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Discusses a distributed programming paradigm, high level APIs, and scalable analytics platforms that simplify implementing algorithms for analyzing large genomic datasets. Discusses tools built on Apache Spark, enabling students to scale to thousands of cores, achieving a balance necessary for processing genomics data. Discusses how to solve some of these problems by bridging bioinformatics, data science, machine learning and the big data ecosystem. Enables students to leverage statistical methods of bioinformaticians and computational biologists in combination with best practices used by data engineers and data scientists across industry.