Data science and software engineering are one of the most popular domains to work in. However, due to the similarities in both these domains, you might find it hard to choose between one of these domains. This article discusses data science vs software engineering to compare the requirements, salary, education, and job outlook in both these domains.

Skills Needed in a Data Science Job

Data science is an interdisciplinary domain that uses statistics, mathematics, data visualization, and machine learning tools to extract knowledge or information from raw data. It enables a company to extract actionable insights from raw data that is generated from the day-to-day operations of a business.

To get into data science, we need the following skills.

1. Programming: Data scientists need to be skilled at programming in languages such as Python, Scala, SQL, Julia, or R. These are the primary languages used in data science to extract and manipulate data.
2. Mathematics and Statistics: A strong understanding of mathematical and statistical concepts is essential for data science. We use statistical concepts and probability theory to summarize and describe datasets, perform sampling, and data modeling, and predict the behavior of users using the data.  We also need linear algebra and calculus to create mathematical models, perform multivariate analysis, and optimize models and algorithms. Without a strong foundation in statistics and mathematics, you will find data science hard to learn.
3. Machine Learning and Deep Learning: In data science, we use machine learning and deep learning to create models and perform analytical tasks that aren’t possible with simple statistical tools. A strong understanding of machine learning and deep learning concepts is essential to excel in data science.
4. Data Visualization: Data scientists need to be skilled at creating clear, effective visualizations of data in order to communicate insights to stakeholders. For this, you need to be good at using data visualization tools and techniques.
5. Soft skills: Data science is a business-oriented job. To communicate with the stakeholders, you need to have good communication and collaboration skills. Soft skills will help you work effectively and efficiently.

You can learn these skills with data science training.

Skills Needed in a Software Engineering Job

Software engineering is the domain of computer science in which we design, develop, test, and maintain software programs. It involves applying engineering and design principles to software development for creating high-quality, reliable, and maintainable software.

In a software engineering job, you need to perform different tasks such as requirements gathering, design, implementation, testing, and maintenance. In all these tasks, you will need a range of technical and non-technical skills. Here are some of the key skills required for you to excel in a software engineering job.

1. Proficiency in programming languages: Software engineers must be proficient in one or more programming languages. The choice of programming language depends on the company in which you are working and the type of software you are going to develop. You might need to learn one or more languages from C, C++, C#, Java, Python, JavaScript, Go, Rust, and others.
2. Familiarity with software development tools: As a software engineer, you are required to be familiar with software development tools, such as Integrated Development Environments (IDEs), version control systems, and project management tools. Without these tools, you won’t be able to write code and develop software efficiently.
3. System Design: In a software engineering job, you must understand how to design software systems. System design includes designing the software architecture using design patterns for high-level and low-level design to ensure the software is scalable, maintainable, and reliable.  Also, system design is one of the most important parts of software engineering interviews. Hence, to even get into a software engineering job, you need to know system design. 
4. Strong problem-solving skills: Software engineers must be able to analyze complex problems and develop effective solutions using critical thinking and problem-solving skills. You need to be able to break down complex problems into smaller parts and implement them.
5. Data structures and algorithms: We need to create software applications that are highly scalable and have good performance. In this task, knowing data structures and algorithms helps you implement each functionality with efficiency. Therefore, knowing data structures and algorithms is a must.
6. Networks and Security: Software applications communicate over the internet. You will also need a proper understanding of network and security protocols to protect the data from malicious attacks while transmitting and storing.  

Data Science vs Software Engineering Salary

The salary of a data scientist or a software engineer depends on various factors. The major factors include your education, location, company, and experience. In 2022, the median salary for a data scientist job in the USA was $120,000/year with a sample of 10,071 employees. On the other hand, the median salary of a full-stack software engineer was $101,794/year among 11,252 employees according to a Glassdoor survey.

In lead roles, the expected salary of a data scientist can be $166,220/year whereas that of a senior software engineer can be $150,000. In senior roles, the salaries for data scientists as well as software engineers are more than $200,000. 

So, if we compare the salaries of a data scientist and a software engineer, data scientists tend to earn more in the early phase of their careers. As the career proceeds, the salaries for both data scientists and software engineers become almost equal.

Data Science vs Software Engineering: Education

When we talk about education, software engineers enjoy leniency compared to data scientists. 

• For software engineering jobs, you can quickly start after your undergraduate degree. After a B.Tech in computer science, information technology, or related courses, you can directly start working as a software engineer. Even if you have a bachelor’s degree in Mathematics and Physics, you can easily start with a software engineering job after a coding boot camp.
• For data science jobs, you need to be good at many aspects like programming, statistics, machine learning, data analysis, etc. Due to this, data science jobs are a tough nut to crack. To excel in a data science job, you will probably need a master’s degree or even a Ph.D. However, you can also start with a data analyst or machine learning engineer role and slowly move to a data scientist job with increasing experience and knowledge.

So, if you compare education requirements for a data science vs software engineering job, data science requires you will even need a Ph.D. to excel. However, A bachelor’s degree and a coding boot camp are sufficient for a software engineering job.

Job Outlook in Data Science vs Software Engineering

Both data science and software engineering are a domain in which everyone wants to work. 

• In data science, you can work as a data analyst, machine learning engineer, data engineer, data architect, statistician, business analyst, data scientist, etc. The compound annual growth rate for data science jobs in the data science domain is close to 30 percent. 
• In software engineering, you can work as a frontend developer, backend developer, software tester, etc. The compound annual growth rate for software engineering jobs is projected at approx 22 percent.

Data science is a new and exciting field. The companies are still waiting to adapt data science to their workflow. Due to this, data science jobs have a better outlook than software engineering. Looking at the current trends, the job outlook in data science is going to be more than software engineering jobs for sure in the near future.

Data Science vs Software Engineering Which is Harder?

Data science and software engineering both require exceptional academic, analytical, and problem-solving skills. However, the nature of jobs in both these domains is very different. Due to this, the perceived difficulty can also be different for data science vs software engineering. 

• At a beginner’s level, software engineering is harder than data science. In the data science domain, you can start with a data analyst or statistician role with zero knowledge about the other aspects of data science like machine learning, data engineering, etc. However, in a software engineering job, you need to be proficient in programming, data structures, algorithms, etc from day one. As you are required to be at least conversant with all aspects of software engineering while starting, software engineering is harder than data science for a beginner. 
• At a senior level, data science is hard compared to software engineering. Once you have worked in software engineering for five or six years, you will become proficient in most of the software engineering tasks like system design, data structures, algorithms, etc. However, you cannot master every aspect of data science due to its interdisciplinary nature. You need to know everything starting from statistics to deep learning concepts. Due to this, data science is harder to master than software engineering. 

Conclusion

In this article, we discussed different aspects of data science vs software engineering to compare different aspects of both these domains like education, salary, and difficulty. To learn more about the data science domain, you can read this article on Python vs R for data science. You might also like this article on whether should you learn SQL or Python first.

I hope you enjoyed reading this article. Stay tuned for more informative articles.

Happy Learning!

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