38-611 Introduction to Large-Scale Computing in Science

6 units, Carnegie Mellon University

This course builds upon the skills acquired in the proceeding minisemester, Modern Programming for Data Scientists, and introduces students to the techniques necessary for manipulating and analyzing big data encountered in modern scientific computing, and using the large computational platforms involved in those processes. The course will use the Pythonfriendly Spark framework to massively amplify capabilities introduced in the previous mini. Starting with mining of large and complex scientific datasets, the students will progress to scalable data analytics and eventually very basic machine learning on various large scalable platforms such as supercomputers and clouds. Lowerlevel concepts such as performance optimization and concurrent programming techniques will also be introduced along the way. Exercises will be motivated by relevant scientific community datasets. This course is required for students enrolled in the MS program in Data Analytics for Science.

Prerequisites: none listed

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