Data Science vs Data Engineering: What's the Difference?

Data engineers build the architecture and infrastructure that enables data scientists to draw inferences. Learn about differences in skills, jobs & education.

Data science vs Data engineering

With the demand for data professionals rapidly rising as big data becomes increasingly valuable to businesses, pursuing a career in data science or data engineering is a promising career move. While the two fields are related, they have distinct differences in their responsibilities, skills, training and more.

Where data engineers create architecture to store, clean, and process raw data so it’s accessible for data scientists, data scientists analyze the prepared data (using analytical systems, machine learning, and statistical methods) to discover hidden patterns, glean valuable insights and make predictions to aid businesses or organizations. Data engineers are crucial to data scientists, and data scientists are crucial to businesses that want to utilize their data to its full potential.

Understanding these and other differences between data science and data engineering will help inform your educational decisions to prepare for the career you want. This article will provide an in-depth discussion of the differences between the two booming fields.

Differences in Roles & Responsibilities

One of the main differences between a data engineer and a data scientist lies in their roles. A data engineer works with raw data that may be flawed, unformatted, or system-specific. Their responsibilities include developing and implementing cyber-secure architecture (like databases and processing systems) to store and process that raw data and support the needs of data scientists. Data engineers correct errors and make it more accessible for data scientists. Their tasks often include:

  • Constructing, testing and maintaining databases and large-scale processing systems.
  • Merging systems so system-specific data can be utilized.
  • Validating the data to identify and remove errors.
  • Recommending and implementing ways to improve data reliability, efficiency and quality.
  • Developing processes for producing, mining, and modeling data sets.

Data scientists, on the other hand, take that prepared data and use machine learning, statistical methods, and analytical programs to parse out valuable insights and make predictions. They then present that information to businesses and stakeholders. Their tasks may include:

  • Conducting research to discover industry or business questions/problems.
  • Working with large volumes of expansive data to find answers and solutions to those questions/problems.
  • Examining data to discover hidden insights and patterns.
  • Utilizing advanced analytical, statistical and machine learning methods to prepare data for prescriptive and predictive modeling.
  • Automating processes so clients can continue to receive insights on a regular basis.
  • Using storytelling and data visualization to present the information to clients and stakeholders in an easy-to-understand, digestible format.

Differences in Tools, Programming Languages & Software

Whether you decide to become a data engineer or data scientist, you will leverage tools like Java, Scala, and C#. However, each specialty works with unique tools, programming languages, and software beyond those, and they can vary depending on the context of the data engineer or data scientist’s role in an organization.

Data engineers work primarily with data processing, framework, storing and analyzing tools, while data scientists utilize data manipulation and visualization tools. Below are a few examples of tools commonly used by each:

Data Engineers

  • SAP
  • Oracle
  • Cassandra
  • PostgreSQL
  • Redis
  • MongoDB
  • Hive

Data Scientists

  • Python
  • R
  • SPSS
  • SAS
  • Tableau
  • RapidMiner
  • Gephi

Differences in Education Background

Computer science is a popular education choice for aspiring data engineers or data scientists, as it’s a practical area of knowledge that will aid both professions. However, each profession can benefit from other fields of study or experience, and that’s where they differ.

Education Requirements for Data Engineers

Data engineers tend to come from engineering backgrounds. If you’re preparing for undergraduate study, consider degrees in computer sciences or engineering. To give yourself a competitive edge, earn program-specific certifications afterward, like the associate-level SAP certification for Master Data Governance.

Graduate programs can increase your competitiveness and make you eligible for higher pay and more advanced professional opportunities, as well as expand your expertise in your field. If you’re applying for graduate programs for a career as a data engineer, there are several beneficial areas of study to choose from. Rice University offers a few relevant degrees, such as the Master of Computer Science (MCS), both 100% online or on-campus.

