Roles like “Data Scientist” and “Data Engineer” are growing popular each day. Whereas the role of a Software Engineer has been there for a long time. In addition, many people who want to pursue a profession in the field of Computer Science or related may not be aware of “Software Engineer vs Data Scientist” roles. This is because a Data Scientist is a fairly new career option.
Data Scientists and Software Engineers play two of the most common roles in the industry. While these two segments are in the technology industry, they are certainly two quite different routes. The need among the workforce is increasing both for Software and Data Engineers.
This article provides you with a comprehensive overview of the critical differences between a Software Engineer and a Data Scientist. It also provides you with a brief overview of the job roles. So if you’re looking to enter either field, closely assess these differences to better understand where you’d best fit. Read along to find out how you can choose the right role for yourself.
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What is a Software Engineer?
A Software Engineer has extensive knowledge of programming languages and is expected to have sound knowledge of Software Development, Computer Programming, Operating Systems, and good Analytical skills. Software engineering knowledge is considered a base for any computer-related stream/jobs/opportunities.
Software Engineers are expected to build software, solve software issues, and provide Infrastructure, Maintenance, and Testing. There are a variety of software domains that a Software Engineer can develop, such as Operating Systems, Business Software, Games, Control Systems, Payment Gateway, etc.
What is a Data Scientist?
Every Data Scientist is a Software Engineer but Every Software Engineer is not a Data Scientist.
However, a Data Scientist is more focused on defining a Problem Statement, Querying Data, performing Exploratory Data Analysis, developing Models, and Interpreting Results.
Data Scientist works on structured and unstructured Big Data and combines data with Mathematics and Science to derive conclusions from the data. Their usual job is to get the data from a Data Engineer, identify the features and labels, model them by algorithm, test, train, and then interpret or forecast the results.
Factors that Drive the Software Engineer vs Data Scientist Decision
Now that you have a basic idea of both roles, let us attempt to answer the Software Engineer vs Data Scientist question. There is no one-size-fits-all answer here and the decision has to be taken based on the roles, skills, tools, and other parameters listed below. The following are the key factors that drive the Software Engineer vs Data Scientist comparison:
1) Roles & Responsibility
Following are the role & responsibilities of a Software Engineer:
- A Software Engineer should be able to analyze user requirements.
- Based on the user’s requirement, a Software Engineer can design and develop the software.
- Perform Testing, Automation, and Release management of the software.
- A Software Engineer should be responsible for creating and maintaining end-user systems.
- A Software Engineer should be responsible for a structured approach to Hardware and Software development.
Following are the roles & responsibilities of a Data Scientist:
- A Data Scientist should be responsible for analyzing Big Data and deriving insights.
- A Data Scientist should work with a considerable volume of data to uncover insights for making informed decisions.
- A Data Scientist must be aware of Distributed Computing and Big Data fundamentals.
- A Data Scientist is required to communicate clearly with business stakeholders.
2) Skills
A Software Engineer must have the following skills:
- A Software Engineer should have good experience in programming languages like C, Java, Python, C++, and SQL.
- A Software Engineer should be very well versed with Object-Oriented Programming structures.
- A Software Engineer must have analytical skills and the ability to solve problems.
- A Software Engineer should have logical thinking to process the requirements and convert them into solutions.
- Good written and verbal communication skills.
A Data Scientist must have the following skills:
- They must have great experience in one of the programming languages like Python, R, and SQL.
- They should have a good understanding of Git and other version control mechanisms.
- They should have a good knowledge of Machine Learning Algorithms and Object-Oriented Programming Language.
- They should have exquisite domain knowledge and must possess analytical thinking.
- Big Data systems like Hadoop, Hive, and Spark can be of added advantages.
Some of the popular tools that a Software Engineer commonly uses:
- Software Engineer heavily uses programming languages like Java, JS, Flask, and Python to build and maintain programs.
- A Software Engineer also uses automation tools like Selenium, and HP ALM to perform automation testing.
- Jenkins, Github, and Ansible have commonly used tools for orchestrations.
- Software Engineers can use any tool that helps them to build an application.
Some of the popular tools that a Data Scientist use are:
- A Data Scientist uses Hadoop to process Big Data and train models on different algorithms.
- A Data Scientist mostly uses Machine Learning tools like scikit, Amazon Sagemaker, etc., to build models based on different algorithms.
- A Data scientist uses tools like Cloud (AWS, GCP, Azure), MongoDB, Hadoop, and MySql.
4) Programming Languages
The most frequent programming language used by a Software Engineer are:
- Java
- Python
- Docker
- RestAPI
- Jenkins
- Github
The most common programming languages a Data Scientist use are:
- Python
- R
- SQL
- Machine Learning Models
- Jupyter Notebooks
5) Educational Background
The Educational Background required for Software Engineers are:
- 4 years of Bachelor’s degree.
- Experience in any of the programming languages like Python, R, and SQL.
- Should possess excellent communication and interpersonal skills.
The Educational Background for Data Scientist is:
- A 4 years Bachelor’s Degree in Computer stream.
- A Master’s Degree and a Ph.D. although optional will help you to understand core concepts and terminology.
- Prior working experience as a Data Engineer.
- Good knowledge of Machine Learning algorithms.
- Experience in working with Cloud Infrastructure, Visualizations, etc.
- Should possess outstanding communication and interpersonal skills.
6) Salary and Job Openings
The salary for Software Engineers and Data Scientists varies across locations. However, on average – An entry-level Data Scientist can earn over $120,089 per year, whereas a Software Engineer can earn somewhere around $103,951 a year in the United States.
For Job Openings, you can refer – to Naukri, Indeed, or JobServe depending upon your location.
7) Career Map
Every organization has a different hierarchy structure for its professionals. Therefore one cannot mention the typical career map for and domain.
Typical career growth for a Software Engineer goes like this:
- Junior Software Engineer
- Staff/Senior Software Engineer
- Principal Software Engineer
Typical career growth for a Data Scientist goes like this:
- Associate Data Scientist
- Data Scientist
- Senior Data Scientist
Conclusion
In this blog post, we have discussed in detail the Software Engineer and a Data Scientist in terms of the difference in both streams to various factors.
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FAQ
Which is better, a data scientist or a software engineer?
It depends on your career interests. Data scientists focus on analyzing and interpreting complex data, while software engineers build and maintain software systems. Both roles are highly valuable, but your choice should depend on your passion for data or software development.
Do data scientists make more than software engineers?
Generally, data scientists tend to earn higher salaries due to the specialized skills required for data analysis, machine learning, and statistical modeling. However, salary can vary depending on experience, location, and industry.
Who gets paid more, software engineer or data engineer?
Data engineers often earn more than software engineers because of their focus on designing and building complex data systems and pipelines. However, the salary difference may vary based on factors such as company, experience, and specific technical skills.
Vishal Agarwal is a Data Engineer with 10+ years of experience in the data field. He has designed scalable and efficient data solutions, and his expertise lies in AWS, Azure, Spark, GCP, SQL, Python, and other related technologies. By combining his passion for writing and the knowledge he has acquired over the years, he wishes to help data practitioners solve the day-to-day challenges they face in data engineering. In his article, Vishal applies his analytical thinking and problem-solving approaches to untangle the intricacies of data integration and analysis.
