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Whether it’s an insurance company leveraging location for better underwriting or risk assessment, a financial services organization enriching transactions for validation and accurate merchant assignment, or a telecommunications company optimizing 5G rollouts and creating new services, there’s one essential commonality: location data.
They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.
Though you may encounter the terms “datascience” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. And you should have experience working with big data platforms such as Hadoop or Apache Spark.
It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the datascience field include mining, statistics, data analytics, datamodeling, machine learning modeling and programming.
As models become more complex and the needs of the organization evolve and demand greater predictive abilities, you’ll also find that machine learning engineers use specialized tools such as Hadoop and Apache Spark for large-scale data processing and distributed computing.
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