Software engineers and data scientists are two distinct, yet equally important roles in computer science. Although they both require knowledge of programming, there are several differentiating factors between software engineers and data scientists. Software engineers specialize in the creation and maintenance of hardware and software for end-user systems, while data scientists gather and analyze company data with the goal of extracting valuable insights for their business.
Today we will be discussing the differences between these roles, including responsibilities, typical projects, necessary skills, and education needed for each job.
Responsibilities and differences
Software engineers are responsible for creating and maintaining end-user systems. They use a structured approach to product design and are responsible for both hardware and software development. This includes programming mobile apps, websites, operating systems, and proprietary software for companies. These are the “architects” of the computer science industry, creating the products, functionality, and features that allow for data collection to be possible in the first place.
Data scientists are responsible for analyzing the information that is produced from these systems. They comb through mass amounts of structured and unstructured data (including data mining and big data sources) to uncover insights for making informed business decisions. What really makes data scientists special are their ability to absorb incoming data, understand it, translate it, and communicate its implications to key stakeholders through data storytelling.
Software engineers can expect to participate in the following projects:
- Develop applications for iOS, Android, Linux, Windows, etc.
- Design software for end-user interaction
- Build networks and operating systems for user-facing applications
- Regularly release updates to improve existing software and technologies
- Integrate multiple software products into one cohesive system
- Design and enforce IT standards/documentation
- Act as general IT manager and systems architect
Data scientists can expect to participate in the following projects:
- Research, test, and prototype ideas to create custom statistical models/algorithms
- Clean and organize mass amounts of data for more efficient analysis
- Utilization of clean data to analyze, test, create, and present results
- Gain understanding of company needs to better help in strategic planning, development of products, and development of solutions
- Create digestible data visualizations
- Work closely with your team to communicate analyses in an easy-to-consume manner
- Present results in a compelling way to internal stakeholders and external clients
Skills recommended for each position
- Proficiency in Python, Java, C, C++, and SQL
- Understands the four key principles of Object-Oriented Design (Abstraction, Encapsulation, Inheritance, Polymorphism)
- Software testing and debugging
- Ability to problem solve
- Logical thinking
- Written and verbal communication skills
- Proficiency in Python, Java, R, and SQL
- Ability to organize, analyze, and present data
- Big data tools like Hadoop, Hive, and Pig
- Experience in manipulating datasets and building statistical models
- Understanding of best practices of data mining and cleansing
- Efficiency in managing unstructured data
- Strong collaboration skills
The majority of employers prefer software engineers who have a bachelor’s degree or higher in software engineering, computer programming, software development, computer science, or another field of study within the programming discipline. Proficiency in common programming languages (Java, C, C++, Python, etc.), database/network fundamentals, project management, web development, and UI design will make you a more valuable asset to the company. Having strong technical writing and communication skills can also prove to be beneficial. Lastly, be sure to maintain an impressive portfolio of your best projects to help show potential employers your experience and level of expertise.
Data scientists generally have stricter education requirements. 40% of employers look for data science candidates who have advanced degrees, such as a master’s, MBA, or PhD. However, some companies will still accept undergraduate degrees in computer science, mathematics, statistics, information systems, economics, engineering, or physics. Proficiency in database management, big data analysis, predictive analytics, business intelligence, data cleansing, and data mining is also beneficial.
Can a data scientist become a software engineer?
The short answer is yes. Data science is, in many ways, a specialization of software engineering. Therefore, if data scientists have a basic understanding of programming and software engineering principles, it wouldn’t be too difficult to make the change. Therefore, switching from a data scientist to a software engineer is generally easier than transitioning between many other computer science positions.