The difference in the career options in Data Science: Data Scientist vs Data Engineer vs Data Analyst vs ML Engineer
Many of us wonder what is the difference among data scientists, data analysts, ML engineers, and data engineers? What is the differentiating factor that helps them to analyze the data from a different point of view? The answer is their JOB ROLES!
Data is gold nowadays. It’s important for any kind of decision making. Today’s world runs completely on data and none of today’s organizations would survive without data-driven decision making and strategic plans. In this article, we will discuss the key differences and similarities between a data analyst, data engineer, ML engineer and data scientist.
Who is a Data Analyst?
Data analytics is the process of the extraction of information from a given set of data. A data analyst is a person who analyses the data. A data analyst extracts the information by several methods like data cleaning, data conversion, and data modeling. Industries can analyze trends in the market, requirements of their clients and overview their performances with data analysis. This allows them to make careful data-driven decisions.
The two most important techniques used in data analytics are descriptive or summary statistics and inferential statistics. A Data Analyst is also well versed with several visualization techniques and tools. Moreover, the data analyst must have presentation skills. This allows them to communicate the results with the team and help them to reach proper solutions.
Data Analytics allows the industries to process fast queries to produce actionable results that are needed in a short duration of time. This restricts data analytics to a short term growth of the industry where quick action is required. Two of the popular and common tools used by the data analysts are SQL and Microsoft Excel.
Who is a Data Engineer?
A Data Engineer is a person who prepares the data for analytical usage. Data Engineering also involves the development of platforms and architectures for data processing. In other words, a data engineer develops the foundation for various data operations. A Data Engineer is responsible for designing the format for data scientists and analysts to work on.
Data Engineers have to work with both structured and unstructured data. Therefore, they need experience both in SQL and NoSQL databases. Data Engineers allow data scientists to carry out their data operations. Data Engineers have to deal with Big Data where they engage in numerous operations like data cleaning, management, transformation, data deduplication, etc.
A Data Engineer is more experienced with core programming concepts and algorithms. The role of a data engineer is similar to that of a software engineer. This is because a data engineer is involved in developing platforms and architecture that utilize guidelines for software development. For example, developing a cloud infrastructure to facilitate real-time analysis of data requires various development principles. Therefore, building an interface API is one of the job responsibilities of a data engineer.
Furthermore, a data engineer has a good knowledge of engineering and testing tools.
Who is a Data Scientist?
Data Science is the most trending job in the technology sector. Almost everyone talks about Data Science and companies are having a sudden requirement for a greater number of data scientists. Every company is looking for data scientists to increase their performance and optimize their production.
There is a massive explosion in data. This explosion is contributed by the advancements in computational technologies like High-Performance Computing. This has given industries a massive opportunity to unearth meaningful information from the data.
Companies extract data to analyze and gain knowledge about various trends and practices. To do so, they employ specialized data scientists who possess knowledge of statistical tools and programming skills. Moreover, a data scientist possesses knowledge of machine learning algorithms. These algorithms are responsible for predicting future events. Therefore, data science can be thought of as an ocean that includes all the data operations like data extraction, data processing, data analysis and data prediction to gain necessary insights.
However, Data Science is not a singular field. It is a quantitative field that shares its background with math, statistics and computer programming. However, due to a high learning curve, there is a shortage in supply for data scientists. This has resulted in a massive income bubble that provides data scientists with lucrative salaries.
Who is a Machine Learning Engineer?
Machine learning engineers sit at the intersection of software engineering and data science. They leverage big data tools and programming frameworks to ensure that the raw data gathered from data pipelines are redefined as data science models that are ready to scale as needed.
Machine learning engineers feed data into models defined by data scientists. They’re also responsible for taking theoretical data science models and helping scale them out to production-level models that can handle terabytes of real-time data.
Machine learning engineers also build programs that control computers and robots. The algorithms developed by machine learning engineers enable a machine to identify patterns in its programming data and teach itself to understand commands and even think for itself.
Conclusion
Knowing the differences between these four 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 sectors continue to implement the use of big data.
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