Nov 21, 2019  
USC Catalogue 2019-2020 
    
USC Catalogue 2019-2020

Applied Data Science (MS)


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The USC Viterbi 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, such as Hadoop and Spark. They will learn how to use the various analytical tools, including machine learning, data mining, and data visualization. And students will 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 homeworks 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.

For Admission Requirements, refer to Viterbi Graduate Degrees and Requirements.

A total of 32 units is required for the degree.

Notes:


Students with limited or no training in computer science should take INF 549 , INF 510 , and INF 550  before taking any other classes.

INF 550  should be taken prior to or in parallel with INF 551  and prior to taking INF 552  and INF 553 .

Directed Research (CSCI 590 ), Thesis courses (CSCI 594a , CSCI 594b , CSCI 594z ), and Internship courses (ENGR 595a , ENGR 595b , ENGR 595z , ENGR 596 ) are not eligible for elective credit.

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