According to the U.S. Bureau of Labor Statistics, jobs in the field of data science will grow by 22% between 2020 and 2030, far higher than the growth rate for other occupations. It appears that big data demand is not slowing down as companies continue to generate, collect, and analyze big data to support their operations. A data science professional, also known as a data scientist, and data engineers are two of the most prominent professions within data science, so this guide provides insight into their main differences.
Data Engineer vs. Data Scientist
· Role and Responsibilities
To understand the difference between data engineers and data scientists, you need to consider their roles in a complementary manner. The systems built and optimized by a data engineer enable a data science professional to perform the tasks. A certified data scientist, on the other hand, finds meaning in the vast amounts of data that data engineers maintain. The contribution of data scientists to medicine and software will surpass that of all biological sciences combined from around 2023. In light of the statements, it is clear that data proliferation is not going to stop, and as a result, data-related technologies like data science and big data are becoming increasingly popular.
· What Does a Data Engineer Do?
Data engineers prepare the infrastructure for analysis by preparing the infrastructure for data collection and analysis. Data formats, resilience, scaling, storage, and security are all factors they consider when determining the readiness of raw data for production. Data engineers are tasked with testing, designing, integrating, managing, building, and optimizing data from various sources. Additionally, they create the frameworks and architectures needed for data generation. Additionally, they create the frameworks and architectures needed for data generation.
The main goal of their work is to create free-flowing data pipelines that support real-time analytics by fusing a variety of big data technologies. To make sure that data is readily available, data engineers also create difficult queries.
· What Does a Data Scientist Do?
The job of a data scientist is to concentrate on extracting novel insights from the data procured from the data engineers. They conduct experiments online, do the formulation of hypotheses, and also utilize their genius in data analytics, statistics, machine learning algorithms, and data visualization for identifying trends and producing forecasts for the company.
In order to understand their particular requirements and communicate complex findings in a way that is understandable to a large business audience, they also interact with business executives. Additionally, they interact with business executives to learn about their particular needs and then present complex findings in a way that is understandable to a broad business audience.
· Education and Requirements
The majority of data scientists and engineers have a bachelor’s degree in computer science or a closely related subject like economics, statistics, mathematics, or IT. A job in data science or data engineering can be obtained without a degree, even though employers frequently prefer applicants with advanced degrees. And while employers often look for candidates with advanced degrees, it is possible to land a role in data science or data engineering without a degree. Additionally, although employers frequently favor applicants with advanced degrees, it is still possible to work in data science or data engineering without one.
· What Does a Data Engineer Earn?
The salaries of data engineers vary depending on factors like the position’s nature, their level of experience, and the location of their workplace. According to Glassdoor, the average salary for a data engineer is about $112,493 per year. And Indeed suggests, a person with more than 10 years of experience in data engineering makes about $148,400 annually. As per Salary.com an entry-level data engineer makes $102,341. Some of the cities in the US pay higher than the average such as the base pay in San Jose, CA is $152,489 per year, San Francisco, CA is $151,882 per year, New York, NY is 146,171 per year, etc.
· What Does a Data Scientist Earn?
Once more, the type of job, a data scientist’s skills, qualifications, and geographic location all affect how much money they make. A certified data scientist‘s annual salary is typically around $139,000, according to Glassdoor. The median salary for data science professionals has witnessed significant growth in 2022. This expansion is largely driven by the rising demand for data science skills across a variety of industries, which can help these industries realize the full potential of the data at their disposal. We can easily draw the conclusion that data scientists will be more valuable in 2023 and that their demand and pay will increase.
· Importance of Data Science Certifications in Data Science
You can become certified in “in demand” big data technologies by acquiring the best data science certifications. A precursor to the rising demand for Big Data expertise and technology is data science training. It provides professionals with data management tools like Mahout, R, and machine learning. A better and more competitive career is made possible by having knowledge and expertise in the relevant skills. Once you have mastered the technologies associated with Big Data and Data Science, you can apply for the highest-paying data science positions.
· Why is data engineering important for data science?
Data science skills enable companies to efficiently understand and derive insights from massive amounts of data from multiple sources and use these insights to make smarter data-driven decisions. Data science is widely used in various industry domains, including finance, healthcare, banking, policy work, etc.
Engineering is significant because it enables businesses to optimize data for usability. The following initiatives, for instance, heavily rely on data engineering: Discover the top techniques for streamlining your software development process.
Conclusion
Data engineering may be the right career choice for you if you’re a tinkerer who is constantly looking for ways to improve the things you build, finds fulfillment in creating supportive tools that assist others in doing their jobs, and enjoys experimenting with the newest tools and technologies.
A career as a data scientist may be ideal for you if you enjoy performing complex statistical analyses, developing machine learning algorithms, and applying creativity to problem-solving. The impact of data scientists on the development of technologies in the software and biological development worlds will be significant now and especially from around 2023.