What does a Datascience Engineer Do?
What is Datascience?
Photo by Boitumelo Phetla on Unsplash
I think Coursera has a pretty fair definition on this.
Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret.
Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.
Buildings systems to manage Data
Make data accessible to organizations to improve
Think about it, fields like machine learning and deep learning can’t succeed without data engineers to process and channel that data. Datascience engineers are the foot soldiers in a sense in the A.I. revolution of the 4th industrial revolution.
Around 2019 hype around Datascience appears to have peaked.
For instance perhaps people you know jumped into the field. Data science master’s degrees started to become increasingly popular, and there was no dearth of online courses on the Internet. Students flocked to sites like Coursera, Datacamp, and Udemy to get data science certifications and enter the job market.
So what’s going on in 2022?
Companies need data engineers. They need people who are able to take large amounts of data and make it usable.
Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals.
These are some common tasks you might perform when working with data:
Acquire datasets that align with business needs
Develop algorithms to transform data into useful, actionable information
Build, test, and maintain database pipeline architectures
Collaborate with management to understand company objectives
Create new data validation methods and data analysis tools
Ensure compliance with data governance and security policies
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Does a Data Science Engineer work with AI?
Another way to put it is that Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value.
The term “Data Scientists” is maybe only around 15 years old. The term “data scientist” was coined as recently as 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data.
Data science is one of the most popular career choices for technically inclined college graduates, and working in the data science industry requires strong coding skills. Data scientists use artificial intelligence, or machine learning, algorithms to detect patterns in large sets of data.
Is Data Science Still in Demand?
Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions.
These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies. In 2022, demand for Data Science engineers appears to still be strong.
Since AI is being implemented in the 2020s at scale, the need for Data Science graduates will remain strong. Data science reveals trends and produces insights that businesses can use to make better decisions and create more innovative products and services. Perhaps most importantly, it enables machine learning (ML) models to learn from the vast amounts of data being fed to them, rather than mainly relying upon business analysts to see what they can discover from the data.
Personality and Skills of a Data Science?
Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts.
Curious
Results-orientated
Industry specific terminology and soft skills
They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.
They must also be able to utilize key technical tools and skills, including:
R
Python
Apache Hadoop
MapReduce
Apache Spark
NoSQL databases
Cloud computing
D3
Apache Pig
Tableau
iPython notebooks
GitHub
Hype Due to Demand
The reason data engineering is so hyped up now is because companies don’t have enough of them.
With the Great Resignation and labor talent shortages along with early retirements in the U.S., demand appears very strong again in 2022.
In recent history, Glassdoor ranked data scientist among the top three jobs in America since 2016. As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists.
The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions. The Cloud and the amount of data growing each year for business value implies datascience engineers are greatly needed.
LinkedIn listed data scientist as one of the most promising jobs in 2021, along with multiple data-science-related skills as the most in-demand by companies.
Difference Between Datascience, AI and Machine Learning
According to Oracle, there’s an easy way to think of the differences between Datascience, AI and so forth.
Here’s a simple breakdown:
AI means getting a computer to mimic human behavior in some way.
Data science is a subset of AI, and it refers more to the overlapping areas of statistics, scientific methods, and data analysis—all of which are used to extract meaning and insights from data..
Machine learning is another subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
And for good measure, we’ll throw in another definition.Deep learning which is a subset of machine learning that enables computers to solve more complex problems.
So where does Data Science fit into the hierarchy of business?
Who oversees the data science process?
At most organizations, data science projects are typically overseen by three types of managers:
Business managers: These managers work with the data science team to define the problem and develop a strategy for analysis. They may be the head of a line of business, such as marketing, finance, or sales, and have a data science team reporting to them. They work closely with the data science and IT managers to ensure that projects are delivered.
IT managers: Senior IT managers are responsible for the infrastructure and architecture that will support data science operations. They are continually monitoring operations and resource usage to ensure that data science teams operate efficiently and securely. They may also be responsible for building and updating IT environments for data science teams.
Data science managers: These managers oversee the data science team and their day-to-day work. They are team builders who can balance team development with project planning and monitoring.
But the most important player in this process is the data scientist.
While automation is impacting datascience in 2022 the heavy lifting still needs to be done by the data scientist, and 80% of the tasks that data scientists generally do cannot be automated.
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