Comparing DevOps Engineers to Data Scientists
DevOps engineers create the software customers download straight from the Internet. Data scientists, however, design algorithms for companies to use with their data.
|Job Title||Educational Requirements||Median Salary (2017)*||Job Growth (2016-2026)**|
|DevOps Engineers||Bachelor's Degree||$91,230||24% (Software Developers)|
|Data Scientists||Master's Degree||$91,504 (Data Scientist/Engineer)||19% (Computer and Information Research Scientists)|
Sources: *PayScale, **U.S. Bureau of Labor Statistics
Responsibilities of DevOps Engineers vs. Data Scientists
Both DevOps engineers and data scientists develop automated software that works with little input from the user. DevOps engineers work on development teams to design everything from computer systems to online video games. Data scientists make sense of raw data collected by businesses, including customer information, by finding patterns.
These tech professionals bring together teams who create software with teams who release it to the world and keep it working efficiently. The software they develop includes cloud systems that manage documents online, as well as other web-based applications. With a focus on continuous delivery, these engineers offer automatic updates to customers. Then, they collect logins and other data to determine how the patches and versions have affected the user experience. Additionally, these engineers are charged with automating deployment operations that make it possible for customers to purchase and download the products online, without the need for a software disk.
Job responsibilities of a DevOps engineer include:
- Understanding and using computer code, such as Java
- Developing smaller modules within an application to allow for faster debugging and release
- Creating automated security checks
- Designing automated systems to test new programming codes as the product is developed
Data scientists collect medical information, shipping details, and even webpage traffic information from across systems. This includes unstructured data that is not part of an established database. Their goal is to use machine learning techniques that allow the computer to find and recognize meaningful patterns. This includes building algorithms using SAS and Python programming languages. For data scientists, the first step is meeting with clients and determining what information is necessary to analyze in order to help clients make informed business decisions. Additionally, they find ways to sort data, making it easier for organizations to search for specific information. These professionals may also update a client's hardware to allow for more space to store data.
Job responsibilities of a data scientist include:
- Testing algorithms to ensure accuracy
- Cleaning the data to remove repetition or irrelevant data
- Applying statistical techniques, such as distribution, to data interpretation
- Designing visual representations of findings to share with business executives
If a career as a DevOps engineer interests you, perhaps a future as a computer programmer could too, especially since both have deep knowledge of coding. On the other hand, if you're serious about a career as a data scientist, you could also research a position as a data modeler, as both make sense of large data sets.