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, including 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 that have a strong math and science background, but may not have much training in computer science. Students will 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 are given a choice of electives to allow them to pursue their own interests within data science.

A total of 32 units is required for the degree.

Core Courses

INF 510 Principles of Programming for Informatics Units: 4 *
INF 549 Introduction to Computational Thinking and Data Science Units: 4
INF 550 Overview of Data Informatics in Large Data Environments Units: 4
INF 551 Foundations of Data Management Units: 4
INF 552 Machine Learning for Data Informatics Units: 4
INF 553 Foundations and Applications of Data Mining Units: 4

One of the following user interface/visualization classes:

INF 554 Information Visualization Units: 4
INF 555 User Interface Design, Implementation, and Testing Units: 4
INF 556 User Experience Design and Strategy Units: 4

Electives **

(4 units)

CSCI 544 Applied Natural Language Processing Units: 4
CSCI 550 Advanced Data Stores Units: 4
CSCI 570 Analysis of Algorithms Units: 4
CSCI 572 Information Retrieval and Web Search Engines Units: 4
INF 529 Security and Privacy in Informatics Units: 4
INF 558 Building Knowledge Graphs Units: 4
INF 560 Data Informatics Professional Practicum Units: 4
INF 599 Special Topics Units: 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8

Notes:

*Students with previous programming experience in Python can petition to replace this class with a listed elective.

**Whichever courses (INF 554 , INF 555 , INF 556 ) are not taken as a required course may be taken as an elective.

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.