Data Science vs Software Engineering: The Key Differences

Software engineering is already a well-established field, with many tech enthusiasts keen on pursuing a career in the area. But although data science is a similar field that also requires a background in computer science, it is much less popular. This is partly because data science is a newer career path.

Though data science and software engineering are similar in many ways, as you dig deeper you’ll discover that there are a few key differences. This includes both the skillsets of the professionals working in these fields and how they execute projects.

If you’re interested in pursuing a tech career but are unsure of whether you want to take on a more well-established role in software engineering, or in the newer field of data science, then keep reading. In this guide, we’ll cover the most significant differences between the two career options to help you decide which is the best for you.

What Is Data Science?

Data science is a field in which raw data is collected and processed to achieve a desired goal. In this area, artificial intelligence (AI) systems are used to perform tasks that would usually require human intelligence. Data science professionals apply machine learning models to solve a variety of problems.

Professionals in this field must also develop their own models, which will then carry out tasks such as monitoring stocks or counting the number of trees in a country.

Data science, AI, and machine learning are increasingly being used by more businesses across industries. Companies of every size that wish to remain competitive in the age of big data need to effectively use data science technologies, or risk being left behind.

To draw balanced and detailed results from raw data, data science engineers must have additional background knowledge in a number of disciplines and areas of expertise.

Data scientists will be required to sort through huge amounts of information and communicate only the pieces that will help a business improve. Data scientists should have skills in data engineering, math, statistics, advanced computing, data visualizations, and coding. 

Data Scientist Salaries and Job Outlook

The average data scientist salary is $100,560. However, this will vary based on job experience and education. The starting salaries for all data science positions are relatively high, as the supply of data professionals has not yet caught up with demand.

In general, companies are looking for individuals who are familiar with the concepts of machine learning, AI, and big data. They will also give preference to experts who have experience in an industry related to the hiring company. 

The tech industry is booming like never before, and data science and AI are expected to be major contributors to future growth. By the year 2026, employment opportunities for computer and information research scientists are expected to rise by 19 percent, much faster than any other occupations, according to the US Bureau of Labor Statistics. 

What Is Software Engineering?

Software engineering is the detailed study of the design, development, and maintenance of software. These professionals are the architects of the digital world, and their work affects almost every aspect of our society, from education to entertainment.

Software engineers develop products from scratch, going through several stages. They will start with a development phase, then design and debug the software until their concept has been realized in the final product. 

Software Engineer Salaries and Job Outlook

In the modern digital era, software engineering has taken up a significant piece of the world economy. So, how much does a software engineer make? The average pay for a software engineer is $92,824. The starting wage can be as low as $55,236, with higher-level employees making up to $155,991.

Software development was ranked as the best job in the US in 2019 by the US News and World Report. According to the US Bureau of Labor Statistics, software engineering jobs are expected to grow by 21 percent before 2028. 

There are many specific careers within software engineering that will also be part of this growth, such as application development, web development, systems development, and embedded systems development.

Data Science vs Software Engineering: The Most Important Differences and Similarities

The demand for software developers is currently higher than that of data scientists. Regardless, the career option you choose should depend entirely on your interests.

If you like building applications and writing algorithms, then software engineering is right for you. But if you’re more into statistics, trends, and researching, then data Science will be a better fit. Let’s take a deeper look at the similarities and differences between the two.

Difference: Methodologies

Extract, transform, load, or ETL, is the main method that data scientists use to extract information. It involves extracting data from various sources, then reorganizing it into a pattern that is more easily digestible. Software engineering is defined by the Software Development Life Cycle.

Difference: Approaches

Data science and software engineering both take different approaches to project management. Data science is a process-oriented field, whereas software engineering uses existing frameworks.

Difference: Skills

The major essential skills utilized in data science include machine learning, programming, and statistics. In software engineering, you will need to know multiple programming languages and have strong problem-solving skills.

Similarity: Programming

Data science and software engineering both use some programming skills, usually in the coding language Python.

Similarity: Automation and Testing

Automation and testing is the process of developing scripts and running them through software to test it. Data science uses a similar process, which is called test automation.

Similarity: Solving Business Problems

Companies such as Intersect show how software can be applied to business analytics in order to solve business-related issues. Data science also helps solve problems by analyzing and studying information collected by a business.

