Whenever you look into the fields of data science and software engineering, you might see two very different fields that have nothing to do with one another. Data scientists are people who know how to manage data and analyze all the numbers to find patterns and information. However, software engineers deal with the practical side of software applications, designing and testing them.
But while these two fields might seem different, they are two sides of the same coin, and they can be used to solve several problems together. Here are some of the ways that data scientists and software engineers can work together to solve the problems of the world.
Starting with some definitions
First, a data scientist is someone who uses coding, math, statistics and machine learning in order to create an impact for the organizations they are working for. They are trying to figure out how to use the latest technologies in order to create solutions and conclusions for their companies, and then they present the data in a useful form that can actually help the company and the software engineers who work with it.
Often, many data scientists follow the same few steps whenever they take on a project:
- Capture
- Prepare
- Process
- Analyze
- Communicate
All data tells a story, and often the world of data science is interpreting and then communicating that story to the leaders in the business world.
Software engineers are the ones who take that data and use math and computer science to develop computer software, developing the systems and the software that businesses will either use behind the scenes or will ship out to customers. Software engineers shape the ideas of the world into real-world solutions, and once they figure out the desired outcome of a project, they get started on building programs and systems to meet the user’s expectations.
While these two positions might seem different, a lot of the skills are the same. Both positions use math, data analysis, statistics, problem solving and communication. And often, both positions are designed to complement one another. However, while data scientists and software engineers are supposed to fully complement one another and work together, both of these individuals can run into problems that can make working together more difficult than expected.
Here are some of the largest issues that can stop data scientists and software engineers in their tracks when they are supposed to work together.
Problems data scientists and software engineers face when working together
One of the biggest problems that prevent data scientists and software engineers from fully working together is that they can tackle the same problem from different ends. For example, a software engineer has a specific problem to solve and a specific need that must be met by the program that they create. However, a data scientist often focuses on the theory and the patterns in the data, and then figures out alternative ways to use that data. Often, both sides are attempting to come up with different ways to gain information from the data, but the data scientist usually needs to focus on the theoretical side of data collection, and the developer needs to focus on solving the problem that has been presented to them.
Another issue that both data scientists and software developers face is actively taking all the data that they have access to and combing the data from many sources. If data is labeled incorrectly, not properly documented or other problems start to arise, then that can make a massive collection of data very hard to understand.
One of the best ways to have data science and software engineering complement one another is through effective communication. And while most developers have little interest in the big picture tools of data scientists, if both sides make an attempt to describe how they are approaching the data, then they can both potentially help one another solve their respective problems.
Which one is better to study?
While both data science and software engineering have several skills that overlap, often look at the same data sets and tend to work together, you might be scratching your head at which field you should try to get into. The question of data science vs. software engineering for your career path can be easily solved with one of the many courses at Baylor University that can teach you all the skills you need for either a career in data science or software engineering.Having a solid foundation in those skills can then get you into a good career in your chosen field.
No matter which one you choose to study and go into business with, you will find yourself working alongside various other fields as you seek to understand or use data. Really think about what you like to study and how you like to handle data. Do you want to learn the patterns of data and seek to interpret what data means, or do you want to use that data to create software programs that are designed to solve problems?
Final words
No matter which path you choose to go down when looking at data, data scientists and software engineers need to work together and use their respective skills to complement one another, which will benefit both companies and clients. Whether analyzing data to look at a specific need or trying to make sense of a wide range of data points, both companies and clients will need both these roles to continue to advance the data needs of the world.
As long as we continue to understand how the two fields of data science and software engineering can support one another in turning data points into actionable answers, we will be able to continue to handle the problems that countless businesses face every single day, and maybe make the world a better place too.
