Data science and test automation are two areas of software development where demand is high for qualified engineers. Both domains require a very similar set of skills. This article examines which field could be the best for a young software engineer career.
Author: Ron Evan
Data science is among the most exciting careers for statisticians and computer scientists today. It is one of those careers that did not exist or seem important a few years ago, yet it is playing a major role in the success of many businesses today.
Data scientists are in demand across the world and companies are paying huge amounts of money to have the best of them (data scientists) in their workforce.
Similarly, due to advancements in technology, software testers are very essential in the business world today. They test the performance, security, reliability, and functionality of software applications.
The main job of a software tester and full stack developer is to determine whether a software application does what it is supposed to do and meets both its users and business requirements.
Technology has automated software testing, making it more accurate and better compared to manually testing the applications. An automation testing course can provide the necessary skills to excel in this evolving field.
Automated testing deals majorly with data, something that makes some people confuse a career in automated testing with one in data science. That notwithstanding, is data science or automated testing better for your long-term career?
Data Science and Automation Testing
Modern software testing involves the use of automated software testing tools and data to see if there are any bugs in software applications.
On the other hand, the demand for data scientists has been growing exponentially, meaning that there are more jobs in the market every day.
As a software tester with experience in data manipulation, you cannot ignore the role played by data science today. This is because of several reasons, among them;
Data is Used Everywhere
Data science can be defined as a field that is used in almost all other industries to make businesses better and improve their performance. Due to the work involved, it gets quite difficult for a person to get data in a format that best suits its analysis.
Through data science, however, businesses can come up with assumptions and predictions on what the data they are working with might look like.
When doing this, they need automation testing to check these predictions and assumptions to see if they meet expectations.
Since automation testing can be done using different tools, it means that testers might get to a point where they are no longer needed. This makes a career in data science better when looking at the long-term benefits that it brings.
Testing of Software Applications is Driven by Data
When testing software applications, whether through automated or manual testing, you are required to use different sets of data to see how the application performs and evaluate whether it meets all its expectations.
This means that automated testing professionals know and understand different automation testing metrics, how to manipulate data, and work with it when testing software applications.
In addition, it also means that they would not find it difficult if they wanted to switch careers to become data scientists.
Due to the increasing demand for data scientists because of businesses’ overreliance on data, a career in data science makes more sense in the long run compared to one in automation testing.
Data Science and Testing Skills Overlap
When testing a software application, testers are required to think critically, doubt the applications they are testing, and find ways that can break it down. They are supposed to look for issues that might be hidden and that might fail the application.
Similarly, data scientists are critical thinkers who work with large sets of data, manipulating it to meet different requirements. They are supposed to find out about the different ways through which businesses can benefit from the data that they are working on.
Looking at the programming languages used in data science such as Python and R, they are also used when writing tests and code for debugging issues.
However, looking at the long-term benefit of both careers, a data scientist sits at a better place since what they do remains in demand even through innovation and automation of testing techniques.
Conclusion
Today, almost every other company is investing heavily in data science, making it very difficult to ignore this field. Due to this, the role played by testers is gradually changing, something that is making most of them move their careers towards data science.
As technology advances, we are going to see most of the work done by testers taken over by automation tools, meaning that a career in data science is better in the long run.