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Data Science vs. Data Engineering: Understanding the Key Differences
Organizations in every sector rise and fall by their ability to turn data into actionable strategies. Whether that data includes public health statistics, consumer trends, operational benchmarks, or other information, two types of professionals help their teams make sense of it: data scientists and data engineers.
Despite similar titles, though, these professionals aren’t identical. In fact, starting a career in data science vs. data engineering requires learning a completely different skill set. Learning the differences is an important first step in starting the next leg of your career.
Data Science: Beyond the Numbers
As a field, data science is focused on discovering trends, building predictive models, and drawing meaning from datasets. This means that data scientists are vital to planning and decision-making in organizations across sectors.
The type of data that a data scientist analyzes varies by industry but typically includes the following:
- Analyze and interpret data to extract valuable insights
- Use algorithms and statistical models to predict trends and detect patterns
- Uncover knowledge and make data-driven decisions
- Manipulate data to create predictive and descriptive models
- Gather data from relevant sources
Data scientists may use artificial intelligence (AI) tools to gather and interpret data. However, AI doesn’t do all the work. Data scientists must typically have strong backgrounds in programming and data visualization to generate and communicate valuable insights.
Data Engineering: Building Digital Bridges
While data science is focused on data itself, data engineering is focused on building and maintaining the systems that make data collection, storage, and analysis possible. In that sense, data engineers are architects, ensuring that data flows seamlessly from its source to the people who need it.
Throughout the day, a data engineer might:
- Design and build systems and architectures for efficient data management
- Ensure the secure, unhindered flow of data from source to destination
- Build and maintain infrastructures that support massive data processing and storage
- Create data pipelines and optimize storage infrastructure
- Create and implement algorithms and other tools that colleagues can use to collect, access, and analyze data
Data engineers must typically understand programming and coding. They often need to be familiar with a broader range of database systems and warehousing tools than data scientists. They also have a more comprehensive understanding of how different tools fit together.
Data Science vs. Data Engineering: Key Differences
Data scientists and engineers are both integral to an organization’s success. While some overlap exists between the fields, the major differences between data science and data engineering lie in the skills required to work in each field, the type of degrees professionals possess, and the career opportunities.
Skills
Data scientists are responsible for turning insights into action. They typically need a strong background in statistics; analytics software; and programming languages such as R, SQL, and Python. These professionals must also be able to effectively communicate their findings to others in their organization, meaning that data visualization and critical thinking skills are just as important.
On the other hand, data engineers are often responsible for identifying the right solutions for an organization’s needs and maintaining them. This means that they need a firm grasp of data warehousing, database management, and system administration.
Degree Options
Data scientists and data engineers often take different paths to enter their fields. Data engineers often earn bachelor’s degrees in computer science, software engineering, or information technology (IT). These four-year programs typically cover the production-level and administrative skills needed to create and maintain software.
Those programs may include some classes that data scientists would also find useful, but in many cases, they don’t cover the data visualization, statistical, or qualitative skills needed to interpret data. For that reason, data scientists often pursue a graduate degree, such as a Master of Science (MS) in Data Science. These programs often take two years to complete and include classes on programming, statistical modeling, machine learning, and other skills needed to excel in this field.
Career Opportunities
While MS in Data Science graduates often work as data scientists, their ability to extract meaning from vast amounts of data makes them great candidates for many other positions, as well. These professionals may work as programmers, statisticians, business intelligence analysts, data modelers, consultants, and more. At the same time, they may work in fields ranging from robotics to public health.
Similarly, data engineers may work in a wider variety of roles than just data engineering, including data architect, big data engineer, computer scientist, and developer. In general, with a background in data engineering, professionals are equipped to take on positions that allow them to create and manage software solutions.
At Schiller, You’ll Become the Master of Data
If you’re weighing your options for careers in data science vs. data engineering, Schiller International University can prepare you for any number of careers in the data-driven economy. Our MS in Data Science program will equip you with deep knowledge, practical skills, and an international experience that sets our graduates apart in an increasingly competitive job market.
At Schiller, data science students learn skills such as programming, cloud computing, and machine learning through an advanced technical curriculum that emphasizes creative problem-solving and high-level analysis. Along the way, students learn to succeed in the global economy with:
- Four international campuses: Study in up to four different countries, understand their cultures and markets, and boost your global mindset.
- Experiential learning: Learn by tackling real-life challenges with top global companies, and acquire the tools and skills demanded in the current market.
- Global Employability Path: We want you to rise as a true leader and achieve success, and that’s why programs such as our Global Employability Path (GAP) aim to nurture you with all the knowledge and skills necessary to achieve it.
- Internationally accredited dual degrees: Our programs hold dual accreditation across Europe and the U.S., allowing you to work as a professional wherever you desire.
- Personalized advisors: You’ll never be alone. Our team of experts will be with you from the beginning to help you reach your maximum potential, guiding and assisting you in building your path.
- Global network: From day one, you’ll complement learning experiences with activities, events, mentorships, and many others, working directly with companies and professionals from around the world who are part of our partnerships.
Can you envision yourself exploring the vast world of data, making informed decisions, and leading the digital revolution? If so, request more information from Schiller and plot your trajectory today.
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