Data Scientist vs Software Engineer. Here’s the Difference. | Towards Data Science

IntroductionData ScientistSoftware EngineerDifferencesSimilaritiesSummaryReferences
Introduction
Data Scientist
Software Engineer
Differences
Similarities
Summary
References
Data Scientists and Software Engineers can work hand-in-hand, while some work completely apart from one another, so you can expect to see some similarities and differences between them. A usual company team encompasses a Data Scientist, Machine Learning Engineer, Product Manager, and Software Engineer (a blend of Product and Engineering). Oftentimes, one is already a Software Engineer and will transition to become a Data Scientist and vice versa. Below, I will be describing the skills, goals, differences, and similarities of each role and between each role. The goal of this article is to highlight these characteristics to better understand these positions, how they work with one another, and to start a discussion that can help you decide which role you would like to stay in or change to. This article will focus more on Data Science’s relation to Software Engineering, so I will not be discussing every component of Software Engineering and Software Development.
Continue reading below if you find Data Science and Software Engineering interesting and want to learn more about what differentiates them.
A Data Scientist’s primary goal or focus is surprisingly similar to that of a Software Engineer. However, the tools and methods taken to get there are much more different. For example, both a Data Scientist and Software Engineer can expect to automate a process that ultimately helps the business in some way. The differences or the focus on Data Science lies in the methods used to achieve the desired result. A typical Data Scientist will work on establishing a problem statement, querying data, exploratory data analysis, feature engineering, model building and development, and result interpretation. A Software Engineer may not work on all of these steps of a typical Data Science process, but they do touch a great amount of this work — including calling API data, storing it, programming enhancements, and deployment of a model (amongst a wide variety of other processes unrelated to Data Science). For now, let’s focus on some of the main skills and goals a Data Scientist can expect to employ.
The main skills for a Data Scientist include, but are not limited to:
Skills
SQL
R
Git
GitHub
Python
Jupyter Notebook
Data Analysis
Machine Learning Algorithms
Object-Oriented Programming (OOP)
Above are just some of the skills a Data Scientist can expect to know and work with at their company. Some of these will also be described below in the Software Engineering section.
The main goals for a Data Scientist include, but are not limited to:
Goals
— automating a process
— saving time and money
— coming up with business solutions
— using Machine Learning to solve problems
Some of these goals of Data Science also tie in nicely with Software Engineering; particularly, automating a process and saving time, as well as money for a company.
A Software Engineer can expect to ultimately solve software issues, while also building upon the software used within the company by means of programming — mainly. A Software Engineer focuses on infrastructure, automation, testing, and maintenance. Because of the wide variety of skills required to become a Software Engineer, some will eventually overlap with that of a Data Scientist.
The main skills for a Software Engineer include, but are not limited to:
Skills
Java/Python, etc.
Docker
Jenkins
REST
Shell
Hadoop
Kafka
Scrum/Agile Methodologies
As you can see, some of these Software Engineering skills overlap with Data Science. However, there are some very specific skills and goals that are usually only required for Software Engineers — depending on the company as well.
The main goals for a Software Engineer include, but are not limited to:
Goals
— automating processes
— integrating processes
— platform development
— system flow solutions
— overall software solutions, fixes, and improvements
The goals of a Software Engineer are extremely broad and can cover something incredibly specialized to something more universal in a company. On some teams, you can expect a Software Engineer to work side-by-side with a Data Scientist — sometimes transitioning into a more focused role of Data Engineer or Machine Learning Engineer.
There are differences in the skills, goals, and education that is taken to be a Data Scientist and Software Engineer. You can expect different schooling and specific classes, like Object-Oriented Programming for Software Engineers and Statistics for Data Scientists. What’s in the name actually is what sheds light on the differences. Easily enough, Software Engineers focus more on, well, software, and Data Scientists focus more on data and science — science usually meaning researching and developing of Machine Learning algorithms.
Here are some of the differences between the two careers:
Differences
Data Scientists practice primarily Machine Learning algorithms
Software Engineers focus more on the software development lifecycle
Software Engineers focus more on programming in general, specifically object-oriented programming
Software Engineers generally know more than just Python, like Java or Javascript/HTML — depending on if they are more of a Backend Engineer or Frontend Engineer, respectively, where it is a Data Scientists’ usual language only (for some)
Data Scientists work with more data and data manipulation for their models
Data Scientists may work with more people and stakeholders
Data Science has a focus on statistics
Data Science has a focus on data analytics
Keep in mind that when I bring these differences up, I am noting that the underlying principles may both be shared between roles, it’s that one role might perform that skill or method more when compared to the other role. There are other types of differences as well, like the position titles.
For example, there are usually more specific roles for Software Engineers, here are some common variations of each role:
* Associate Data Scientist* Data Scientist* Senior Data Scientist- Junior Software Engineer - Staff Software Engineer- Principle Software Engineer
Although there is a general flow of titles for each position, it is always best to discuss with each company what each title means, and where the minimum and maximum titles are in terms of seniority, before assuming what each title will mean.
Every time I write these articles comparing roles, I start to realize how similar different roles really are. For example, in the above differences section, a lot of the differences are still shared, but the focus is different per role. However, for this section, I am going to discuss some of the general similarities that you can expect to see when comparing Data Scientists to Software Engineers.
Here are some of the similarities between the two careers:
Similarities
programming languages — Python usually
solving business problems
working with data
automating and testing
Jenkins
Docker
SQL
There are several languages and tools that both roles can share. Some days, as a Data Scientist, you can find yourself programming or coding so much that you feel like a Software Engineer, while some days, as a Software Engineer, you work on model deployment and find yourself feeling like a Data Scientist. There are other instances of overlap as well, and feel free to discuss them in the comments section below.
Data Scientists and Software Engineers have a lot in common, as well as a lot of differences. With each specific role and company, you can expect what I discussed to be true, or in other cases, it can be different. As I usually say, it is up to all parties to designate the functions of a role and where they stress their importance on specific skills, tools, languages, and goals. We have discussed the skills and goals for the common Data Scientist and Software Engineer, as well as have highlighted some of the key differences and similarities between the two roles.
Please feel free to discuss down below what you have experienced in either or both of these roles. It will be interesting to see if some Software Engineers find themselves as part-time Data Scientists or vice versa. Perhaps, it is completely different and experiences are vastly different as well, and a Software Engineer has not touched a part of the Data Science process in some way.
If you would like to learn more about Data Science in relation to Business Analytics, feel free to check out my other article here [6]:
towardsdatascience.com
Thank you for reading! I hope you found my article both interesting and useful!
[1] Photo by Anastasiia Kamil on Unsplash, (2019)
[2] Photo by Myriam Jessier on Unsplash, (2020)
[3] Photo by Christina @ wocintechchat.com on Unsplash, (2019)
[4] Photo by Fabian Stroobants on Unsplash, (2019)
[5] Photo by Viktor Talashuk on Unsplash, (2018)
[6] M.Przybyla, Data Scientist vs Business Analyst. Here's the Difference, (2020)