologies.
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