Data Scientist vs Data Engineer

Updated April 28, 2023

Difference Between Data Scientist and Data Engineer

The number of job opportunities available for data engineers is approximately five times more than that for data scientists. Data Scientist and Data Engineer are two tracks in Bigdata. Generally, Data Scientist performs analysis of data by applying statistics and machine learning to solve critical business issues. In short, they do advanced data analysis driven and automated by machine learning and computer science. Data engineers, on the other hand, are software engineers who design, build, and integrate data from various resources and manage big data. And also prepare extensive data infrastructure to be analyzed by Data Scientists.

Key Differences Between Data Scientist vs. Data Engineer

The difference between Data Scientist and Data Engineer is as follows:

Basis for Comparision	Data Scientist	Data Engineer
Responsibilities	Data Scientists to answer industry and business questions will conduct research. They also use vast volumes of data from external and internal sources to answer that business. Data Scientists also use the most developed machine learning analytics programs and statistical methods to prepare data for prescriptive and predictive modeling. Explore and examine data to find hidden patterns. Automate work through the use of predictive and prescriptive analytics. Tell stories to key stakeholders based on their analysis.	Discover opportunities for data acquisition. Data Engineers also Develop, test, construct and maintain architectures Ensure Architecture will support the requirements of a business. For data modeling, mining, and production, they  Develop dataset processes. Data Engineers also employ various languages and tools (e.g., Scripting languages) to combine systems. To improve data efficiency, reliability, and quality, they also suggest some ways to do that.
Job Outlook	The Data Scientist role has been in demand since the start of the hype. But these days, companies are looking to have data science teams rather than prefer unicorn data scientists that possess creativity, communication skills, curiosity, cleverness, technical expertise, etc. For recruiters, it’s hard to find someone with the qualities that companies seek, and the demand exceeds the supply. So, we can tell that the Data Scientist bubble will soon burst.	Data flows will need to be replaced and redirected in the future. As a result, the center of interest is on, and the number of job postings to hire Data Engineers has gradually increased.
Need to Develop Knowledge and Expertise	Data Scientists need to be experts in communicating and presenting the results of their analysis.	Data Engineers need to be expertise in system monitoring and data Cleaning.

Comparison Table

Mentioned below is the comparison table :

Basis for Comparision	Data Scientist	Data Engineer
Tools	They use tools like Matlab, SAS, Jupyter, and RStudio.	They use tools like Oracle, Hadoop, MySQL, Hive, DashDB, MongoDB, and Cassandra.
They Work on	They work on Data Analysis, Statistics, Machine learning, Data Mining, Research, Statistical modeling, Algorithms, and Programming.	They work on Data Warehousing, ETL, Databases, and Business Intelligence.
Languages	They are very familiar with R, Python, LaTeX, etc languages.	They are very familiar with Java, Unix, JavaScript, Linux, SQL, etc languages.
Salaries	In a medium market, they will earn a minimum of $43k and a maximum of $364k.	Data Engineers in a Medium market will earn a minimum of $34k and a maximum of $341k.
Hired By	They get hired by Dropbox, Microsoft, Walmart, etc.	They get hired by Verizon, Bloomberg, Play station, etc.
Tasks Performed	Understanding data Generating features Extracting patterns from data Modeling and visualizing data to get new insights Communicating and explaining these new findings	Data Scientists will gather data from different sources Tidying data and storing it in the best formats ETL tasks Creating data pipelines Monitoring data collection, storage, and retrieval processes
Educational Background	Data Scientists are from computer science backgrounds and often study Econometrics, Mathematics, Statistics, and Operational Research.	Data Engineers are also from a Computer Science background and also Computer Engineering.

Head-to-Head Comparison Between Data Scientist vs. Data Engineer (Infographics)

Below are the top 7 comparisons between Data Scientist vs. Data Engineer:

Data Scientist vs. Data Engineer Working Together

Both skill sets (Difference Between Data Scientist vs. Data Engineer) are critical for the data team to function correctly. It is demanding that we will be able to land a unicorn, a single individual with skills as Data Scientist and Data Engineer. Therefore, we must build a team where each member complements the other member’s skills. And they must work well by being together.

Recognizing their complementary roles in our business enterprise is essential to avoid this situation or dilemma. It is impossible to overstate how important the communication between a Data Scientist and Data Engineer is and how important it is to ensure that both Data Scientist and Data Engineering roles and teams are well-resourced and imagined. This is because data needs to be optimized to the use case of the Data Scientist. A clear understanding of how this works is essential in reducing the human error component of the data pipeline.

Failing to prepare adequately for this can doom our enterprise’s efforts. We need to eliminate the situation where Data Scientists are onboard without a data pipeline sufficiently done. This leaves them in the uncomfortable and expensive position of being forced to dig into the hardcode Data Engineering needed or remaining idle. Neither option is a good use of their capabilities or our enterprise’s resources.

Conclusion

In conclusion, both work together on the data. And they both are needed as finding all skills in a particular individual is difficult, so data scientists and data engineers must complement each other to work effectively for the Business Enterprise. Because Data Scientist’s worries about data pipelines are less productive, and Data Engineers’ concerns about business insights are less effective. By combining both, they work well.

Recommended Articles

This has been a guide to Data Scientist vs. Data Engineer. Here we have discussed Data Scientist vs. Data Engineer head-to-head comparison, key differences, infographics, and a comparison table. You may also look at the following articles to learn more –

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