Full Stack Data Scientist Vs Full Stack Software Engineer

Total Data Science
Published in
7 min readFeb 5, 2022

Should You Become A Full Stack Data Scientist OR A Full Stack Software Engineer

The Definitions

Full Stack Software Engineer

“A full-stack software engineer is the one that’s writing code not only for the user’s front-end web applications or mobile applications, but they’re also writing API code that sits in the middle, they’re writing server code that sits in the back, and they’re also connecting and communicating with databases.”

Full-stack software engineers use interdisciplinary skills, including web design, graphic design, web publishing, web programming, application programming and database management to create web and mobile applications. Their main concern is to build a fully functional applications.

Full Stack Data Scientist

Data science is a field used for analysis, prediction, forecasting, and optimizing data in the field. Full Stack Data scientists use math, statistics, and algorithms to analyze data using a combination of domain expertise and programming skills to find patterns, extract meaning, use algorithms to understand what the past, present and future looks like and make predictions of the future as well as give recommendations to business leaders.

A Full Stack Data Scientist knows the “end-to-end” of a data science project. When I say end-to-end, I mean right from getting the data, doing feature engineering, model building and model optimisation as well as model deployment. Their main focus is working with data

Where They Differ

Full Stack Software engineers are more concern with the optimum and operational development of the software. They are well versed in programming languages. Most importantly, their coding skills are on point and they love coffee…lol

Most Full Stack Software engineers are highly concern about their programming language and which tools to use. Most software engineers use a combination of programming languages and tools.

The following screenshot shows sample of the most popular programming languages used by Full Stack Developers

screenshot from fullstackdatascientist.com

The following shows the preference of the various programming languages by software engineers according to the 2020 stackoverflow survey.

screenshot from 2020 stackoverflow survey report

Full Stack Software engineers are the ones that make ideas possible in the form of software applications. Think of mobile applications and web platforms like Uber, Amazon, Netflix, Facebook, Tinder, etc. Without them, ideas are just ideas.

Where do Full Stack Data Scientist comes in then?

Unlike software developers, Full Stack Data Scientist are mainly concern about what the data on these developed applications means and what it can be used for. Full Stack Data Scientist are not much concern with the softwares and its working. They are not much into core programming or development. Data Scientist can use a combination of two or three tools to get their job done. For instance, most Data Scientist can use Python, Tableau and AWS to analyze, process, build machine learning models and deploy these models for a complete fully functional project. This will be difficult to do when it comes to Full Stack Software development.

Full Stack Data Scientist are well versed in Mathematical and Statistical concepts, which is unlike software development where a minimum of these concepts are required. Again, Full Stack Data Scientist are concern with domain knowledge in order to bring the best out of the data they are facing. For instance if a Data Scientist wants to build a machine learning model that will run under a health recommendation system to recommend the best drugs for chronic diseases, he/she needs to understand the domain of chronic diseases or tie up with health professionals in order to thoroughly understand what drug goes with what symptom since this a critical area and any wrong recommendation can lead to the patients loosing their lives. This is not a concern of a full stack software developer.

Data Scientist use simple and fewer tools to achieve greater results. The following are sample of the tools used by Full Stack Data Scientist:

screenshot from KDNuggets blog

Note, most data scientist use just a few combination of these tools based on the problem statement.

In my earlier article published on medium, I dived into the ultimate path to becoming a Full Stack Data Scientist. You may want to read that.

In short:

Full Stack Software Engineers worry about the software while Full Stack Data Scientist worry about the data on the software.

Where They All Meet

Despite Full Stack Software Engineer and Full Stack Data Scientist have their differences, they are have some commonalities in many similar ways.

Although Data Scientist are not core programmers as I indicated earlier, they still use most software engineering skills. It is important to note that Data Science is just a combination of software engineering, statistics and business knowledge. A software engineer can use Python programming to build a fully functional software application. For instance as per full stack software developers at Netflix, Python is used through the “full content lifecycle,” from security tools to its proprietary content distribution network (CDN) Open Connect. Most of the network devices at Netflix are managed by Python-based applications. At the same time Python is used to by full stack data scientist and machine learning engineers to build Netflix’s recommendation system.

What am trying to say here is that in order for Data Scientist to work, they need the platform to be developed by software engineers. Without software engineers, data scientist work is nearly impossible.

On the other hand, Data Scientist are the ones that make the difference after the softwares are developed. Think about it, if Netflix was just developed without the recommendation system to recommend and organised specific movies for specific people, do you think it will be fun to watch Netflix? How will Netflix recommend kids movies to kids and even particular kids movies to particular kids. Imagine if you log in to Amazon to buy laptop charger and amazon is recommending Brazilian rice to you. How on earth are the two related? Without Data Scientist, the work of software engineers are just there to yarn.

What Are The Job Growth Projections For Full Stack Data Scientists and Full Stack Software Developers?

While both tracks hold tremendous earning potential, with Full Stack Data Scientists showing 37% annual growth and Full Stack Engineers showing 35% annual growth according to LinkedIn’s 2020 Emerging Jobs Report, it is worth noting the exponential future needs and potential of Machine Learning and AI, bolstering the demand for data scientists now into the future. In fact, data science has topped the report for three years running, citing AI as a significant contributing factor.

The following is a screenshot from LinkedIn’s 2020 Emerging Jobs Report for top jobs.

#3 Data Scientist: 37% annual growth

screenshot from linkedin job report 2020

#4 Full Stack Software Engineer: 35% annual growth

screenshot from linkedin job report 2020

As you can see for yourself, the data is clear and the winner is obvious. In the coming years, people with data skills are even going to be more in demand than any other field. This explains why most software engineers are shifting careers from core software engineering to become data scientist. In fact, it is easier to become a Full Stack Data Scientist if you are already a software engineer, although not required.

Over the past few years, due to my role as a Data Scientist here at Microsoft, I have guided many people from different backgrounds globally to transition from their current career to become Full Stack Data Scientist. In last month, one of my students from India, enrolled in my course, got a job offer remotely to work with one American company. She used to be a marketing professional with salary of $52,000/year, currently her new role as a Data Scientist comes with salary of $98,000 as a beginner Data Scientist. There others with two or three years of experience that command over $102,000/year.

Last Words

Choosing a career is important and the future of that career is even more crucial. As we have seen above, your interest and professional goals are important as well as the field you want to spend the rest of your life.

Choose wisely and make the best out of the field.

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Total Data Science

Data Science | Artificial Intelligence | Machine Learning