DescriptionData Science and Big Data Analytics will give students a foundation level understanding of big data and the state of the practice of analytics. This course provides an introduction to big data and a Data Analytics Lifecycle to address business challenges that leverage big data. It provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools, including MapReduce and Hadoop. This course has two sessions that contain hands-on and/or demonstrated introductory-level labs on this topic. Audience: This offering is limited to only graduate students and faculty members of KAUST. Here are recommended readings for this course: 1) Machine Learning, by Tom M.Mitchell (McGraw-Hill ) 2) The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. (Princeton). The online version can be downloaded at: :http://www-stat.stanford.edu/~tibs/ElemStatLearn/
Lead Data Architect at KAUST, with 25+ years of experience in architecting, designing, and developing mission critical applications and systems infrastructure for US Fortune 500 companies, as well as major private and public sector entitities in Latin America, Europe, and Middle East. An industry-certified Enterprise Architect, Latif is interested in exploiting the advances in mobile platform development in the system building blocks of KAUST enterprise architecture.
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