Data Science vs Software Engineering: 8 Important Differences

Key Differences: Data Science vs Software Engineering

Data Science and Software Engineering both involve programming skills. The difference is that Data Science is more concerned with gathering and analyzing data, whereas Software Engineering focuses more on developing applications, features, and functionality for end-users. 

Software Engineer vs Data Scientist Quick Facts

	Software Engineer	Data Scientist
Median Annual Salary, 2018*	$105,590	$118,370
Required Education	Bachelor’s Degree Coding Bootcamp	Bachelor’s Degree Data Science Bootcamp
Job Outlook, 2018-28*	21% growth	16% growth

*Retrieved from the most recent BLS data available on Data Scientists and Software Engineers.

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When you first begin your research in the tech field, you’re likely to come across a vast array of different specialties and careers. Two of the most popular options are data science and software engineering. Both of these fields come with their own sets of pros and cons, and between the two are a number of similarities and differences. 

Although they’re both segments of the tech industry, they’re definitely two very different paths to go down. If you want to decide which is the better fit for your skills and interests, this Career Karma article should help to clear up any confusion.

What is Data Science?

Data science is notoriously hard to define exactly, but you could think of it as “the use of algorithms and statistics to draw insights from structured and unstructured data”.  The goal of a data scientist is going to depend quite a lot on the problem they’re examining. In the context of business, a data scientist might be measuring the impact of changes in promotional material; in finance, a data scientist is probably trying to discover what (if anything) accurately predicts returns in one of the major markets.

I know people building machine-vision applications to read handwritten math equations, chatbots that offer financial advice, and systems to recommend medical-grade Cannabis to treat a variety of ailments. 

The more skilled data scientists might also be tasked with creating new algorithms and frameworks for processing data sets.

It is a valuable, growing field that offers plenty of opportunities to those with the right skills and experience.

What is Software Engineering?

Software engineering is another one of the major divisions of the tech industry. Software engineering has a fairly apt and self-explanatory title: it involves using programming and engineering skills to develop new software. In software development, the goal is to create new programs, applications, systems, and even video games. Because there’s no such thing as bug-free software, an inescapable secondary goal for software engineers is to constantly patch and iterate on existing software to make it better and ensure it performs as required.

Software engineering is also a highly-valued field, and the benefits of a good software engineering skillset are well known. If you have software development chops, you’re sure to find someone out there who would want to utilize them.

Data Science vs Software Engineering: Differences

While both are highly technical fields, and while both have similar skill sets, there are big differences in the ways in which these skills are usually applied. So, let’s compare data science vs software engineering to find the most important differences.

For the most part, a data scientist uses their skills to sift through data, interpret it in meaningful ways, find patterns, and use what they’ve learned to help a business make a decision or learn how to be more efficient.

To put it another way, data science tends to be much more about analysis in practice, with some aspects of programming and development thrown in. 

Software engineering, on the other hand, tends to focus on creating systems and software that is user-friendly and that serves a specific purpose. It’s not uncommon for there to be a heavy analytical component to this process, so it’s easy to see how the two fields overlap.

An example: If you’re reading this article on the Google Chrome browser on your phone, it’s a safe bet that a team of software engineers developed it and continue to support it to ensure that it works well with your new phone or after updates.

Now let’s say you use Chrome to search “Best Data Science Bootcamps”. You’ll see a listing of results relevant to your search, and there’s a good chance you’ll find exactly the type of article you’re looking for within the first few links. 

This is achieved through the power of a data science algorithm.

The algorithm is able to sift through a truly enormous data set (which some people call ‘the internet’) and serve up a list of content it thinks you’ll find useful by looking for specific keywords, the authority of the sites, the quality of their content, and a number of other factors.

All of this saves you an immense amount of time because you don’t have to endlessly search for a well-written, well-curated article. The algorithm has already done that for you.

So now maybe it’s easier to see why these two fields are important and what they involve, but it’s probably not enough to help you pick between the two. Let’s look at each of these fields more closely.