Education Requirements for Data Scientists

Data scientists may have more diverse backgrounds like mathematics and statistics, research and analysis, or business and management consulting because their roles require numerous areas of interdisciplinary knowledge across multiple fields. Earning an undergraduate degree in computer systems and information sciences, mathematics, or statistics would lay a solid foundation for a career as an entry-level data scientist.

If you’re interested in master’s programs that will enhance your role as a data scientist, Rice offers several options. For "career" Data scientists, Leads or Chief Data Officers, the Professional Master of Data Science (MDS) program features a customizable curriculum with either a Business Analytics or a Machine Learning specialization, plus the D2K Lab (Data To Knowledge Lab) Capstone that provides hands-on experience addressing real-world problems. For Engineers who want to remain in their engineering technical area (like mechanical or biomedical) while learning big data and data science techniques and skills, Rice offers a Data science specialization within the Master of Engineering Management & Leadership degree.

While education highlights another difference between data science vs. and data engineering, it’s important to note that different backgrounds are beneficial to developing careers in both areas. The professionals in data-related fields come from a variety of education and work histories, including research, engineering, geology, management consulting, data analysis, database administration, and much more.

Differences in Pathway to Data Engineering vs Data Science

The career paths of a data engineer vs. data scientist may vary, but they follow the same general structure: learning relevant knowledge, getting an undergraduate degree and certifications, gaining entry-level experience and building a portfolio, and then getting a master’s degree to expand your career opportunities.

Below are more details on the areas data engineering vs data science career paths may include:

Career Paths for Data Engineers

To start your career as a data engineer, you should begin learning about programming languages, big data and ETL tools, scripting, databases, and the automation, warehousing, computing and processing of data. You may also want to learn the basics of machine learning and data visualization to help you understand the needs of data scientists and how they’ll interact with the data.

Pursue an undergraduate degree in computer science, IT, software engineering, math, or business, then pursue a master of computer science or computer engineering. After you’ve completed your education and have several certifications, possible careers to consider include data engineer, data architect, database developer, and machine learning engineer. Earning your master's degree could help you get senior, chief, and director titles.

Career Paths for Data Scientists

For data science, you’ll need to study computer and data science basics, including Python, mathematics and statistics, recommendation systems and matrix algebra, machine learning (including deep learning, neural networks, and computer vision), natural language processing, and time series. It’s also helpful to study business so you have a grasp of finance, economics and growth strategy.

You could start your career path with an undergraduate degree in computer science, data science, mathematics, statistics, or business, with relevant minors. After completing the degree and additional certifications, pursue a master of data science, prioritizing programs that offer specialties that match your career goals (like MDS@Rice with a specialization in machine learning).

Potential job titles include data scientist, data analyst, business intelligence analyst, statistician, marketing analyst, and artificial intelligence architect. As with data engineering, advanced degrees and certifications will give you a competitive edge for senior, chief, and director roles.

Differences in Job Title & Salary

As you may have noted above, there are general differences in the roles that a data scientist or data engineer would take on. Data engineers typically work to source, store, and organize raw data for businesses, creating and managing secure databases and data pipeline formations. Data scientists’ roles are somewhat broader and include responsibilities like applying data insights to marketing and business strategies.

Your salary will be influenced by your experience, location, responsibilities, and scope of work, but below are the current averages for job titles in each industry.

Getting Started as a Data Engineer or Data Scientist

Now that you know more about each field, you can make an informed decision about the profession you wish to study. If you’re purely interested in working with raw data and computing, consider data engineering. If you prefer a more diverse position that blends communication skills, data science, machine learning, problem-solving, and creativity, then you may be a perfect fit for a data scientist position.

Whether you’re interested in becoming a data scientist or data engineer, you’ll need to start by securing an undergraduate degree in a relevant STEM field like Computer Science, Statistics or Engineering. While continuing education will be a constant for most throughout their careers, after at least 1-2 years of work experience, professionals may start to consider whether a master's program is right for them. Rice's Master of Data Science was designed specifically for the needs of aspiring Data Scientists, whereas the Master of Computer Science curriculum would be a better fit for aspiring Data Engineers.