Is Data Science Harder Than Software Engineering?

Data scientists and software engineers play important roles within organizations that work with digital products, services, and platforms. But their skills and areas of focus are considerably different. So, which is harder? Learn more about the two professions here.

which is harder data science or software engineering

Software and data are the twin mantles of tech and the future of business. While both data scientists and software engineers are well-versed in hard computer science skills such as coding and machine learning, they use these skills to achieve different ends. Where software engineers build applications and systems, data scientists tease out meaningful and actionable insights from collected data. The two disciplines work in tandem: data scientists make sense of the troves of collected information, which software engineers then use to create or improve on a product. If you are considering a  career in tech, your choice between data science and software engineering will depend on your personal strengths, interests, and technical skills. 

What is data science?

The explosion of big data generated by connected devices, social media, and business transactions means that organizations are capturing unprecedented amounts of real-time data—to the tune of a collective 2.3 trillion gigabytes per day. If properly collected, organized, and analyzed, this data can reveal impactful insights. That’s where data scientists come in.

Data scientists are akin to detectives: they gather and interpret evidence to make meaning. They interpret data in order to reveal concealed patterns and track trends that will help an organization set its priorities. Organizations use data science to optimize product development, reduce operational costs, calculate risk, and better connect with consumers. 

The “detective” work of a data scientist happens within a technical framework: they acquire and clean data sets, then validate, integrate, and analyze the data using machine learning, statistical modeling, and advanced algorithms. 

What is software engineering?

Software engineers are the builders: they design the architecture of digital products. 

Using programming languages such as Python and Java, software engineers build everything from mobile apps to operating systems. While some engineers can build both the front-end user-facing application and the back-end that powers a program (I.e. full-stack engineers), many software developers specialize in one or another. 

Software engineers approach software development as problem-solving. Software engineers use analytical insights produced by data scientists in order to identify user issues and formulate product-based solutions. After figuring out how a product will fix the problem at hand, software engineers collaborate with designers to outline a sleek, functional design that meets project requirements. Software engineers then build, test, and troubleshoot their product. Following its launch, software engineers will maintain the product and improve it with updates and new features.

4 technical skills all data scientists need

Data science skills run the gamut computer science, statistics, and communication. 

Here are a few hard skills data scientists need: 

  • Coding. Programming languages such as Python and SQL are used to wrangle data, build and manage data libraries, and implement machine learning algorithms. 
  • Statistical modeling. Inferential statistics are applied to identify the trends and characteristics of a data set. Foundational techniques like regression analysis, which demonstrates a causal relationship between variables, are used to explore data and pave the way for predictive modeling and other in-depth methods of analysis.
  • Machine learning. Machine learning enables data scientists to automatically identify and predict groups or categories within data sets. Data scientists must implement supervised and unsupervised machine learning algorithms based on vital machine learning techniques such as decision trees, clustering, naive Bayes classifiers, and more.
  • Big data management. As the volume of inbound data balloons, data scientists must use processing framework tools like Spark and Hadoop to store, clean, and organize large data sets.

4 technical skills all software engineers need

Software engineers use a combination of engineering principles and computer science skills to build programs and applications. 

Here are some hard skills that software engineers need.

  • Front end development. Software engineers use JavaScript and other standard programming languages to build a web page or application. Skills like DOM manipulation, event-driven programming, and debugging are also crucial to crafting a smooth user interface.
  • Back end development. This requires a command of at least one popular programming language — Python, Flask, PHP, and Ruby are key examples. Back end development also requires SQL proficiency to construct and maintain the databases that store application and client data. 
  • Version control proficiency. Version control tools like Github are used to track and manage changes to source code. These platforms help preserve the integrity and consistency of a code base and enable teamwide collaboration.
  • Scalable design. Software engineers must design scalable products that can handle the large amount of data generated by an ever-growing user base. Software engineers use cloud infrastructure, domain-driven design, and automation to create software that is capable of handling increasingly vast volumes of data.

Which is harder: data science or software engineering?

The distinct challenges and responsibilities of data science and software engineering will suit people with different dispositions, interests, and aptitudes.

Data science will appeal to those who are natural critical and analytical thinkers who enjoy spotting patterns, trends, and relationships between variables in the world around them. This work is ideal for the meticulous detective who enjoys collecting, assembling, and interpreting evidence to explain phenomena. If you have a knack for statistics and an analytical streak, you might find data science to be the easier of the two professions.

Software engineering will attract those who like to problem solve and build within parameters. This work caters to the architects—someone who loved LEGO as a kid might find a similar sense of gratification in the construction of software. If you enjoy hands-on building and have a mind for both form and function, software engineering might be the path that comes most naturally. 

While both data science and software engineering require overlapping skills—namely, coding and the ability to problem-solve—the former profession focuses on finding meaning in data sets, while the latter is focused on building products supported by those findings. Focusing on the emphasis of each field will help you assess which career track is the best match for your skills and strengths. 

Is data science the right career for you?

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Check out Springboard’s Data Science Career Track to see if you qualify.

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Springboard now offers a Data Science Prep Course, where you can learn the foundational coding and statistics skills needed to start your career in data science.

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