{"id":213,"date":"2024-06-19T16:12:46","date_gmt":"2024-06-19T23:12:46","guid":{"rendered":"https:\/\/dornsife.usc.edu\/math-data-science\/?page_id=213"},"modified":"2026-01-03T22:34:02","modified_gmt":"2026-01-04T06:34:02","slug":"curriculum","status":"publish","type":"page","link":"https:\/\/dornsife.usc.edu\/math-data-science\/curriculum\/","title":{"rendered":"Curriculum"},"content":{"rendered":"\n\n\n\n \n \n\n\n\n\n\n\n\n
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Degree Requirements<\/strong><\/h2>\n

Students are required to complete a minimum\u00a0 of thirty-two (32) units of course work. For course descriptions, please use the \u201cCourses of Instruction\u201d<\/a> search tool on the Catalogue.<\/p>\n

At least 32 units are required, including:<\/p>\n

 <\/p>\n

Core Curriculum (5 courses, 19 units)<\/strong><\/h2>\n\n\n\n\n\n\n\n\n
COURSE<\/strong><\/td>\nCOURSE TITLE<\/strong><\/td>\nUNITS<\/strong><\/td>\n<\/tr>\n
MATH 447<\/td>\nMathematics of Machine Learning<\/td>\n4<\/td>\n<\/tr>\n
MATH 546<\/td>\nMathematical Statistics for Data Science<\/td>\n4<\/td>\n<\/tr>\n
MATH 549<\/td>\nFoundations of Mathematical Data Science<\/td>\n4<\/td>\n<\/tr>\n
MATH 550<\/td>\nStatistical Consulting and Data Analysis<\/td>\n3<\/td>\n<\/tr>\n
PHYS 515<\/td>\nPython for Data Science and Scientific Computing<\/td>\n4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Electives (12 units, selected from the following list)<\/strong><\/h2>\n

NOTE:<\/strong> Some of these courses have prerequisites. You are expected to have learned the prerequisite’s topic from your previous undergraduate institution or from self-studying in order to waive the prerequisite. If you prefer to take the prerequisite at USC, units for prerequisite courses do not count towards the degree.<\/p>\n

Although all of the courses listed below are approved electives for this degree, we are not able to give departmental authorization or permission for you to register in courses outside of the Mathematics department. Students will need to request D-clearance and prerequisite waivers from the department who offers the course. If you are not sure how to contact the department, email the MDS academic advisor, Dr. Patil (upatil@usc.edu).<\/p>\n

COMPUTER SCIENCE (CSCI)<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n
COURSE<\/strong><\/td>\nCOURSE TITLE<\/strong><\/td>\nUNITS<\/strong><\/td>\n<\/tr>\n
CSCI 551<\/span><\/td>\nComputer Networking<\/span><\/td>\n4<\/td>\n<\/tr>\n
CSCI 544<\/span><\/td>\nApplied Natural Language Processing<\/span><\/td>\n4<\/td>\n<\/tr>\n
CSCI 566<\/span><\/td>\nDeep Learning and Its Applications<\/span><\/td>\n4<\/td>\n<\/tr>\n
CSCI 567<\/span><\/td>\nMachine Learning<\/span><\/td>\n4<\/td>\n<\/tr>\n
CSCI 570<\/td>\nAnalysis of Algorithms<\/td>\n4<\/td>\n<\/tr>\n
CSCI 585<\/td>\nDatabase Systems<\/td>\n4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

DATA SCIENCE (DSCI)<\/strong><\/p>\n\n\n\n\n
COURSE<\/strong><\/td>\nCOURSE TITLE<\/strong><\/td>\nUNITS<\/strong><\/td>\n<\/tr>\n
DSCI 560<\/td>\nData Science Professional Practicum<\/td>\n4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

DATA SCIENCE AND OPERTIONS (DSO)<\/strong><\/p>\n\n\n\n\n\n\n
COURSE<\/strong><\/td>\nCOURSE TITLE<\/strong><\/td>\nUNITS<\/strong><\/td>\n<\/tr>\n
DSO 528<\/td>\nBlended Data Business Analytics for Efficient Decisions<\/td>\n3<\/td>\n<\/tr>\n
DSO 545<\/td>\nStatistical Computing and Data Visualization<\/td>\n3<\/td>\n<\/tr>\n
DSO 530<\/td>\nApplied Modern Statistical Learning Methods<\/td>\n3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

