Data Science vs. Data Engineering: What’s the Difference?


Data jobs have been on the rise for years. Companies in virtually every industry are looking for experts to help them manage and make sense of the huge amounts of data we create, which is why roles like Data Scientist and Data Engineer rank highly on Glassdoor’s list of the 50 Best Jobs in America. But which path is right for you?

Ahead, we’ll explore the nuanced differences between data science and data engineering, and then show you how to break into these exciting 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?

A Data Scientist’s job is also complex. It requires more than programming and computer science skills — they also need to know math and statistics and have a solid understanding of the industry they work in. Companies rely on Data Scientists to utilize their data effectively and improve their systems and processes, create new products, and find insights that can help inform their strategies.

As a result, a Data Scientist’s role can involve a wide range of responsibilities — but their exact duties will depend on the company they work for and the problems their teams are trying to solve. There are many different careers you can have as a Data Scientist, but some of the most popular include:

  • Analytics Specialists: Sometimes called Data Analysts, these Data Scientists collect and analyze large sets of both structured and unstructured data to find trends and insights. Then, they share their findings with other stakeholders with data visualization techniques.
  • Machine Learning Specialists: These Data Scientists use data to create predictive and prescriptive machine learning models and algorithms. Other responsibilities include performing cluster analysis, feature engineering, and tuning hyperparameters.
  • Machine Learning Engineer: These Data Professionals work with big data and turn algorithms into machine learning applications.

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 choose 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 paths are for you. These beginner-friendly courses will teach you the skills you need to land an entry-level position in your preferred specialization and show you how to use them to build portfolio-worthy projects.

And if you’re more interested in data engineering, stay tuned! We’ve got a Data Engineer career path coming soon. In the meantime, you can 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.

Data Science Courses & Tutorials | Codecademy
Data Scientists try to make sense of the data that’s all around us. Learning Data Science can help you make informed decisions, create beautiful visualizations, and even try to predict future events through Machine Learning. If you’re curious about what you can learn about the world using the data p…

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