Today, business is entirely based on data, and without data-driven major plans and decision-making, no companies will thrive. Data science is comprehensive, including data scientists and data engineers, and includes numerous job positions. The right technology can be complicated, but it can be much harder to develop the right team with the right expertise to implement big-data initiatives.
Suppose you want to join the field of data science. In that case, it is essential to consider the distinctions between a data scientist and a data engineer and see if you can switch roles without taking too much time and effort. Certification is a good choice when you are looking for a way to get an edge. Certifications measure your expertise and skills against business and vendor-specific standards to demonstrate that you have the correct know-how for employers. Get ahead by enrolling with the top-paying emerging data scientist and data engineer certification courses for today’s hottest skills.
Here the article outlines the main differences between these two positions to help you make an informed decision.
Who is a Data Engineer?
A data engineer is a specialist who prepares the data infrastructure for analytical purposes. Their tasks include data availability and different items such as stability, scaling, standards, and security.
Who is a Data Scientist?
Data scientists are the people who can draw practical lessons from large data sets to deal with particular business problems. These persons analyze vast quantities of data to produce applied mathematical models at their base.
Factors that differentiate Data Engineer and Data Scientist roles
- Role: Data engineers develop and manage a system that helps data scientists provide access and data interpretation. Generally, the task includes creating data models, data pipelines, and ETL monitoring (extract, transform, load). After data cleaning, data scientists build and train predictive models using data. They then move on their analysis to administrators and managers.
- Responsibility: The primary responsibility of data engineers is to create data pipelines so that incoming data is readily accessible to data scientists and other internal data users. As data pipelines are an essential aspect of data intake from divisive data sources and collected raw data arrives in various structured, unstructured, and semi-structured formats, even data engineers are responsible for cleaning up data.
In particular, a data engineer aims to convert the data to a functional format while “cleaning.” Data engineers are also responsible for architectural maintenance and building software solutions to improve the extraction, conversion, and loading of data into cloud-based or local database systems. These activities are usually called extraction, transformation, and loading (ETL).
The data scientist’s responsibility is to transfer the data to the next phase: determine whether the business issue or query they are searching for is a solution or an answer. A data scientist cleanses a dataset for predictive and inferential purposes to feed it to a statistical model.
- Skills: Data engineers come from a programming background at their base. Typically Python, Java, and Scala are included. Usually, these people focus on big data and distributed systems. Data engineers know the math-free tricks.
On the other hand, data scientists usually come with the computer sciences from statistics and applied mathematics. These people must also communicate with various business experts to foster the desired insights. Instead of the system, data scientists concentrate on math. Instead of running maths effectively, the system works.
- Code: Data engineers write code, as data scientists do. They are highly analytical and interested in visualizing data. Data engineers develop tools, infrastructure, applications, and facilities compared to data scientists—inspired by a mature parent, software engineering.
- Languages: The data scientists will use SPSS, R, Python, SAS, Stata, and Julia languages to develop models.
- Tools: Data engineers also work on SAP, Cassandra, Oracle, MySQL, Redis, Riak, PostgreSQL, MongoDB, neo4j, Hive, and Sqoop. Python and R are undoubtedly the most popular tools for data scientists here. When working in Data Science in Python and R, you most frequently use packages like ggplot2 to create incredible information visualizations in R or Python Data Manipulation library Pandas. Of course, when you work on data science projects like Scikit-Learn, NumPy, Matplotlib, and Statsmodels, there are several other packages available that will be helpful. In this sector, you can also find commercial SAS and SPSS are good, and further also toolboxes such as Tableau, Rapidminer, Matlab, Excel, and Gephi.
- Educational background: Data scientists and data engineers may have something in common: their computer science backgrounds. This field of study for both careers is widespread. Naturally, you can also see that data scientists have studied mathematics, operations research, econometrics, and statistics. They are also more business-friendly than data engineers. You often see that data engineers also come from engineering backgrounds.
- Job openings: The number of work vacancies for data engineers is nearly five times that of data scientists. It makes sense because most companies need more data engineers than data scientists.
- Salary: In any company, both data scientists and data engineers play a crucial role. Compared to data scientists, data engineering is not the same as media engineering. Their annual salary of data engineer is often more significant than the average data scientist: $137,000 (data engineer) versus $121,000 (data scientist).
Conclusion
Data engineers are curious, qualified problem solvers who enjoy both data and creating helpful stuff for others. However, data engineers and data scientists are involved in a team effort that converts raw data to offer the companies a competitive advantage.
There is a warning: both need a considerable amount of experience in various yet interconnected areas. Experienced software engineers would probably have an easier transition to the data engineer position — but you cannot prevent them from understanding the role of data scientists. If the applicant in data science has no advanced knowledge of various statistical models, predictive analytics, and how to perform a comprehensive analysis and reporting cycle, this gap must be narrowed by more training and hands-on experience. Regardless of which path you choose, both jobs will remain in demand soon. Start learning today interactively.