Curriculum
Reflecting the multi-disciplinary nature of data analytics, this program consists of a core combination of 12 credits from across three USU Departments: Mathematics and Statistics, Data Analytics & Information Systems, and Economics and Finance.
Students select one of three available specialization options:
- Management Information Systems
- Economics and Finance
- Statistics
Your specialization choice determines your advising home and academic department.
The capstone course is an applied project in collaboration with faculty and other students, involving real-world data-driven questions provided by corporate partners. This provides a crucial conduit for qualified students to enter the workforce upon graduation by matching them with potential employers during their capstone experience.
MDATA Core Courses (12 Total Credits)
| Course Number | Course Name | Credits | Semester Offered |
|---|---|---|---|
| DATA 3330 |
Database Management |
3 | Fall, Spring |
| DATA 3500 | Introduction to Python Programming | 3 | Fall, Spring, Summer |
| STAT 5050 | Introduction to R | 1 | Fall, Spring |
| STAT 5650 | Statistical Learning and Data Mining I | 2 | Spring |
| DATA 6900; STAT 6950 |
MDATA Capstone | 3 | Spring |
Specialization Options
Select one of the following three specialization options to complete the degree requirements (Management Information Systems, Economics and Finance, Statistics).
Management Information Systems Specialization (18 Credits)
| Course Number | Course Name | Credits | Semester Offered |
|---|---|---|---|
| Complete the following: | (9 credits) | ||
| DATA 5600 | Introduction to Regression and Machine Learning for Analytics | 3 | Fall, Spring, Summer |
| DATA 6500 | Advanced Python Programming for Analytics | 3 | Fall, Spring, Summer |
| DATA 6610 | Advanced Machine Learning for Analytics | 3 | Fall, Spring |
| Choose 3 electives: | (9 credits) | ||
| DATA 4330 | Advanced Database and Database Analytics | 3 | Fall, Spring |
| DATA 5170/6170 | Tech Innovation | 3 | Fall |
| DATA 5180/6180 | Tech Commercialization | 3 | Spring |
| DATA 6330 | Data Pipeline Engineering | 3 | Fall, Spring |
| DATA 6360 | Data Warehousing | 3 | Fall, Spring |
| DATA 6400 | Visual Data Analytics | 3 | Fall, Spring |
| DATA 6420 | Introduction to Text Mining | 3 | Fall |
| DATA 6480 | Data Mining | 3 | Fall, Spring |
| DATA 6570 | Building SW with AI | 3 | Spring |
| DATA 5620/6620 | Causal Inference | 3 | Fall, Spring |
| DATA 6630 | Deep Forecasting | 3 | Spring |
| DATA 5690/6690 | Computational Methods for FinTech | 3 | Fall, Spring |
| DATA 5695/6695 | Predictive Methods for FinTech | 3 | Fall, Spring |
| DATA 5910/6910 | Data Competitions | 3 | Fall, Spring |
| IS 5100/6100 | Agile Tech Design and Development | 3 | Fall, Spring |
| IS 5170/6170 | Applied Tech Innovation | 3 | Fall, Spring |
| IS 6250 | Graduate Internship | 1, 2 or 3 | Fall, Spring, Summer |
| IS 6700 | Advanced Client-Side Web Application Development | 3 | Fall |
| IS 6750 | Advanced Server-Side Web Application Development | 3 | Spring |
| IS 6950 | Independent Readings | 1, 2 or 3 | Fall, Spring, Summer |
| MATH 5710 | Introduction to Probability | 3 | Fall, Spring, Summer |
| MATH 5720 | Introduction to Mathematical Statistics | 3 | Spring |
| STAT 6555 | Advanced R Programming for Data Science | 3 | Spring |
| STAT 6645 | Mathematical Methods for Data Science | 3 | Fall |
| STAT 6650 | Stat Learning: Multivariate Stat Analysis for Bioinformatics, Data Mining, and Machine Learning | 3 | No sections available in banner |
** 2-week course held on campus 2 weeks prior to fall semester
Statistics Specialization (18-19 Credits)
| Course Number | Course Name | Credits | Semester Offered |
|---|---|---|---|
| STAT 5080 | Data Technologies | 2 | Fall |
| STAT 5100 | Modern Regression Methods (CI/QI) | 3 | Fall, Spring |
| STAT 5550 | Statistical Visualization I | 2 | Fall |
| STAT 6555 | Advanced R Programming for Data Science | 3 | Spring |
| STAT 6560 | Statistical Visualization II | 2 | Spring |
| STAT 6655 | Machine Learning | 3 | Spring |
| Choose 3 or more credits from the following courses: | |||
| STAT 5410/6410 | Applied Spatial Statistics | 2 | Even Spring |
| STAT 5500/6500 | Biostatistics Methods | 3 | Fall |
| STAT 5570/6570 | Statistical Bioinformatics | 2 | |
| STAT 5685/6685 | Deep Learning Theory and Applications | 3 | Fall |
| STAT 6100 | Advanced Regression Analysis | 2 | Even Spring |
| STAT 6180 | Financial Time Series | 2 | Even Fall |
| STAT 6200 | Generalized Linear Mixed Model Analysis | 2 | Odd Fall |
| CS 5665 | Machine Learning for Data Science | 3 | |
| CS 5830 | Data Science in Practice | 3 | |
| CS 6665 | Data Mining | 3 | |
| CS 6675 | Advanced Data Mining | 3 | |