Master of Science in Applied Data Science

All information contained here is summarized from the USC Catalogue and is considered non-official. For all rules, regulations, procedures, and outlines, please see the current academic year USC Catalogue. The USC Catalogue supersedes all other publications.

Current students follow degree requirements in effect for the academic year they began their studies at USC.  If you are a current student, please refer to your STARS report or the appropriate USC Catalogue for your year.  Students seeking to advance their catalogue year to follow updated curricula may contact their department advisor.

Program Director: Fred Morstatter, PhD

The Master of Science in Applied Data Science will train students as data scientists. This degree provides students with the knowledge and skill to solve real-world world challenges that require a combination of data management and data analytics skills. Students will learn how to use the latest big-data infrastructures, including Hadoop and Spark. They will learn how to use various analytical tools, including machine learning, data mining and data visualization. Students will also learn how to apply these tools to real-world problems.

This degree is designed for students with a range of backgrounds, but students are expected to have at least a strong math and science background to pursue this degree. Students that do not have much training in computer science will first learn the basics of data science, including data formats, tools and techniques. They learn how to build data processing programs in Python, and they will learn how to apply the latest analytical tools through hands-on homework and projects. Students with a computer science background will be able to jump directly into the more advanced data science courses including data management, machine learning, data mining, and statistics for data science. Once students have completed the introductory and core courses, they are given a choice of electives to allow them to pursue their own interests within data science.

Published on July 16th, 2018

Last updated on July 20th, 2023