Data Science vs Software Engineering: Pros and Cons

In data science, information is used to make decisions that can improve a company’s value. But these companies will most likely also need a skilled software engineer to improve operations by creating websites, apps, and more. Below, we’ll review the pros and cons of both of these career choices.

Data Science Pros

• An abundance of positions. Since data science is a new field, there are few people who have the required skills to enter it. So, there are many job openings in this area, should you choose to study it.
  
   
• Versatility. Data science has many possible applications. It is used in health care, banking, and e-commerce. As a data scientist, you’ll have the opportunity to work in these fields and many others.
  
  
• High salary. The compensation for data scientists is excellent. 
  
  

Data Science Cons

• Complexity. Some of the tools and techniques used in data science are complex and require a lot of practice to master. You will also have to know which tools to use in different circumstances.
  
  
• Data security. The information and insight derived from the data obtained can be misused against an organization, so it is important to make sure all data is protected.
  
  
• Difficult to become an expert. Since data science is a very vast field, it is difficult to master it.
  
  

Software Engineering Pros

• High demand. Software engineers are in high demand. Most mid-sized and large companies will have a team to develop, maintain, and support software.
  
  
• Freelance work. Not only can you work from anywhere, but the average freelance salary of a software developer is often higher than the average permanent salary.
  
  
• Global recognition. Software engineering has no geographical boundaries. Having the right degree and credentials means you can get hired anywhere in the world.
  
  

Software Engineering Cons

• Never-ending learning curve. Though this might also be considered positive, it can become overwhelming to continually improve your skills in order to stay relevant.
  
  
• Intense competition. This industry is highly competitive. You will need to work hard and upgrade your knowledge if you want to keep your job.
  
  
• Mentally draining. The work you do can sometimes be very challenging, and will often require that you put your problem-solving skills to use.
  
  

Data Scientist vs Software Engineer: Required Skills and Duties

Data scientists are professionals who must have a technical background, as well as a strong understanding of machine learning. Software developers use their technical knowledge to develop products that machines will run on. 

Data Scientist Required Skills

• Python coding. Python is the most common programming language used in data science, so you should become fluent in it.
  
  
• SQL database. SQL is a programming language specifically designed to help you access, communicate, and work on data, so it is essential to any data science career.
  
  
• Statistics and probability. These are the pillars of data science. You must be familiar with these in order to analyze data properly.
  
  

Data Scientists Required Job Duties

• Analytics. A data scientist designs and evaluates advanced statistical models to predict user behavior. They also must select which visualization technique should be used to best convey data analytics findings to a client. 
  
  
• Managing data. A data scientist will play a role in managing data within a company by defining the goals of a project and using the proper tools to execute the necessary data analysis.
  
  
• Collaboration with stakeholders. Data scientists work closely with marketers and other stakeholders to communicate the possible outcomes of a project. 
  
  

Software Engineer Required Skills

• Programming and coding. Computer programming is one of the fundamental skills of a software engineer. It is the basis of software engineering, so it is crucial that you learn some of these languages, such as Java and Python.
  
  
• Multitasking. As a software engineer, you’ll have to work on multiple projects at once under strict deadlines. To succeed, you must be productive and multitask.
  
  
• Problem-solving. Problem-solving is an essential skill, as it will help you debug programs. 
  
  

Software Engineer Required Job Duties

• Research and analysis. Software engineers spend a lot of time researching solutions and conducting object-oriented analysis.
  
  
• Implementing programs. Writing efficient code for a project is the most important duty of a software engineer.
  
  
• System design. A back-end software engineer needs to design all the elements of a system in order to bring a designer’s idea to life.
  
  

Should You Study Data Science or Software Engineering?

You should study whichever career is more appealing to your interests. With so many job options currently available in tech, it can be pretty difficult to decide which one is the best for you. But after reviewing all the benefits and drawbacks of these two fields, we hope you have been able to determine your preference.

Software engineering will be the best choice if you are interested in creating applications or websites using code. But if you like to be surprised, and love poring over statistics to find patterns, then you’re better off becoming a data scientist. 

The salary package for both software developers and data scientists is very similar, but in data science, you will face less competition for jobs.