Topics In Demand
Notification
New

No notification found.

Data Engineer vs. Data Scientist: Which Is Better?
Data Engineer vs. Data Scientist: Which Is Better?

February 24, 2023

298

0

Careers in data science have been in higher demand recently, with employment expected to grow substantially faster than average for other professions between 2020 and 2030. This demand is going to stay as long as businesses focus on producing, collecting, and analyzing big data to support their operations.

 

The following article explains the key distinctions between a data scientist and a data engineer, two of the more prominent careers in data science. It also includes all the information you need to choose the best career path for you, from duties and roles to average salaries, educational requirements, and the various pathways that can lead to a dream career working with data.

 

What Does a Data Engineer Do?

 

An expert in data who prepares the infrastructure for data analysis is known as a data engineer. They are worried about the production readiness of raw data and its various components, including formats, resilience, scalability, data storage, and security. Engineers who work with data design, produce, test, link, preserve, and enhance data from many sources. Additionally, they offer the methods and infrastructure necessary for data generation.

 

Its main objective is to combine several big data technologies that enable real-time analytics to create free-flowing data pipelines. To make data accessible, data engineers build challenging queries.

 

What Does a Data Scientist Do?

Data engineers have collected the data; data scientists are focused on obtaining new knowledge from the data. They do online studies as part of their job, create hypotheses, and apply their knowledge of statistics, data visualization, data analytics, and ML algorithms to uncover trends and forecasts for the business.

 

Additionally, they collaborate with business executives to understand their needs and convey outcomes in a way that both a verbal and visual audience in business can appreciate.

 

Qualifications and Requirements

Both data engineers and data scientists hold a bachelor's degree in computer science or a closely related field like statistics, mathematics, economics, or information technology. Even while employers frequently look for candidates with advanced degrees, online data science certification courses can help you start a career in data science or data engineering. 

 

What Qualifications Are Needed to Become a Data Engineer?

Programming linguists with expertise in Python, Java, SQL, and Scala are common among data engineers with software engineering backgrounds. Instead, they have a degree in mathematics or statistics, enabling them to apply various analytical approaches to deal with business difficulties.

 

Most employers want candidates for data engineering positions to have a bachelor's degree in computer science, applied math, or information technology. Many data engineering certificates may also be required of the applicants. Furthermore, it is advantageous if they are skilled in building sizable data warehouses that can gather, modify, and load data sets on top of them.

 

What Qualifications Are Needed to Become a Data Scientist?

Data scientists frequently have to deal with enormous amounts of data without any clear business challenges. In this scenario, the data scientist will be expected to examine the data, create pertinent questions, and present their findings. Therefore, data scientists need to be knowledgeable in big data infrastructures, machine learning techniques, data mining, and statistics. To run their algorithms correctly and effectively, they must also be conversant with all the most recent technological developments since they work with data sets in various formats.

 

Programming languages, including Python, R, SQL, and Java, as well as platforms like Hadoop, Hive, Cassandra, and MongoDB, must be understood by data scientists. 

Salary Outlook 

How Much Money Do Data Engineers Make?

The type of work, necessary experience, and location of employment are all factors that affect data engineers' compensation. The average annual income, according to Glassdoor, is around $142,000.

How Much Money Do Data Scientists Make?

A data scientist's pay is based on work, abilities, credentials, and location. Around $139,000 is the average annual salary for a data scientist, according to Glassdoor.

 

So, Which Is Better for You, a Data Scientist or a Data Engineer?



 

While having similar areas of knowledge, data scientists and data engineers play different roles, and the professions sometimes need particular personality types.

 

Data engineers are particularly interested in the organization and structure of data. They are powerful developers who enjoy learning and utilizing new technologies, developing inventive ways to improve software and procedures, and thriving on helping a company save time and money. Data engineering may be the right career for you if you're a tinkerer always looking for ways to improve the things you create, find fulfillment in creating practical tools that help people do their jobs, and enjoy experimenting with the newest tools and technologies.

 

Analytical thinkers who are curious, open to inquiries, and enthusiastic about testing their hypotheses are data scientists. Data scientists use data to understand not just what has happened in the past but also to look for trends and try to understand what might happen in the future. A career as a data scientist may be for you if you like performing complex statistical analysis, creating machine learning algorithms, and using your creativity to find solutions.

 

Conclusion

 

Building a solid career requires a thorough understanding of the company area one wants to focus on while starting out or even moving from another profile. An innate interest in and passion for the field could make the job route even more exciting. The data scientist and data engineer occupations are tremendously rewarding, but to succeed in both, one must have a strong technical background.


 


That the contents of third-party articles/blogs published here on the website, and the interpretation of all information in the article/blogs such as data, maps, numbers, opinions etc. displayed in the article/blogs and views or the opinions expressed within the content are solely of the author's; and do not reflect the opinions and beliefs of NASSCOM or its affiliates in any manner. NASSCOM does not take any liability w.r.t. content in any manner and will not be liable in any manner whatsoever for any kind of liability arising out of any act, error or omission. The contents of third-party article/blogs published, are provided solely as convenience; and the presence of these articles/blogs should not, under any circumstances, be considered as an endorsement of the contents by NASSCOM in any manner; and if you chose to access these articles/blogs , you do so at your own risk.


© Copyright nasscom. All Rights Reserved.