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Software Engineer vs Data Scientist


“Everything is going to be connected to cloud and data… All of this will be mediated by software.”

  • Satya Nadella, CEO, Microsoft.

The first part of the quote requires a Data Scientist, and the second one requires a  Software Engineer

Clearly, both of them play a crucial role in the tech world, and ultimately in our lives too.  There are 120433 software engineer job openings and 13566 data scientists job openings listed on naukri.com. 

When you search for some content and get the relevant results within fractions of seconds, it is the data scientist who worked behind it. If you can see the same content properly on your smartphone and on your PC/laptop or any other gadget, it is a software engineer who has made it possible. It will work on every device, or on your phone after a few updates as well. 

At first glance, both of the domains look the same because for both of them you need basic programming skills. While data science is about collecting and analyzing data, the software engineer is about developing applications, features, and functions for customers. 

There are many opportunities in both the fields, good growth is seen in data science being a new and high-paying domain. 

You can go with a data science program for making a career in the exciting field of Data Science.

Let us look at the key differences between the two disciplines.

Who is a Software Engineer?

Simply put, a tech pro who leverages the principles of software engineering to design, develop, manage, manage, test, and evaluate a software application, is referred to as a Software Engineer. A bachelor’s degree in computer science is required to become a Software Engineer. In addition, you need strong coding skills to develop a software application.

Apart from developing a software application, a software engineer has to patch and iterate on existing applications to ensure that it works as per the requirements.

Who is a Data Scientist?

Data Science is a domain of converting the data in different forms to useful, structured data. It is an interdisciplinary field that involves the usage of scientific methods, processes, pr algorithms to extract useful insights from structured and unstructured data.

Data Scientists are analytical experts who are skilled in the technical and science domain to find out trends and manage data. The tech pros use contextual understanding, industry knowledge, suspicions of assumptions with the intent of uncovering solutions to business challenges.

A data scientist is able to provide solutions to business issues by measuring the effect of changes in promotional material and can also give financial solutions by predicting returns in the market. 

Data Science is an exponentially growing, valuable field that has a plethora of opportunities if you are skilled the right way.

Data Scientist Vs Software Engineer

Though both of them have common basic prerequisites, both are different from each other in many aspects. A data scientist uses programming and development skills to sift through the data, analyze it, find patterns, draw conclusions, and interpret it in meaningful ways to help in decision-making.

A software engineer, on the other hand, focuses on creating user-friendly software that serves some specific objectives.

Some quick facts about software engineer and data scientist are:

Data Scientist Software Engineer
Prerequisites A bachelor’s degree in Computers and certification in data science. A bachelor’s degree in Computer Science and deep knowledge of coding languages.
Average Annual Salary


Ranges between $67k and $134k (PayScale) Ranges between $92k and $108k (Glassdoor)
Growth (2018 to 2028) 16% 21%

Let us find out the key differences from different viewpoints.

While enormous data is being generated from different resources every second, the requirement for skilled professionals who can draw meaningful conclusions is increasing exponentially. The expertise needed to manage the large volumes of data, analyze it, and interpret it wisely has made the need for data scientists grow high.

On the other hand, without knowing software engineering, creating software is prone to break. You need a skilled software engineer to deliver a software application that is error-free.

Data Science follows a process-oriented approach. This includes the implementation of different algorithms according to the type of data, pattern recognition, and crunching numbers to draw useful conclusions.

The approach that software engineering follows is a framework or methodology-oriented. The software development lifecycle may be carried out by following the Waterfall model, Agile framework, Spiral, or V-shaped frameworks. 

Data Science involves ETL (Extract, Transform, Load) to extract data from various resources, transform it into a user-readable format, and load it to the system for further processing.

Software Engineering involves SDLC (Software Development LifeCycle), the steps of which include planning, implementation, testing, documentation, deployment, and management of a software application.

Data Science includes the usage of analytical and data visualization tools along with database tools.

Software Engineering includes leveraging design and analysis tools, coding language tools, programming language tools, database tools, SCM tools, Web Application tools, Continuous Integration Tools, and Testing tools.

  • Eco-system, platforms, and Environment

Data Science includes platforms such as Hadoop, Spark, Map Reduce, Flink, Data Warehouse.

Software Engineering includes Business planning and modeling, Analysis and design, development of user-interface, coding, maintenance, and reverse engineering along with project management

To be a successful data scientist you are required to know how to build data visualizations and products in a user-readable format, algorithms, data mining, Big Data processing, machine learning, domain knowledge, SQL and NoSQL databases, coding, statistics, and probability.

To become a successful software engineer, you need to properly understand what a user wants you to build, core programming languages, configuration tools (such as Chef, Puppet), testing, build tools (Gradle, Maven, etc), Build and Release Management(Jenkins, Artifactory, etc)

A Data Scientist can take up the responsibilities of Data Analyst, Data Engineer, Business Analyst, and Big Data Specialist apart from being a data scientist.

A Software Engineer can be a Developer, Designer, Tester, Big Data Engineer, Build and Release Engineer, Administrator, Product Manager, or Cloud Consultant.


You have seen that both of the roles are highly in demand in the tech industry. With IoT, gaming, healthcare applications, proliferating in our lives, fuels the demand for both software engineers and data scientists. 

With good salaries and innovation in the field, you would like to have a career in Data Science with basic knowledge of coding. You can study the rest of the stuff in an online training course that will let you launch yourself as a successful data scientist.

 So, when are you going to register yourself in an online training course for data science?


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