What is the difference between a Data Scientist and a Data Engineer?
To build and train prediction models, data scientists use cleansed data. Managers and executives are then informed of their findings. A data science engineer is responsible for developing and maintaining the technologies that enable data scientists to access and analyze data. The job entails developing data models, constructing data pipelines, and monitoring ETL (extract, transform, load).
What do they provide?
Data Engineers, as previously stated, design, create, test, integrate, and optimize data collected from various sources. They design free-flowing data pipelines that use Big Data tools and technology to enable real-time analytics applications on complex data. Complex queries are also written by Data Engineers to improve data accessibility.
Data engineers provide infrastructure and tools to help data scientists and analysts in delivering end-to-end solutions to business problems. They build batch and real-time evolution strategies, as well as scalable, high-performance infrastructure for extracting reliable business information from the raw data sources.
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Data Scientists, on the other hand, are more concerned with finding answers to critical business concerns such as how to improve corporate operations, cut expenses, and improve customer experience.
They deal with data infrastructure designed and managed by data engineers on a regular basis, but they are not responsible for it. Instead, they are internal clients tasked with undertaking high-level market and business operation research in order to detect trends and tasks that require them to engage with and act on data using a range of sophisticated technologies and advancements.
Data Engineers and Data Scientists have quite diverse skill sets. Furthermore, their skill levels differ. A Data Scientist’s analytical skills, for example, will be far more advanced than those of a Data Engineer.
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What do they deal with?
Data engineers deal with advanced programming languages such as Python, Java, Scala, and others, as well as distributed systems, data pipeline technologies (IBM InfoSphere DataStage, Talend, Pentaho, Apache Kafka, and others), and Big Data frameworks such as Hive, Hadoop, and Spark.
Data scientists employ advanced analytics and BI technologies such as Tableau Public, Rapidminer, KNIME, QlikView, and Splunk, in addition to Python and Java. Apart from these technologies, Data Scientists rely significantly on machine learning libraries such as TensorFlow, PyTorch, Apache Spark, DLib, Caffe, and Keras, to mention a few.
Both Data Engineers and Data Scientists have a bright future ahead of them, with lucrative annual salaries. The top recruiters for these positions are Amazon, IBM, TCS, Infosys, Ernst & Young, Capgemini, Accenture, General Electric, Apple Inc., Microsoft, and Facebook.
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The average income for Data Engineers in India is INR 8,43,140 LPA, while it is US$ 92,260 in the United States, according to PayScale. A Data Scientist’s average pay in India is INR 8,13,593 LPA, whereas it is US$ 96,089.
Finally, we must recognize that the responsibilities of a Data Engineer and Data Scientist are complementary. Data Scientists rely on Data Engineers to create appropriate data creation and analysis pipelines. Similarly, without the analytical activities of Data Scientists, the data that Data Engineers create will be useless. Whether you want to become a data engineer or a data scientist you can use online courses to learn the skills you need. You can find all the information about the best and most relevant courses on data science by clicking here.