ELECTRICAL AND COMPUTER ENGINEERING (EE)<\/strong><\/p>\n\n\n\n\n\n\n
COURSE<\/strong><\/td>\nCOURSE TITLE<\/strong><\/td>\nUNITS<\/strong><\/td>\n<\/tr>\n
EE 546<\/td>\nMathematics of High Dimensional Data<\/td>\n4<\/td>\n<\/tr>\n
EE 553<\/td>\nComputational Solution of Optimization problems<\/td>\n3<\/td>\n<\/tr>\n
EE 660<\/td>\nMachine Learning II: Mathematical Foundations and Methods<\/td>\n4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

INDUSTRIAL SYSTEMS ENGINEERING (ISE)<\/strong><\/p>\n\n\n\n\n\n
COURSE<\/strong><\/td>\nCOURSE TITLE<\/strong><\/td>\nUNITS<\/strong><\/td>\n<\/tr>\n
ISE 520<\/td>\nOptimization: Theory and Algorithms<\/td>\n3<\/td>\n<\/tr>\n
ISE 530<\/td>\nOptimization Methods for Analytics<\/td>\n4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

MATERIALS SCIENCE (MASC)<\/strong><\/p>\n\n\n\n\n
COURSE<\/strong><\/td>\nCOURSE TITLE<\/strong><\/td>\nUNITS<\/strong><\/td>\n<\/tr>\n
MASC 520<\/td>\nMathematical Methods for Deep Learning<\/span><\/td>\n4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

MATHEMATICS (MATH)<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n
COURSE<\/strong><\/td>\nCOURSE TITLE<\/strong><\/td>\nUNITS<\/strong><\/td>\n<\/tr>\n
MATH 446<\/td>\nData Science with Python<\/td>\n4<\/td>\n<\/tr>\n
MATH 505a<\/td>\nApplied Probability<\/td>\n3<\/td>\n<\/tr>\n
MATH 542<\/td>\nAnalysis of Variance and Design<\/td>\n3<\/td>\n<\/tr>\n
MATH 545<\/td>\nIntroduction to Time Series<\/td>\n3<\/td>\n<\/tr>\n
MATH 547<\/td>\nMathematical Foundations of Statistical Learning Theory<\/td>\n3<\/td>\n<\/tr>\n
MATH 548<\/td>\nMachine Learning in Quantitative Finance<\/td>\n3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

QUANTITATIVE AND COMPUTATIONAL BIOLOGY (QBIO)<\/strong><\/p>\n\n\n\n\n
COURSE<\/strong><\/td>\nCOURSE TITLE<\/strong><\/td>\nUNITS<\/strong><\/td>\n<\/tr>\n
QBIO 578a<\/td>\nComputational Molecular Biology<\/td>\n3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

SPATIAL SCIENCES (SSCI)<\/strong><\/p>\n\n\n\n\n
COURSE<\/strong><\/td>\nCOURSE TITLE<\/strong><\/td>\nUNITS<\/strong><\/td>\n<\/tr>\n
SSCI 581<\/td>\nConcepts for Spatial Thinking<\/td>\n4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Courses that do NOT satisfy the elective requirement:<\/strong><\/p>\n

CSCI 512 Testing and Analysis of Software Systems
\nCSCI 548 Machine Learning
\nCSCI 555L Advanced Operating Systems
\nMATH 541a\/b Introduction to Mathematical Statistics<\/span><\/p>\n

For all other courses that are not included in the lists above, please contact the Academic Advisor, Dr. Patil (upatil@usc.edu), to request the course to be reviewed.<\/p>\n

Additional Requirements<\/strong><\/h2>\n

To complete the program, students enroll in at least 1 unit of MATH 590 Directed Research and submit a summative report at the end of the term. Note: enrollment in MATH 590 will often be concurrent with enrollment in MATH 550. Students should consult with their advisor for details.<\/p>\n\n\n\n<\/div>\n\n\n <\/div><\/div>\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n

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\n Sample Course Plans\n <\/h2>\n\n\n<\/div>\n \n \n
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The Mathematical Data Science M.S. program is designed to be completed in 3-4 semesters, or 1.5-2 years.<\/p>\n

International students must enroll in a minimum of 8 units to meet the full-time enrollment required to maintain their visa status. International students can register below 8 units if they meet any of the conditions outlined in the Reduced Course Load website<\/a> from the Office of International Services.<\/p>\n

Domestic students can choose to enroll full-time or part-time, but are recommended to mindful of any enrollment requirements if they are receiving financial aid.<\/p>\n

Core courses offered *ONLY* in Fall semester: MATH 546, MATH 549, MATH550
\nCore courses\u00a0offered *ONLY* in Spring semester: PHYS 515
\nCore courses\u00a0offered in *BOTH* Fall & Spring semesters: MATH 447<\/div>\n\n\n\n<\/div>\n \n \n <\/div>\n \n
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