Difference Between Data Scientist vs Data Engineer

Before directly jumping into the differences between Data Scientist vs Data Engineer, first, we will know what actually those terms refer to.

Data Scientist and Data Engineer are two tracks in Bigdata. Generally, Data Scientist performs analysis on data by applying statistics, machine learning to solve the critical business issues. In short, they do an advanced level of data analysis that is driven and automated by machine learning and computer science. Data Engineer, on the other hand, are software engineers who design, build, integrate data from various resources and manage big data. And also, they prepare big data infrastructure to be analyzed by Data Scientists.

Head to Head Comparison Between Data Scientist and Data Engineer (Infographics)

Below is the top 7 Comparison Between Data Scientist and Data Engineer:

Key Differences Between Data Scientist and Data Engineer

Following is the difference between Data Scientist and Data Engineer are as follows:

Basis for Comparision	Data Scientist	Data Engineer
Responsibilities	Data Scientists to answer industry and business questions, they will Conduct research. They also take advantage of huge volumes of data from external and internal sources in order to answer that business. Data Scientists also use most developed machine learning analytics programs, and statistical methods to prepare data for use in 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 a wide range of languages and tools (e.g. Scripting languages) in order to combine systems together. 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 hype But during these days companies are looking to have data science teams rather than preferring to unicorn data scientists that possess creativity, communication skills, curiosity, cleverness, technical expertise, etc. For recruiters, it’s hard to find the person, who is having those qualities that companies looking for and the demand clearly exceeds the supply. So, we can tell that in near future Data Scientist bubble will 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 raised throughout the years.
Need to develop Knowledge and Expertise	Data Scientists need to be experts in communicating and presenting the results of an analysis they have done.	Data Engineers need to be expertise in system monitoring and data Cleaning.

Data Scientist and Data Engineer Comparison Table

Below is the comparison table between Data Scientist and Data Engineer

Basis for Comparision	Data Scientist	Data Engineer
Tools	They use tools like Matlab, SAS, Jupyter, RStudio	They use tools like Oracle, Hadoop, MySQL, Hive, DashDB, MongoDB, Cassandra
They Work on	They work on Data Analysis, Statistics, Machine learning, Data Mining, Research, Statistical modeling, Algorithms, Programming	They work on Data Warehousing, ETL, Databases, 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	They in a medium market they will earn a minimum of $43k and a maximum of $364k	Data Engineer in a Medium market they 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 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 also they often studied Econometrics, Mathematics, Statistics and Operational Research.	Data Engineers are also from Computer Science background and also Computer Engineering.

Data Scientist vs Data Engineer working together

Both skillsets (Difference Between Data Scientist vs Data Engineer) are critical for the data team to function properly. It is highly difficult that we will be able to land a unicorn a single individual who is having skills as Data Scientist and Data Engineer. Therefore, we will need to build a team, where each member complements the other member’s skills. And it is critical that they work well by being together.

In order to avoid this situation or dilemma, it is important to recognize the different complementary roles that they both are playing in our business enterprise. It is impossible to overstate not only how important the communication between a Data Scientist and Data Engineer is, but also 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. Having a clear understanding of how this works is important in reducing the human error component of the data pipeline.

Failing to prepare adequately for this from the starting, can doom our enterprise’s efforts We need to get rid of the situation, where Data Scientists are onboard without a data pipeline being sufficiently done. This leaves them in the uncomfortable and expensive position of either 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 in order to work effectively for the Business Enterprise. Because Data Scientists worries about data pipeline are less productive and Data Engineer worries about business insights are less productive. By combining both, they definitely work well.

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