The difference between software engineering and data science


As data science has become more and more popular recently, it has continued to merge with the field of engineering and software development. It is a good confusion. Most of the professionals in each respective field have similar education, previous jobs, and even development experience. These professionals can work in the same software development company, such as BairesDev. However, their jobs are very different.

What exactly is the difference between data science and software engineering?

Why understanding the differences is important

As data science continues to gain in importance and becomes a critical value driver for all kinds of organizations, business leaders who rely on both software engineering and data science teams at the within their own business must understand how they are different and how they can work together. .

In practice, IT teams and software development vendors are typically responsible for building the tools and infrastructure that data science teams require to be successful. While the two seem similar, many IT managers approach the professionals on each team the same, which leads to incorrect assignments and assumptions and ultimately undermines each team.

To better understand the difference between software engineering and data science, it’s best to first understand what each department actually does, what their responsibilities are, and how they work within a business to be successful.

What do software engineers do?

To put it in its simplest form, software engineers and developers are creators. They read, write, test and review software and code daily. From mobile apps to websites, a developer writes the code needed to make the technology work. The job of a software engineer is to regularly check and update software to ensure that it is always performing at an optimal level.

Software engineers code for design and functionality. They create and maintain software for various purposes. These developers must be experts (or work as part of a team of experts) in front-end, back-end, user experience and beyond in order to fully develop software.

What do data scientists do?

Data scientists are responsible for developing ways to solve problems. Between extracting, cleaning, analyzing and manipulating data, data scientists spend most of their time trying to use data to help their businesses find the best information-driven business solutions. They too write code, but usually to develop programs to help them while trying to find business information.

Data scientists need to have experience with statistics and coding languages ​​(such as Python and SQL) in order to do their jobs effectively, but don’t work exclusively with coding and software development.

Understand the differences between data science and software engineering

Software engineering and data science are two fields with remotely similar requirements and work plans, but they have very different end products. It’s important to understand the differences between these areas, the skills required for each job, and how they help companies succeed as individual departments.

While there are many similarities between the two fields, there are three main differences to consider between data science and software engineering: tools, processes and methods, and skills.

  • Tools – Data scientists and software engineers use a wide variety of technologies to do their jobs as efficiently as possible. A data scientist relies on tools for data visualization, analysis, database management and analysis, predictive modeling and machine learning, to name a few tasks. These technologies can include everything from MySQL to Apache Spark and Amazon S3.

Software engineers use tools to design and analyze software, test programs, programming languages, web applications, and many other tools depending on the task at hand. For example, these tools could range from Django for back-end web development to TextWrangler and Visual Code Studio for actual code production.

  • Approaches – Data scientists and software engineers use quite different approaches to projects. Software engineers typically approach tasks within existing frameworks and methodologies. There is normally a software development lifecycle that most developers follow to keep things tidy throughout development while still allowing for adequate and thorough testing.

As a very process-oriented field, data scientists process and analyze data sets in the way that best enables them to understand a problem and ultimately arrive at a solution. The closest software development lifecycle process within data science would be the Extract, Transform, Load (ETL) process.

  • Skills – The minimum skills required to become a data scientist include machine learning, statistics, data visualization, programming, and a general willingness to learn and constantly update skills. Different positions in various companies may require a variety of other skills in addition to these.

Software engineers, on the other hand, must be able to program and code in multiple programming languages ​​while working as a team to solve problems and adapt their products to different situations.

Why is this important?

The difference between a data scientist and a software engineer is very important. If a company hired a software engineer to work on data science projects (or vice versa), it wouldn’t end well, to say the least.

Companies need to understand the requirements of the position they are hiring for and the requirements necessary for the position so that they know what type of highly regarded professional to hire. Hiring the wrong person for the job could cost the company and the person hired time, money and a lot of frustration.

Do you have any thoughts on this? Let us know below in the comments or refer the discussion to our Twitter or Facebook.

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Gordon K. Morehouse