The Impacts of Data Science and Software Engineering

It’s difficult to quantify how immensely and irreversibly both data science and software engineering have changed life as we know it. Most of us can hardly remember the barbaric darkness of just a few years ago when you couldn’t ask Siri to give you directions to the nearest coffee shop.

If you do remember those times, you probably don’t want to go back to them.

Today, you don’t have to drive around searching for a coffee shop when you need a quick caffeine jolt. You just ask your phone assistant (developed by a software engineer) to find one for you. Then, an algorithm (developed by a data scientist) searches your query and finds a coffee shop just one mile away. It then opens up your map application (developed by a software engineer) to tell you exactly where to go.

And that’s just considering our personal lives. Software engineering and data science have, if anything, had an even more outsized impact on how businesses operate. Using software and big data, businesses are able to make data-informed decisions. This means being better able to identify an audience, anticipate their needs, give them what they want, and make bigger profits. 

Data Science vs Software Engineering: Methodologies

While there is some overlap in work processes, software engineers and data scientists tend to have very different methodologies. Let’s look at data science first.

Data Science Methodology

There are a variety of places at which a person could come into the data science pipeline. If they’re gathering data, they’re probably called a ‘data engineer’, and they’re going to be pulling data from various sources, cleaning and processing it, and storing it in a database.

This is usually referred to as the Extract, Transform, Load (ETL) process. 

If they’re using these data to build models and do analysis, they’re probably called a ‘data analyst’ or ‘machine learning engineer’. The crucial aspects of this part of the data science pipeline are making sure that any models built aren’t violating their underlying assumptions and that they’re actually driving worthwhile insights.

Software Engineering Methodology

Software engineering, on the other hand, uses a methodology called SDLC, or the software development life cycle. This workflow is used to develop and maintain software. The SDLC steps include planning, implementation, testing, documentation, deployment, and maintenance.

In theory, following one of the various SDLC models will lead to the software running at peak efficiency and will improve any developments in the future.

Data Science vs Software Engineering: Approaches

Another big difference between data science vs software engineering is the approach they tend to use as projects evolve.

Data science is a very process-oriented field. Its practitioners ingest and analyze data sets in order to better understand a problem and arrive at a solution.

Software engineering, on the other hand, is more likely to approach tasks with existing frameworks and methodologies. The Waterfall model, for instance, is a popular technique that maintains that each phase of the software development life cycle must be completed and reviewed before moving on to the next.

Some other frameworks used in software engineering include Agile, the V-shaped model, and Spiral. To the best of my knowledge, there simply are no equivalent data science processes (though many data scientists work within one of these approaches as part of a larger team).

Different Tools: Data Science vs Software Engineering

Data scientists and software engineers use a wide variety of precision machinery to do their jobs effectively and efficiently. Let’s take a look at a sample.

A data scientist’s wheelhouse contains tools for data analytics, data visualization, working with databases, machine learning, and predictive modeling. Which of these they wind up using will depend on their role.

If they’re doing a lot of data ingestion and storage they’ll probably be using Amazon S3, MongoDB, Hadoop, MySQL, Postgresql, or something similar. For model building, there’s a good chance they’ll be working with Statsmodels or Scikit-learn. Distributed processing of big data requires Apache Spark.

A software engineer utilizes tools for software design and analysis, software testing, programming languages, web application tools, and much more. As with data science, a lot depends on what you’re trying to accomplish. 

For actually producing code Atom, TextWrangler, Visual Code Studio, Emacs, and Vim are all popular. In the world of backend web development Ruby on Rails, Python’s Django, and Flask see lots of use. Vue.js has emerged in recent years as one of the best ways of building lightweight web applications, and the same could be said for AJAX when building dynamic, asynchronously-updating website content. 

And basically everyone needs to know how to use a version control system like Github.

Whatever your role, it’s important to have the best tools for the job you’re doing and to know how to use them.

