Data Science vs Software Engineering
Updated on 01st Jul, 21 65 Views

In this blog on Data Science vs Software Engineering, we will read in detail about the following topics:

Let’s begin by reading about both domains to get an idea of both of them completely. 

Why Data Science vs Software Engineering?

Both Data Science and Software Engineering domains involve programming skills. Where Data Science is concerned with gathering and analyzing data and Software Engineering focuses on developing applications, features, and functionality for the end-users.

Interested to start a career in Data Science? Check out this tutorial explaining how you can start a career in Data Science:

Now, you will read in detail about the two technologies mentioned above.

Data Science

The domain of Data Science is incredibly diverse and requires a skill-set from several different domains to be used together for adequate results. If you are an Iron Man fan, you already know about Jarvis, Tony Stark’s assistant – a virtual AI. It helps Tony in predicting an outcome for any given action. The process of collecting data, analyzing the data, and predicting a certain outcome is Data Science.

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In the real world, more data has been created in the past 2 years than in the entire history of the human race. For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income. Data accounts to be an important feature for any organization due to its ability to direct in making decisions based on facts, statistical numbers, and trends. Data Science is a concept that involves data collection, data processing, and exploration leading to analyzing the data and consolidating the results.

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Software Engineering

A detailed study of engineering to the design, development, and maintenance of software is called Software Engineering. It involves the analysis of users’ requirements by focusing highly on the best processes and methodologies and producing high-quality software. A suitable programming language and solutions to algorithmic problems complying with the ’users’ requirement is a prime objective of a software engineer.

It ensures that the application is built consistently, error-free, and on budget. As the application is built, there is always an immense rate of change in the users’ requirements, Software Engineering becomes resourceful here.

Looking to become a Software Engineer? Check out this detailed video on How to Become a Software Engineer and get an idea of this field: 

Now, it is time to read in detail about the process and differences between Data Science and Software Engineering.

Qualifications required for Data Science and Software Engineering

The advent of data science has attracted many talents to increase their computer programming and machine learning skills and sometimes for software engineering skills are mandatory for data scientists. In the next section, we will list out the skills necessary for aspiring data scientists and software engineers.

Qualifications for Data Science

Data Scientists come from various educational backgrounds. Primarily, they involve B.Tech or M.Tech majoring in Computer Science or Information Technology, a B.Sc or MSc in Statistics, and few MBA graduates from top business schools.

Qualifications to be a Data Scientist

The role primarily requires a skill set of mathematics, statistics, knowledge of programming languages, and other computer science essentials. Machine Learning plays a major role in Data Science.

Along with these a domain expertise will come in handy to soar in your company’s domain.

To sum it up, a Data Scientist can be from the following educational backgrounds :

  • Engineering
  • A degree in statistics or mathematics

Software Engineering

A software engineer requires a Bachelor’s degree majoring in a relative computer program as a minimum entry-level requirement. Software Engineering demands a deep understanding of at least a couple of languages and how they function. Some of the popular languages are Python, JavaScript, C#, C++, Ruby, Java.

Knowledge in data structures and algorithms is recommended, as a lot of the interviewers look for these skills during recruitment. Along with theoretical knowledge, there is a high recommendation for practical knowledge in reading code, building projects, and working in a team.

An outline of the qualifications and requirements for Software Engineering are listed below :

  • Bachelor’s degree in any relative computer program
  • Deep and practical programming knowledge
  • Data structures and algorithms

Become a Data Science Architect IBM

Career paths for Data Science vs Software Engineering

Let’s now talk about how you can start off with careers in Data Science and Software Engineering. Later in each section, we follow up with the career path you could follow if you are considering a career as a Data Scientist or Software Developer

Data Science Careers

As a rapidly growing field, we do not see this demand slowing down anytime soon. Specializing in Data Science provides entry-level jobs as an Analyst in different domains.

  • Data Analyst
  • Quantitative Analyst
  • Business Intelligence Analyst
  • Business Analyst
  • Systems Analyst
  • Marketing Analyst
  • Operations Analyst

Check out our blog on Data Science Tutorials to learn more about Data Science.

These roles can further lead to senior and lead roles. To maximize your earnings, your eventual goal can be a C-suite role in data science as a chief data officer for an organization.

Timeline if you are choosing to specialize in Data Science.

Data Scientist
  • Data Scientist
  • Senior Data Scientist.
  • Machine Learning Engineer.
  • Lead Data Scientist.
  • Chief Data Officer.

Software Engineer

An aspiring software engineer can expect regular opportunities to progress your career. As you learn technical skills and gain experience, new opportunities will open. A career in software can be kickstarted with the following roles :

  • Front End Engineers
  • Back End Engineers
  • Full-Stack Engineers
  • Mobile App Developers
  • Graphics Engineer
  • Game Engineer
  • Data Engineer
  • DevOps Engineer
  • Software Development Engineer in Test
  • Embedded Systems Engineer
  • Security Engineer

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These roles can further lead to senior and lead roles such as :

Software Engineer
  • Junior Software Engineer
  • Senior Software Engineer
  • Tech Lead
  • Team Manager
  • Technical Architect
  • Chief Technology Officer

Data Science vs Software Engineering Salaries

Pay is normally skill-based, competency-based, or performance-based. This section will give you a brief overview of the salaries in the Data Science and Software Engineering domains.

Data Scientist Salaries

Experience is one of the most important factors in a Data Scientist’s salary. With an increase in every year of experience, a data science professional’s salary increases exponentially every year.

LevelUS Salaries (USD) / YearIndian Salaries (INR) / Year
Entry Level$57k₹341k
Mid-Level$79k₹823k
Experienced$106k₹2m

Software Engineer Salaries

Software Engineers are highly regarded and it is no secret that they are in high demand

Software Engineers are highly regarded and it is no secret that they are in high demand to earn a large amount.

LevelUS SalariesIndian Salaries
Entry Level$63k₹274k
Mid-Level$87k₹561k
Experienced$129k₹1M

Both Data Scientists and Software Engineers are highly paid professionals and both professions come with great benefits and challenges.

** Source of salaries of Data Scientist Vs Software Developer: Payscale 

Difference between Data Science and Software Engineering

All the differences in this article Data Scientist Vs Software Developer domains have been summarized in the below table.

Data ScienceSoftware Engineering
Data Science focuses on gathering and processing data.Software Engineering focuses on the development of applications and features for users.
Includes machine learning and statistics.Focuses more on coding languages.
Deals with Data Visualisation tools, Data Analytics tools, and Database Tools.Software Engineering deals with programming instruments, database services plan instruments, CMS devices, testing devices, integration apparatus, etc.
Deals with Exploratory Data.Software Engineering focuses on systems building.
Data Science is Process OrientedSoftware Engineering is methodology-oriented.
Skills include programming, machine learning, statistics, data visualization.Skills include the ability to program and code in multiple languages.

Is Data Science better than Software Engineering?

Finally, The final question “Software Engineer vs Data Scientist” – Which profession is better? Both Data Science and Software Engineering requires you to have programming skills. While Data Science includes statistics and Machine Learning, Software Engineering focuses more on coding languages.

Both career choices are in demand and highly rewarding. Ultimately, it depends on your choice of interest. Although the field of data science is soaring, its importance will never outgrow that of a software engineer, because we will need them to build the software the data scientists work on. We will always need Data Scientists to analyze the data and bring new scope for the business on which Software Engineering can build software. Thus, we reach the end of our Data Science vs Software Engineering article. We hope you have gained few insights regarding the topic. Please feel free to ask any questions regarding Data Science vs Software Engineering in the comments below.

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