Opportunities in data science have shown increasing demand in recent years, with the rise in employment growth from 2020 to 2030—much more than the typical growth of other professions. As organizations continue to concentrate on creating, gathering, and evaluating big data to help them operate their operations, this demand shows no signs of abating.
The following article gives you an understanding of the key differences between two of the more influential careers within data science — data scientist and data engineer — and includes all you need to know to conclude the career option that’s preferable for you, from responsibilities and roles to average earnings, educational qualifications, and the various pathways that can ultimately usher to a dream career working with data.
What Does a Data Engineer Do?
A data engineer is a data specialist who prepares the data infrastructure for analysis. They are concerned with raw data production readiness and components such as formats, robustness, scalability, data storage, and security. Data engineers plan, create, test, link, maintain and improve data from different sources. They also provide the infrastructure and systems that allow data creation.
Their major goal is to create free-flowing data pipelines by merging several big data technologies that allow real-time analytics. Data engineers create complex queries to make that data is accessible.
What Does a Data Scientist Do?
Data scientists focus on extracting new information from the data that data engineers have gathered for them. As part of their profession, they execute online studies, develop theories, and utilize their understanding of statistics, data visualization, data analytics, and ML algorithms to find patterns and predict for the company.
They also work with corporate executives to understand their requirements and communicate results that a business audience can grasp, both verbally and graphically.
Qualifications and Requirements
Data engineers and data scientists have a bachelor’s degree in computer science or a related discipline such as statistics, mathematics, economics, or information technology. While businesses often seek applicants with advanced degrees, it is also possible to make a career in data science or data engineering with an online data science certification course.
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What Are the Requirements for Becoming a Data Engineer?
Data engineers often come from a software engineering background and are fluent in programming languages such as Python, Java, SQL, and Scala. Alternatively, they have a degree in math or statistics, which allows them to use various analytical methodologies to tackle commercial challenges.
Most firms want applicants with a bachelor’s degree in computer science, applied math, or information technology to work as data engineers. Candidates may also be needed to hold a few data engineering certifications. It also helps if they have expertise in making massive data warehouses that can execute gather, change and load on top of data sets.
What Are the Requirements for Becoming a Data Scientist?
Data scientists are often faced with vast amounts of data with no specific business challenges to tackle. In this case, the data scientist will be asked to investigate the data, develop appropriate questions, and deliver their results. As a result, data scientists must be well-versed in the methodologies in big data infrastructures, machine learning algorithms, data mining, and statistics. They must also be up to speed with all the current technologies as they deal with data sets that come in different formats to run their algorithms successfully and efficiently.
Data scientists are required to be knowledgeable in programming languages such as Python, R, SQL, and Java, as well as technologies such as Hadoop, Hive, Cassandra, and MongoDB.
Salary Differences Between Data Scientists and Data Engineers
Both roles provide a very satisfying and profitable career for the analytical mind.
What Does a Data Engineer Earn?
Data engineers’ salaries are determined by factors such as the kind of work, appropriate experience, and location of the employment. According to Glassdoor, the average income is about $142,000 per year.
What Does a Data Scientist Earn?
The salary of a data scientist is determined by the work, talents, credentials, and location. A data scientist earns about $139,000 per year on average, according to Glassdoor.
Which Is Better for You, a Data Scientist or a Data Engineer?
Although the similarity in expertise between the two career options, data scientists and data engineers have different roles, and the jobs can be better suited to specific qualities types.
Data engineers are primarily concerned with the structure and design that stores and organizes data. They are strong developers who like learning and using new technologies, finding new methods to make software and processes more efficient, and thrive on assisting a business in saving time and money. If you’re a tinkerer who is constantly seeking ways to better the things you develop, find meaning in making helpful tools that assist people to perform their jobs, and like experimenting with the newest tools and technology, data engineering might be the appropriate profession for you.
Data scientists are analytical thinkers who are interested, aren’t hesitant to ask questions, and thrive on putting their assumptions to the test. Data scientists utilize data to not only make sense of what has occurred in the past but also to anticipate patterns and attempt to comprehend what could happen in the future. If you appreciate doing complex statistical analyses, designing ML algorithms, and utilizing your imagination to solve issues, a career as a data scientist might be for you.
Conclusion
Proper Understanding of the section of a firm one wishes to focus on while starting out or even migrating from another profile becomes essential to building a strong career. An innate interest and love in the area might make the career path even more interesting. Data scientist and data engineering professions are both very gratifying, but they require a solid theoretical foundation as well as hands-on experience to be effective.
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