By Austin Gorsuch
If you’re new to the job market or looking to change directions mid-career, you probably already know that you have a lot of options. To help you work through some of them, we thought it might be useful to describe some of the similarities and differences between the fields of data science and software engineering to help you on the way to finding the career that’s right for you.
We’ll break down both data science and software engineering in terms of their goals, the process used to achieve those goals, and skills required to thrive in the job, and then dive into some of the points where the two fields meet. This includes potential roles, like data engineering, that require skills from both disciplines. We'll also dive into some of the more significant differences between the fields.
Let’s get started.
What is Software Engineering?
We’ll start with software engineering because it’s the simpler of the two fields to describe.
Put simply, software engineering seeks to apply the principles of engineering to the problem of developing software. Software engineers focus on designing, implementing, and then testing software with the consumer, or end user, in mind. After they have designed a given application, software engineers are often also charged with the task of keeping their software bug-free through patches and updates meant to optimize end user experience.
Unsurprisingly, to develop applications, software engineering follows what is known as the Software Development Life Cycle, or SDLC, which has six stages:
- Requirement Analysis
- Architectural Design
- Software Development
Note that the six stages of the SDLC are cyclical: the “end” of the process (Deployment) feeds back into the beginning of the process (with a new set of requirements determined by what has been deployed). There are several slightly different versions of SDLC, but if you understand the above process, then you’ll be well prepared to handle any other version that might fall into your lap.
What is Data Science?
In general, data science seeks to derive actionable insights, predictions, and analysis from large amounts of data. Data scientists focus on discovering patterns in data and developing tools or algorithms that make those patterns useful to their employer.
Since this process involves explaining the results of one’s analysis to a diverse group of interested parties, data science often also requires a high degree of domain-specific knowledge (in finance, business, or the relevant science, for instance) and the ability to communicate effectively about the complicated process of data interpretation.
The process often employed in data science is called Exploratory Data Analysis, or EDA, and involves the following three steps:
- Plotting raw data
- Applying simple statistical models
- Positioning models to maximize pattern recognition
Like the SDLC, the EDA process is also somewhat cyclical, with insights derived from the process being used to refine previous steps and change the final outcome of the analysis. For instance, a pattern recognized in step three might be the inspiration for a data scientist to run an additional statistical analysis that then yields further patterns that inspire further forms of statistical modeling.
Data science is a field where many employers still prefer a strong background (read: a Master’s degree or above) in mathematics, economics, or a related field. Coding-wise, a background in Python (especially pandas, Numpy, Matplotlib, seaborn and the scikit-learn library for machine learning applications), SQL, or R are preferred, depending on the nature of the work. A data scientist may also end up working with a software like Excel or Tableau, depending on the size and preferences of the company.
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If you are working in software engineering, your concerns will be with engineering an application, focusing on the problems of scale and reproducibility. On the other hand, working in data science, your concerns will be more analytical and business-oriented, determining next steps for the product and business as a whole.
If you work better in an environment with clearly established goals and a process to reach them, you may find that software engineering is a better fit for you than data science, where the goals and outcomes of analysis have to be flexible to keep up with the patterns in the data.
A software engineer usually has a clear sense that the things they’d like to accomplish through the SDLC process are readily achievable. Yes, there will be obstacles that have to be overcome and unforeseen problems in the production process, but the end goal is almost always clearly defined and possible.
In comparison, a data scientist may put in weeks of effort on a particular project, only to find that their model or algorithm cannot accurately predict trends in the data, at which point it’s back to the drawing board with nothing concrete to show for all your hard work.
Furthermore, the data scientist also has to present the outcomes of their analysis to other people in the company and it can be difficult to explain the failure of a specific model to people who may not understand that this is a common phenomenon in the process of EDA.
In terms of skills, a software engineer will tend to be more of a generalist than a data scientist. The software engineer has a number of coding languages under their belt and is constantly looking to expand their repertoire to make sure they always have the right tool for any given job.
By comparison, a data scientist tends to be a specialist in their field, both in terms of the data-oriented coding languages they know as well as the domain-specific knowledge that they bring to the job in order to make their analyses intelligible to others.
Already decided that data science is the career for you? Check out our article on the Data Scientist Career Path on Interview Query.
Depending on the specifics of your role in a company, you may find yourself in a position where you’re performing software engineering duties as a data scientist, or vice versa. One good example of this potential overlap can be found in:
- the data engineer (and some data scientists are expected to act as data engineers, especially at smaller companies), who applies the principles of engineering to create a data pipeline for a data science team.
- the machine learning engineer, who will create and scale machine learning algorithms for business applications.
In these cases, the line between data science and software engineering blurs and a more holistic approach to problem solving may be warranted.
However, even if you find yourself working only one of these roles, it’s important to remember that you’re working as a part of a larger team at a company, one that will probably include software engineers (if you’re a data scientist) or data scientists (if you’re a software engineer).
If you’re a data scientist deploying a machine learning model to production, you will probably come face to face with more than a handful of software engineers, and if you’re a software engineer working at a data-driven company, you’ll probably interact with a couple of data scientists here and there when it comes time to design your user interface.
Whatever your role, being familiar with the responsibilities and challenges faced by other members of your team can only help you perform better at your own job.
Now that you have a sense of the similarities and differences between software engineering and data science, you might be wondering about the next steps you should take.
You can check out our article exploring the differences between data science and data engineering if you’d like to further increase your understanding of your options as an aspiring programmer.
If you’ve already decided on a course of action, you can prepare for the technical interview in your field of choice by practicing your coding skills on questions from real interviews at companies like Google, Facebook, Amazon, and more at Interview Query.
Thanks for reading!