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Scalable Data Analytics

DIL investigates and applies quantitative methods for gaining new insights from large-scale, connected datasets.

Core competences include statistical modelling, network analytics, machine learning, including Deep Neural Networks, as well as the implementation of scalable data engineering and analytics techniques on-top-of horizontally scalable architectures. DIL follows an open source strategy and has strong expertise in applying tools and frameworks such as R, SciKit Learn, Tensorflow, Keras, Apache Spark, Apache Cassandra and Apache Hadoop.

 

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