In the data science companies, data scientists and data engineers are the top job titles that decide the success of any project. These professionals are relying on data analytics certification to manage big data and AI projects in the present Data Science industry. There are many different sources of confusion related to how these two types of people in the organization work in a team.
In this article, we have tried to point out a few similarities and differences between the work accomplished by data scientists and data engineers, and how they work together in a team.
What data science is all about?
Data science is the advanced scientific field of information extraction, analysis, and reporting of analytics using programming techniques, tools and theories. In data science, you will find a classic inter-relationship between coding, software development, people management, and predictive intelligence coming together to apply data across a wide range of disciplines and domains.
Data engineering is just one of the techniques that are present in the data science ecosystem.
Today, thanks to the rapid rise of Big Data, data engineering, and AI models, data scientists are able to apply their techniques to build strong data analytics platforms. These platforms are handled by analysts with data analytics certifications dealing with computer science, data architecture, storage, statistics, mathematics, and business intelligence.
Due to the immense influx of data and demand for analytics, we are able to market a lot of buzzwords like Big Data, Deep Learning, and Automation machine learning to promote the data science developments as the fourth pillar of the Industrial Revolution.
Between 2015 and 2021, the population of data science professionals with certifications increased to 1 million, and these are mostly professionals with solid experience and technical skills in programming languages, logical reasoning, and advanced mathematics. That’s why, even today, top data scientists are picked from these specializations with certifications from top grade data analytics courses.
What data engineering is all about?
Now, let us talk a little about data engineering. As we mentioned before, data engineering can be considered as a subset of data science that involves hundreds of different activities such as data mining, modeling, analytics, and visualization. Data engineering majorly requires computing and database management skills as demonstrated during the ETL processes of data extraction, transformation, and loading.
Let’s understand this better.
Now, there are different actions involved in engineering with data or information. The first step would be mining or collecting data from a source. Then a series of advanced data science techniques would be involved for data ingestion, data extrusion, and data manipulation before pushing it into the next funnel of data engineering.
Overall, the job of data engineering is to feed data into the pipeline of machine learning engineers who create semantic data models for training and refining data for their algorithms, also called as data labeling and tagging.
These days, data engineering doesn’t necessarily report to the traditional IT heads of departments, but could even report to the Head of Marketing and Sales, Revenue generation, or Finance Heads. This shows that data engineering is an important cog in the wheel of business intelligence used in different departments based on their organizational targets and requirements.
The similarities between data engineering and data science are that both are part of the same family, and the job titles that use these benefit from learning the skills involved in these specializations. However, from the hierarchy point of view in the organization, data engineers report to a data scientist. These data scientists are responsible for creating a detailed roadmap on the ETL and the tools they can give to engineers to handle a wide range of data types and sources.
In proper context, you are more likely to develop and utilize the data science and machine learning techniques in your engineering roles than as a data scientist. It’s only when you start deriving the analytics out of these AI ML models that you really begin to see how different engineering is from larger data science outcomes.
Big Data: The Biggest Influencers of Data Science
Companies are becoming profitable by hiring and training data engineers. Data science and engineering are coming closer to each other as part of the data analytics certification. In an era where 90% of the organizations are either data-driven or planning to become a data driven companies, statistics reveal that these organizations are training their existing workforce in data management teams to take on data scientist roles.
