From a report by the Bureau of Labor Statistics, the demand for personnel working in the data science industry increased by 20% in 2024. Two of the most prominent jobs are data scientist and data engineer.
Although both are in high demand with attractive salaries, many people do not know the fundamental differences between these two professions.
If you are one of them, this article will help you. Let’s read on to discover!
The data scientists will perform jobs such as analysis and statistics. After analysis, they need to explain the applicability of the results in practice.
These people must also make predictions and descriptions based on the analyzed dataset.
With research for business purposes, data scientists will have to examine and uncover hidden patterns within the database. From there, they conclude for company development.
The main tools of data scientists are the Python programming language and the statistical software SPSS.
In addition, those who do this job also need to be proficient in charting and visualization software. Some popular languages that scientists frequently use are SAS, SPSS, R, Python, and other data visualization.
They combine these best to build and control the ideal model. These tools and software also help them accelerate data-related work.
Data engineers build systems that aggregate, store, and export data from a number of apps and systems created by software engineers.
Data engineers possess a skill niche of software engineers. 40% of data engineers are initially software engineers, and this is one of the most common career development directions.
In addition to the above duties, these people must also be proficient in using big database technology. Using this technology, they will build pipelines instead of just writing complex queries.
They will have to use many processing tools such as MySQL, PostgreSQL, and Redis to accomplish these tasks well.
With the definitions above, you are probably wondering, why don’t data scientists take on the role of data engineers?
With the increasingly strong development of the data science industry, data scientists and engineers will have their tasks and workloads.
|Analyze datasets and provide forecasts.
|Build the freelance pipeline.
Database processing algorithms.
Programming languages and statistical software.
|Programming languages and statistical software.
Build a big warehouse.
Good math and applied math background.
The primary task of data scientists is to analyze and draw applicable conclusions from the prepared database.
To reach scientific conclusions, those doing this work must constantly hypothesize, predict, test, analyze, and visualize databases to prove hypotheses.
Because of the ability to make scientific predictions, people who do this job meet with business leaders to discuss measures to help improve the business.
In a nutshell, the main task of data scientists is to make interpretations and inferences from the data engineers provide.
Meanwhile, the data engineers will focus on collecting raw databases. The dataset they collect will come from many different sources, so they are also responsible for optimizing the infrastructure.
They will spend a lot of time building the freelance pipeline. In addition, in some cases, they are also responsible for maintaining the upper channel to ensure that it is easily accessible to users at any time.
Bachelors, who study computer science programs or fields related to math, statistics, and information systems, can pursue these two careers. However, each profession requires specific skills.
Data scientists need a technical understanding of big infrastructure. In addition, they also need to know the algorithms to handle sets in different formats.
Finally, those who do this job need to be fluent in programming languages and statistical software such as Python, SQL, R, and Java.
The data scientists and engineers must also be fluent in the above programming languages. In addition, these people need to be familiar with building big warehouses, with some basic operations like extraction and ETL.
Finally, it is a tremendous advantage if data engineers have a good background in mathematics and applied mathematics. Especially if you have taken courses related to statistics, you will be better at solving business problems.
There is not much difference in the salaries of these two occupations. With data science, the average salary will be around $124,540 a year.
Meanwhile, an average data engineer will earn about $120,813. Of course, the salary will largely depend on your qualifications and experience.
A senior’s income is always 2-3 times better than a fresher’s. At higher levels, the number of salaries is unlimited.
Data science is your job if you are an analytical thinker who can predict trends.
Pursuing this career, you will often perform tests, perform complex statistical analyses, and write machine learning algorithms.
If you are interested in the above jobs, career development in the direction of a data scientist will be proper for you.
As a data engineer, you must build a database that stores and organizes data.
Therefore, this work will require exploration and process improvement to build a system that saves more time and resources.
If you are always ready to learn and improve and are knowledgeable about the latest tools and technologies, a data engineer is for you.
We have tried to point out the differences between each profession; however, the answer depends on you.
After distinguishing the requirements of each job, you need to make a choice that suits your abilities and long-term career development orientation.
If you have further questions, do not forget to contact us via email to receive an accurate answer. Thank you for reading!