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Data science vs. Data Engineering: Making the Best Choice

In the field of big data and analytics, two prominent roles stand out: Data Scientist and Data Engineer. Both play crucial roles in extracting insights from data, but they have distinct focuses and skill sets. If you're considering a career in this field, you might be wondering which path is the best choice for you. Let's explore the differences between data science and data engineering, we gathered the most frequent questions to help you make an informed decision.

Without further ado, here are the most frequently asked questions and answers about your career choice in the Data field.

Which is better: data science or data engineering?

The question of which is “better” between data science and data engineering depends on your interests, strengths, and career goals. Data science is ideal for those passionate about deriving insights from data, building predictive models, and uncovering trends. On the other hand, data engineering suits individuals who enjoy designing and building robust data pipelines, optimizing databases, and ensuring data quality.


What is the main difference between data science and data engineering?

  • Data science: Data Scientists focus on analyzing data to extract meaningful insights, develop predictive models, and communicate findings to stakeholders. They work with statistical methods, machine learning algorithms, and data visualization tools to uncover patterns and trends in data.
  • Data engineering: Data Engineers, on the other hand, are responsible for designing, building, and maintaining the architecture of data systems. They focus on developing pipelines to collect, clean, and transform data, ensuring it is accessible for analysis by Data Scientists and other stakeholders.

Is data engineering more in demand than data science?

Both data science and data engineering are in high demand, but the demand for Data Engineers has been particularly strong in recent years. This is due to the increasing volume of data that needs to be managed, processed, and stored efficiently. Companies across industries rely on Data Engineers to build and maintain the infrastructure needed to handle vast amounts of data. Do you want to know more about this interesting career? Take a look at our in depth Guide to becoming a Data Engineer.

However, do not believe that data science is not in demand. Data scientists are highly sought after in various industries, from tech to healthcare to finance, due to their expertise in analyzing and deriving insights from complex datasets. With the exponential growth of data, businesses rely on data scientists to make informed decisions, improve processes, and gain a competitive edge.


Which is harder, Data Engineer or Data Scientist?

The difficulty of data engineering versus data science largely depends on your background and skills. Data engineering can be challenging due to the complexity of designing scalable data pipelines, optimizing databases, and ensuring data integrity. Data Scientists, on the other hand, face challenges in understanding statistical models, building accurate predictive algorithms, and effectively communicating insights

Your preparation for either role is critical; a well-structured education will significantly impact your ability to adapt to the needs of different companies. For aspiring Data Engineers, a strong foundation in database management, data storage, and advanced batch pipelines is crucial. Therefore, a highly technical training program would be the optimal choice.

On the path to becoming a Data Scientist, it’s essential to cover the fundamentals of data analysis methods and key tools. Mastery of machine learning, deep learning, and a solid understanding of AI solutions and advanced predictive models can set you apart. This unique combination of skills is often found in hands-on bootcamp training programs.

Do you still have doubts about whether studying to become a Data Scientist is worth it? Take a look at this complete guide.


Which career, data science or data engineering, allows me to study or work from home?

Both data science and data engineering offer opportunities to study and work from home, especially with the rise of remote work and online learning platforms.

Data science online study or work enablers:

  • Online courses: Many reputable training providers and online platforms offer data science courses that can be completed entirely online. This allows you to learn from home at your own pace.
  • Remote work opportunities: With the right tools and internet connection, much of the work can be done remotely. Many companies now offer remote positions for Data Scientists, allowing them to work from anywhere.
  • Virtual collaboration: Data science projects often involve collaboration with team members who may be located in different parts of the world. Tools like Slack, Zoom, and collaborative coding platforms make it easy to work together remotely.

Data Engineering online study or work enablers:

  • Online learning: Like data science, data engineering courses are available online. You can learn about databases, data pipelines, and cloud technologies from home through various online platforms.
  • Remote work opportunities: Many data engineering tasks can be done remotely and stored in the cloud. Companies that rely on cloud platforms like AWS or Azure often have remote positions for Data Engineers.
  • Virtual teams: Data engineering projects often involve collaboration with Data Scientists, Software Engineers, and other team members. Remote work is facilitated through virtual meetings, shared code repositories, and cloud-based tools for collaborative work.

Read more about why choosing an online bootcamp over self-teaching can be the right choice for you.

Between Data Engineer and Data Scientist, who earns more?

Salaries for Data Engineers and Data Scientists can vary based on factors such as experience, location, and the specific industry. Generally, Data Scientists often command higher salaries due to their specialized skills in advanced analytics and modeling. And of course, we have a Data Science Salary Guide available for you.

On the other hand, experienced Data Engineers with expertise in big data technologies and cloud platforms can also earn competitive salaries. If you want to learn more about this important topic, you should check out Data Engineer Salary: Trends and Insights

Which should I choose: training for data science or data engineering?

Choosing between training for data science or data engineering depends on your interests, career goals, and strengths. Here are some considerations:

  • Pick data science training if: You have a passion for analyzing data, building predictive models, and deriving actionable insights. Data science training will focus on statistics, machine learning, programming languages like Python or R, and data visualization, and the big plus OpenAI.

Pursue your passion and enroll in the upcoming Data Science & AI course.

  • Pick data engineering training if: You enjoy working with data infrastructure, designing pipelines, and optimizing databases. Data engineering training will cover databases (SQL, NoSQL), big data technologies, and programming languages and tech tools (Python, Kubernetes, Fivetran, to mention a few).

Select the top Data Engineering bootcamp and submit your application for the next available start date.



In the data science vs. data engineering debate, there is no one-size-fits-all answer. 

Both roles are integral to the data ecosystem, each with its unique set of challenges and rewards. If you have a knack for uncovering insights from data, love building models, and enjoy communicating findings, data science might be the right path for you. On the other hand, if you thrive on designing efficient data systems, optimizing pipelines, and ensuring data reliability, data engineering could be your calling.

Ultimately, the best choice comes down to your interests, strengths, and career aspirations. Whether you choose data science or data engineering, both paths offer exciting opportunities in a field that continues to evolve and grow. So, take the time to explore both roles, consider your skills and passions, and embark on a rewarding journey in the dynamic world of data analytics and engineering.


Once you have made your choice, Le Wagon offers you the opportunity to develop all the skills you need to succeed in your Data career. Additionally, you can have a chat with our career advisors in case you have any other questions about the main differences and advantages between data science and data engineering careers. And of course, to ensure there is no delay in kick-starting your training, you can learn all about our available financing options.

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