The difference between software engineering and data science
Companies must understand the requirements of the position that they are hiring for and the requirements necessary for the job.
As data science has become more and more popular recently, it has continued to get confused with the field of software engineering and development. This is a fair confusion. Most of the professionals within each respective field have similar educational backgrounds, previous jobs, and even development experience. These professionals may work at the same software development company, such as BairesDev. However, their jobs are quite different.
What exactly is the difference between data science and software engineering?
Why Understanding the Differences Matters
As data science continues to grow in importance and becomes a critical driver of value for all kinds of organizations, business leaders who rely on both software engineering and data science teams within their own companies should understand how they differ as well as how they can work together.
In practice, IT teams and software development providers are typically responsible for creating the tools and infrastructure required by data science teams in order to be successful. Although the two seem similar, many IT leaders approach professionals in each team in the same way, which leads to misguided assignments and assumptions, and ultimately undermines each team.
To better grasp the difference between software engineering and data science, it is best to first understand what each department really does, what their responsibilities include, and how they work within a business to see success.
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 on a daily basis. From mobile applications to websites, a developer writes the code necessary to make technology work. A software engineer’s job is to continuously check and update the software regularly to ensure that it is always performing at the optimal level.
Software engineers code for the purpose of design and functionality. They create and maintain software for a number of different purposes. These developers must be experts in (or work within a team of experts in) front-end, back-end, user experience, and beyond in order to fully develop a piece of 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 company find the best information-backed business solutions. They too write code, but usually to develop programs to assist them while trying to find business insights.
Data scientists must have experience in statistics and coding languages (such as Python and SQL) in order to effectively do their jobs, but do not exclusively work with coding and software development.
Understanding the Differences Between Data Science and Software Engineering
Software engineering and data science are two fields with similar-looking requirements and job blueprints from afar, but they have very different end products. It is important to understand the differences between these fields, the skills required for each job, and how they help businesses succeed as individual departments.
Although there are many similarities across the two fields, there are three main differences to consider between data science and software engineering: tools, processes and methods, and skills.
- Tools – Both data scientists and software engineers use a wide variety of technologies to do their jobs as efficiently and effectively as possible. A data scientist relies on tools for data visualization, analytics, database management and analysis, predictive modeling, and machine learning, just to name a few tasks. These technologies can include everything from MySQL to Apache Spark and Amazon S3.
Software engineers utilize tools for designing and analyzing software, testing programs, programming languages, web apps, and many other tools depending on the task at hand. For example, these tools can 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 rather different approaches to projects. Software engineers typically approach tasks within existing frameworks and methodologies. There is normally a software development life cycle that most devs follow to keep things in order throughout development whilst allowing for adequate and thorough testing.
As a very process-oriented field, data scientists process and analyze data sets in a way that best allows them to understand a problem and ultimately arrive at a solution. The closest process to the software development life cycle 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 constantly be learning and updating one’s skill set. Different positions at 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 within a team to problem-solve issues and adapting their products to different situations.
Why Does It Matter?
The difference between a data scientist and a software engineer matters quite a bit. If a company were to hire a software engineer to work on data science projects (or vice versa), it would not end well, to say the least.
Companies must understand the requirements of the position that they are hiring for and the requirements necessary for the job in order to know which kind of highly esteemed professional to hire. Hiring the wrong person for the job could cost a company and the hired person time, money, and quite a bit of frustration.
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