Data Science vs Software Engineering: Skills

It probably comes as no surprise, but software engineers and data scientists get paid quite well.

Their pay figures are no accident; they need to master highly technical skills in order to excel, and they need to continually learn as the technology for their fields evolve. 

Let’s look at some skills you need to master if you want to be a data scientist or software engineer.

The most important things you’ll need to know to become a data scientist include programming, machine learning, statistics, data visualization, and a willingness to learn. Different positions may require more than these skills, but it’s safe to say these are the bare minimum when pursuing a career in data science.

If you’re interested in software engineering, the necessary skills will often be a little more intangible. Of course the ability to program and code in multiple programming languages will be required, but you also need to be able to work well in teams, problem solve, adapt to different situations and have a willingness to learn. Again, this isn’t an exhaustive list of the skills you’ll need, but these skills will certainly serve you well if you’re seriously interested in this career path.

Career Paths: Data Science vs Software Engineering

How to Become a Data Scientist

Data science is one of those career paths where graduate levels of education are still pretty highly valued. This is both because it’s a relatively new field and because most data science jobs require domain specific knowledge. 

Thankfully with the advent of high-quality data science bootcamps, this is becoming less the case as time goes on. 

If you want to become a data scientist, an excellent first step would be to see if programming and data analysis is even something you’re interested in. You can learn some basic coding and take an introductory data science course to see if it’s right for you.

Next, you’ll want to get the equivalent of an undergraduate education. Whether you do a traditional university or a bootcamp, it’s highly recommended to make connections and undergo internships. Hopefully you can leverage your connections into an entry level job. To move up in your company or get a better job, you may need to pursue a Master’s degree or even a PhD.

The barrier to entry for data science is high. Even after getting your foot in the door, you’ll be learning for years to come. Hard work and dedication will be required on this path. 

How to Become a Software Engineer

Software engineers often don’t require quite the same level of education that data scientists do, and many are even self-taught. Completing a two or four-year degree is the more traditional path to becoming a software engineer, but coding bootcamps are another viable option here. You can find fully online coding bootcamps and even completely free coding bootcamps.

If you want to become a software engineer, you’ll need to be proficient in at least one programming language, preferably more. The more languages you’re proficient in, the more jobs you’ll be able to qualify for when you begin searching. 

Again, becoming a software engineer requires hard work and dedication. But if you’re willing to put in that work, you can enter this field and be successful. The barrier to entry for software engineering isn’t quite as high as data science, so this might be something you want to keep in mind when making your decision.

What About Software Developers and Data Engineers?

It’s worth mentioning two fairly adjacent positions in the field: software developers and data engineers.  

Software Developers vs Data Scientists

Software developers are a more generic term for anyone who develops software. It often overlaps and is used interchangeably with software engineering. The key difference is that software engineers apply engineering principles specifically. 

Since software engineering and software development overlap so heavily, the strong majority of the differences from data science above apply to both software engineering and software development. 

Data Engineers vs Software Engineers

Data engineers are actually closer to software engineers than data scientists are. Data engineers differ from data scientists in that engineers focus on how data is handled, while scientists focus on the result of that data. Data engineers primarily work on the software that gathers and handles data that data scientists often use. 

Data engineers develop software just like software engineers, only that software is solely focused on data. The relationship is similar to game developers, who also develop software like engineers but only focus on video games. 

Data Science vs Software Engineering: Summary

Let’s sum up the data science vs software engineering question:

• Data science involves collecting and analyzing data, while software engineering is concerned with creating useful applications.
• Data scientists use the ETL process, while software engineers use the SDLC process.
• Data science is more process-oriented, whereas software engineering uses frameworks like Waterfall, Agile, and Spiral.
• Data scientists use tools like Amazon S3, MongoDB, Hadoop, and MySql; engineers use tools like Rails, Django, Flask, and Vue.js.
• Data science skills include machine learning, statistics, and data visualization; software engineering skills are focused on coding languages.

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