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Millions of people across the around the world are wondering, what is the difference between data scientist and data engineer. These are exciting new fields that seemed like prosperous avenues for college students and older individuals who are looking for a career change. Many of these newcomers often do not know the specific difference between the two fields. They are seen as almost interchangeable and are usually referred to in the same breath. But the fields are in fact quite different. They require a different skill set in order to be successful and find a long-term career path.
What is Data Engineering?
Data engineering is the study of how technology and individuals can manipulate and make sense of data. It often involves analyzing data and studying it to spot trends and patterns. Engineering involves putting together the mechanisms and systems that help analyze data. Data engineering is often the first step in the process of analyzing data to benefit companies or other organizations. Engineers develop and then produce architecture that packages data, removes outliers, and allows data to be further manipulated down the line.
What is Data Science?
Data science is the more theoretical understanding of how data works. This field involves studying how patterns form in data and the relationship that different pieces of data have to one another. The field of data science is often understood through statistical analysis. Individuals put large amounts of data together and use mathematical formulas to figure out how they fit together.
The concept of machine learning is one that has increasingly taken over the data science field. In machine learning, data is fed to a computer that either thinks on its own or attempts to use information to replicate an initial data set. A data scientist will often be in charge of the artificial learning process. He or she will manipulate the working of the artificial intelligence platform to suit the company’s intended outcome. An understanding of data science is essential for helping machines produce numbers at a rate that individuals cannot ever match.
Data Scientist and Data Engineer Backgrounds
Data scientists have to have a certain familiarity with coding and with famous coding languages such as Python and Java. These individuals deal with computers and numbers on a daily basis. They must know coding if they want to make any basic changes in the information that they use so often.
Data Scientist Coding Ability
But their coding knowledge does not have to be as extensive as the coding knowledge of a data engineer. Instead, these individuals need to know about how neural networks, database analysis, and machine learning through artificial intelligence work. They need to know patterns in data and they need to have a sophisticated understanding of statistics. Statistical knowledge will help these individuals process data and make sense of the large amounts of data that a company uses. They can turn millions or tens of millions of data points into a clear projection that shows a manager the best way forward for a company.
Data Engineer Coding Ability
A data engineer will often have a background in different forms of software engineering. They will know how to put together networks and data architecture. These individuals will most likely also know something about user experiences and a user interface. They will need to know how individuals in their field interact with technology and with the data connected to technology. Data is essential for companies and data engineering is essential for those companies being able to use their data. As a result, data engineers must have an extensive background in computer science and software engineering.
They have to be fluent in the number of programming languages that are useful for their fields. Data engineers also have to know something about patterns and relationships in data and how data is accessed in databases by the employees and other individuals who need that data the most.
Job Profile Comparison of Data Engineer vs Data Scientist
Data scientists need a considerable amount of experience in analyzing data and working with machine learning in artificial intelligence. They need experience in the different networks and equations that comprise the field of data science. Many of these individuals will need experience or a higher degree in statistics or some other sort of math. They will also need experience in formulating new algorithms and other tools that help make sense of data.
Data Scientist Data manipulation
Many companies will need new tools in order to detect patterns and do the things that they want with their data. Data scientists cannot simply master the tools that have already been developed for data. They need to be able to manipulate those tools and apply them to every possible circumstance that a company may need in order to help their workflow process and their general productivity.
A job profile for a data engineer will focus a considerable amount on the ability to write code and come up with different architectures for making sense of data. An individual may be asked about their skills with data and their ability to manipulate and understand databases.
Data Automation Engineer
One of the important tasks that a data engineer has to complete on a regular basis is automating processes and procedures that were originally done manually. A data engineer has to be able to communicate with employees to see how they manually input, search for, or use data. By knowing what is already happening in great detail, a data engineer can then know how to craft a new product that will automate the process while also meeting the needs of employees.
Data engineers will also be required to have communication skills. A data engineer is often in close contact with individuals who are collecting the data that is being analyzed and understood. Earlier roles that involve interaction with the public may be helpful for these positions.
Data Scientist and Data Engineer Job Responsibilities
Data engineers are responsible for formulating and working on new pieces of data engineering software. They often have to troubleshoot problems with data systems and figure out new solutions to the company’s problems. A data engineer may also be at the first line of analysis for data that is coming into a company. Data engineers may be responsible for maintaining databases and fixing any data access or management problems that arise in a company. Data scientists, on the other hand, would be responsible for artificial intelligence systems and machine learning programs. They would be expected to formulate new programs in accordance with whatever a company may need.
Technical Skills of a Data Scientist and Data Engineer
Individuals mostly will not be working with purely abstract data outside of the academy. They will have to know about how data helps lumber companies, craft breweries, or government agencies streamline their procedures and save money. Being able to learn about these different fields quickly and in a comprehensive manner can help an individual considerably when they are attempting to secure their next position as a data scientist.
Data Scientist Machine Learning Skills
The data scientist has to know primarily about algorithms and machine learning. He or she has to be familiar with neural networks and how those neural networks improve machine performance over time. Individuals have to be familiar with all of the techniques that companies in a particular area use to manipulate and make sense of their data. One helpful skill for any data scientist to have is the ability to learn about a particular field in a short period of time.
Big Data Software Engineer Skills
The most prominent tools of a data engineer are networks and software code. Data engineers have to often manipulate code in order to develop the complex information pipelines and user interfaces that are required for their jobs. A data engineer will also need to know about all of the machines that a company uses.
Data engineers should know the specific functions of fax machines, smartphones, and any other digital device that a company may use to interact with data. A data engineer working for a company that digitizes thousands of documents per day needs to be familiar with the digitization process and the tools and techniques used for that process.
The Final Say Between a Data Scientist and Data Engineer
Anyone interested in a job in data engineering or data science needs to start by studying and perhaps getting a degree in either computer science or statistics. Individuals need a general background in data and statistics before even considering one of these fields. Then, individuals need to decide if they want to deal with architecture and raw data or more with artificial intelligence and abstract concepts. Choosing between these areas is a first step in deciding whether or not a job in data engineering or data science would be right for a particular individual.