The Master of Science in Statistics—Connect program is designed for students from all backgrounds with a BS/BA degree, provided the student has experience with basic calculus and statistics. The first semester of the degree program provides students with the foundational knowledge needed to study successfully alongside direct-entry graduate students. The field of statistics plays a critical role in the support of nearly every industry including technology; business, management, and finance; healthcare and pharmaceuticals; and more. The MS in Statistics develops a comprehensive and flexible skill set that allows graduates to adapt to an ever-changing job market in various occupations and industries. In an era of increasing automation of Big Data, the value of the rigor of statistical thinking and analysis by individuals grows with the rise of automated Big Data analysis (e.g., artificial intelligence and machine learning). This program in statistics is designed to provide learners with a solid foundation in applied, modern, and computational approaches to statistical analysis and exposure to the statistical thinking skills necessary to critically assess data and answer business and research questions across domains. Core courses integrate theory and application, enabling students to be ready for the job on day one. Upon application, each student selects an industry concentration (biostatistics, statistical machine learning, and statistical theory and modeling) to examine statistical theories and applied methodologies most relevant to specific career pathways. In this degree program, students are admitted to the college associated with their concentration, and their degree is awarded by that college. The concentrations are associated with the following colleges:
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Biostatistics—Bouvé College of Health Sciences
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Statistical Machine Learning—Khoury College of Computer Sciences
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Statistical Theory and Modeling—College of Science
Students will follow all policies associated with their college. Each student finishes the program with experiential courses such as a thesis, capstone, or consulting project, where they gain hands-on, project-based experience addressing business problems and presenting and communicating the findings and recommendations.
Complete all courses and requirements listed below unless otherwise indicated.
Connect Courses
Course List | Code | Title | Hours |
| 8-10 |
| Intensive Foundations of Computer Science | |
| Accelerated Linear Algebra | |
| Accelerated Multivariable Calculus | |
| Accelerated Probability and Statistics | |
| Applied Linear Algebra and Matrix Analysis | |
Required Courses
Course List | Code | Title | Hours |
| MATH 5010 | Foundations of Statistical Theory and Probability | 4 |
| MATH 6241 | Stochastic Processes | 2 |
| MATH 6243 | Statistical Learning | 4 |
| PHTH 6800 | Causal Inference in Public Health Research | 3-4 |
| or PHTH 6801 | Causal Inference 1 |
| PHTH 6830 | Generalized Linear Models | 4 |
Concentrations
Complete one of the following concentrations:
Experiential Courses
Course List | Code | Title | Hours |
| 2 |
| Master's Project | |
| Statistical Consultancy | |
Program Credit/GPA Requirements
39-41 total semester hours required (40-42 for students who declare the Concentration in Statistical Theory and Modeling and also opt to participate in co-op)
Minimum 3.000 GPA required
Biostatistics Concentration—Bouvé College of Health Sciences
Course List | Code | Title | Hours |
| 12 |
| Using SAS in Public Health Research | |
| Advanced Methods in Biostatistics | |
| Causal Inference 2 | |
| Survival Analysis | |
| Design and Analysis of Clinical Trials | |
| HLTH 6964 | Co-op Work Experience | 0 |
Statistical Machine Learning Concentration—Khoury College of Computer Sciences
Course List | Code | Title | Hours |
| CS 5100 | Foundations of Artificial Intelligence | 4 |
| CS 6140 | Machine Learning | 4 |
| DS 5110 | Essentials of Data Science | 4 |
| CS 6964 | Co-op Work Experience | 0 |
Statistical Theory and Modeling Concentration—College of Science
Course List | Code | Title | Hours |
| 12 |
| Machine Learning and Statistical Learning Theory 2 | |
| Statistics for Bioinformatics | |
| Probability 2 | |
| Mathematical Statistics | |
| Applied Statistics | |
| Regression, ANOVA, and Design | |
| EESC 6500 | Pathways to Professional Success | 1 |
| EESC 6964 | Co-op Work Experience | 0 |
