Applied Data Science (MS)

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datascience.usc.edu

Program Director: Fred Morstatter, PhD

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 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.

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

A total of 32 units is required for the degree.

Core Courses

DSCI 551 Foundations of Data Management Units: 4
DSCI 552 Machine Learning for Data Science Units: 4
DSCI 553 Foundations and Applications of Data Mining Units: 4

Electives*

Take five courses.

CSCI 544 Applied Natural Language Processing Units: 4
CSCI 550 Advanced Data Stores Units: 4
CSCI 551 Computer Networking Units: 4
CSCI 561 Foundations of Artificial Intelligence Units: 4
CSCI 566 Deep Learning and Its Applications Units: 4
CSCI 567 Machine Learning Units: 4
CSCI 570 Analysis of Algorithms Units: 4
CSCI 571 Web Technologies Units: 4
CSCI 572 Information Retrieval and Web Search Engines Units: 4
CSCI 585 Database Systems Units: 4
CSCI 587 Geospatial Information Management Units: 4
DSCI 510 Principles of Programming for Data Science Units: 4
DSCI 529 Security and Privacy Units: 4
DSCI 531 Fairness in Artificial Intelligence Units: 4
DSCI 549 Introduction to Computational Thinking and Data Science Units: 4
DSCI 550 Data Science at Scale Units: 4
DSCI 554 Data Visualization Units: 4
DSCI 555 Interaction Design and Usability Testing Units: 4
DSCI 556 User Experience Design and Strategy Units: 4
DSCI 558 Building Knowledge Graphs Units: 4
DSCI 560 Data Science Professional Practicum Units: 4
DSCI 564 Probability and Statistics for Data Science Units: 4
DSCI 599 Special Topics Units: 1, 2, 3, 4, 5, 6, 7, 8
(4 units of DSCI 599 required if chosen)

*Notes:

A maximum of two CSCI electives can be applied toward the degree.

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

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