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Careers in Data Science: Data Analyst vs Data Engineer vs Data Scientist

With proper interpretation and use of data, organizations can minimize costs, increase efficiency, identify new business opportunities and gain a competitive market advantage. Thus, the need for experts in data science is on the increase while the supply for talent is low. Data science is a fast-growing field that offers some of the most lucrative careers in technology.

Those who want to venture into data science should know the career paths are available in the field, and what distinguishes them from each other to make a wise choice. So, here is a comparison of the top careers in data science: data analyst, data engineer and data scientist.

Data Analyst

A data analyst gathers, organizes and interprets statistical data using data analysis tools to come up with meaningful results. The client then uses these interpretations to make important business decisions.

A data analyst doesn’t require the high-level data interpretation expertise of data scientists or the software engineering abilities of data engineers. It is an entry-level career – which means that one does not need to be an expert. Graduates who have bachelor degrees in mathematics, statistics, economics or any other field related to math can pursue it.

Also, data analysts are usually generalists, which means that they can fit in different teams or roles to help make data-driven decisions.

Data Engineer

Big data engineering was ranked high among emerging jobs on LinkedIn. A data engineer is a professional who prepares and manages big data that is then analyzed by data analysts and scientists. They are responsible for designing, building, integrating, and maintaining data from several sources.

Data engineers play no part in the analysis of the data that they receive and store. Their job is to make sure it is available to the users – who are the data analysts and data scientists. If a company wants to use data to better their business, a data engineer is the first to come in to build data pipelines. They also improve on these pipelines regularly to make sure the data stored for analysis is accurate and accessible. Data engineers need advanced software development skills,  which are not as essential for data analysts and data scientists.

Data scientists

Data scientist was named the most promising job of 2019 in the U.S. The work of a data scientist is to analyze and interpret raw data into business solutions using machine learning and algorithms.

A data scientist performs the same duties as a data analyst, but possess more advanced algorithms and statistics expertise. Additionally, they know how to build, train, and use machine learning and deep learning models to understand data – skills that data analysts don’t possess. These skills make data scientists immensely valuable in interpreting answers from open-ended questions and also identifying hidden insights.

Data scientists can be engineers who have strong business acumen and communication skills. However, they are not usually in charge of developing or maintaining data architecture. Most data scientist jobs ask for a master’s degree in data science or a related field.

Conclusion

Knowing the differences among these three fields makes it easier for engineering students and IT professionals who are interested in data science to assess themselves and decide on which path fits them best. Jobs in data science are growing every year – and paying some of the highest salaries – as both the public and private sector continue to implement the use of big data.