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Data science vs. data engineering: What's the difference?

Data science is a growing field with a booming job market. Every day, companies look for new ways to use their data, so the need for data professionals has never been greater.

Both Data Scientists and Data Engineers rank highly in LinkedIn's list of the top 15 emerging jobs in the U.S. But what's the difference between the two? Because of data science's wide range of applications and the nebulous responsibilities and titles of data professionals that vary between companies, the distinction can be hard to discern.

To help you understand the difference (and clarify your potential career path), we'll explore both data science and data engineering in the paragraphs below. Then, we'll show you how to break into these emergent fields.

What is a Data Engineer?

Without Data Engineers, a Data Scientist's job would be much harder. Data Engineers work in the background designing the databases and data stores that hold a business's data cache. They also build the pipelines that transform this data into formats that are more useful for Data Scientists.

Data Engineers often deal with raw data that comes from analytics and tracking tools, IoT devices that output sensor data, sales data from e-commerce sites, and more. This data could have errors, misconfigured data points, and information that only applies to the data systems. There could also be a lot of it to deal with, and the data doesn't stop coming in most industries.

It's up to a Data Engineer to design and create an architecture that supports retrieving the data from all these sources and storing it in an easy-to-use format. To do so, they need to be skilled with databases, programming languages like SQL, ETL (Extract, Transform, Load) tools, and other data processing tools.

This job can be complex because it's not as simple as moving the data around. Errors and misconfigured data must be either removed or fixed. Sometimes system-specific codes in the data have to be looked up in another system to make sense in the final dataset. Or one dataset may have to be merged with another. Finally, the results can be delivered to Data Scientists or Data Analysts who use it to provide business insights.

What is a Data Scientist?

Data Scientists wrangle big data. They collect and analyze large sets of both structured and unstructured data. Most come from a variety of backgrounds since the skills needed to become a Data Scientist go beyond programming or computer science skills. A Data Scientist must have technical skills, but they must also know both mathematics and statistics.

It's a Data Scientist's job to discover the questions they need to ask to make a business grow. For example, what type of revenue increase would a company have if they added a new product line? After asking this, they would look at the data they have and see if they can pull the answer from it. If the data isn't available, they may work with a Data Engineer to set up a pipeline to retrieve it.

Once a Data Scientist has their data, they prepare it so it can be used to create predictive and prescriptive machine learning models. To do this, they may have to transform and clean the data even more, and they also may have to research their industry further to determine which machine learning models and methods will work the best for the information they're trying to generate.

Once Data Scientists have gathered the insights they need, they will have to turn these insights into a story they can present to stakeholders. Once those results are accepted, they have to automate the process they used to generate and deliver reports to these stakeholders regularly.

What's the difference between data science and data engineering?

Now that you know what both a Data Scientist and Data Engineer do daily, it is easier to see the difference between the two disciplines. The key differences are:

  • Data Engineers collect, move, and transform data into pipelines for Data Scientists, while Data Scientists prepare this data for machine learning and use it to create machine learning models.
  • The final result of a data engineering process is data that is easy to use and process, while the final results of data science are reports and insights that are presented to business stakeholders.
  • Data Engineers use programming languages to move, transform, and clean data, while Data Scientists use programming languages to create machine learning models.

While we draw a line between data engineering and data science in this article, this line is usually blurry in the real world. So whichever way you chose to go, it doesn't hurt to know both disciplines.

Getting started with data science and data engineering

Data is the new gold, especially in the business world. Because of this, choosing either data science or data engineering as a career path means you will be in demand in the job market. After going over the details of each job, you should have a better idea of which job will be the most rewarding for you.

If you're leaning more towards the Data Scientist role, then our Data Scientist Career Path is for you. It's a beginner-friendly course that will teach you how to become a data-driven decision-maker.

But that's not all we have for future Data Scientists. Building a Machine Learning Model with Python will introduce you to machine learning, another tool in the Data Scientist toolbox. For even more, check out our data science course catalog.

If you're interested in data engineering, you'll need to Learn SQL so you can query databases effectively. After that, Learn Python to start building pipelines for your data and create your own databases from scratch with our Design Databases with PostgreSQL Skill Path.

Whichever you choose, we wish you luck on your journey into the world of data.

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Stephan Miller

Stephan Miller

Stephan Miller has been a full-stack, mobile, and machine learning developer for two decades and has written code for companies both big and small, both startups and established businesses.

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Data science vs. data engineering: What's the difference?
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