STATDSMS - Statistics and Data Science
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30
Core MS Courses (18 units):
course / course: Advanced Statistical Regression Analysis (3)
course: Statistical Machine Learning (3)
*A minimum grade of B is required for this course. If the student does not have a minimum grade of B, they can take the theory portion of the qualifying exam and must pass with a minimum MS pass.
An MS Thesis or in lieu of a Thesis, advanced statistical coursework; minimum 3 units as follows:
course: Thesis (3) OR any one of:
course: High-Dimensional Health Data Analysis and Machine Learning (3)
course: General Linear and Mixed Effects Models (3)
course: Survival Analysis (3)
course: Statistical Machine Learning (3)
course: Bayesian Statistical Theory and Applications (same as course) (3)
course: Survey Sampling (3)
course: Time Series Analysis (3)
course: Statistical Computing (3)
Additional Elective Courses; minimum 9 units from any of the following:
course: Statistical Genetics for Quantitative Measures (3)
course: Biostatistics for Research (3)
course: Applied Biostatistics Analysis (3)
course: Data Management and the SAS Programming Language (3)
course: Analysis of Categorical Data (3) OR course / course: Categorical Data Analysis (3)
course: High Dimensional Health Data Analysis and Machine Learning (3)
course: Clinical Trials and Intervention Studies (3)
course: General Linear and Mixed Effects Models (3)
course: Special Topics in Biostatistics (3)
course: Survival Analysis (3)
course: Biostatistics Seminar (1)
course: Detection and Estimation in Engineering Systems (3)
course: Spatio-Temporal Ecology (3)
course: Introduction to Econometrics (3)
course: Econometrics (3) OR course: Advanced Applied Econometrics (4)
course: Econometrics (3)
course: Statistical Package for Research (3)
course: Educational Tests and Measurements (3) OR course: Statistical Methods in Psychological Research (3)
course: Multivariate Methods in Educational Research (3)
course: Theory of Measurement (3)
course: Theory of Measurement (3)
course: Advanced Data Analysis: Structural Equation Modeling (3)
course: Advanced Data Analysis: Dyadic Data and Bivariate Systems (3)
course: Advanced Data Analysis: Multilevel Modeling (3)
course / course / course: Spatial Statistics and Spatial Econometrics (3)
course: Applied Time Series Analysis (1-3)
course: Statistical Natural Language Processing (3)
course: Advanced Statistical Natural Language Processing (3)
course: Topics in Modern Analysis (3)
course: Theory of Graphs and Networks (3)
course: Stochastic Processes (3)
course: Stochastic Processes (3)
course: Stochastic Differential Equations (3)
course: Applied Stochastic Processes (3) OR course / course: Stochastic Methods in Surface Hydrology (3)
course: Topics in Applied Mathematics (3)
course: Bioinformatics and Functional Genomic Analysis (3)
course: Multivariate Analysis in Management (3)
course: Adaptive Optics and Imaging Through Random Media (3)
course: Principles of Image Science (3)
course: Statistical Mechanics (3)
course: Research Design & Analysis of Variance (3)
course: Graphical Exploratory Data Analysis (3)
course / course: Advanced Geographic Information Systems (3)
course: Stochastic Modeling I (3)
course: Engineering Decision Making Under Uncertainty (3)
course: Queuing Theory (3)
course: Simulation Modeling and Analysis (3)
course: Fundamentals of Optimization (3)
course: Advanced Quality Engineering (3)
course: Social Statistics (3)
course: – Bayesian Statistical Theory and Applications (same as course) (3)
course: Survey Sampling (3)
course: Statistical Computing (3)
Students have the option of completing a thesis in place of the qualifying exam. In addition to the required courses, master’s students complete their degree by taking an additional 4 (with the thesis option) or 5 courses.
All Statistics & Data Science Graduate Students are required to complete Communications requirements.
Prepare a basic web page containing information on their own research, teaching, and other professional activities and make this page available through the Program’s web site.
Prepare a professional CV and post it on the web site.
Write articles or proposals and give lectures or presentations for audiences of various levels of sophistication. At least one of these activities must be verbal, and at least one must be written. For a complete list of activities see the Student Handbook.
Please refer to the Graduate Student Handbook for students who are pursuing this program of study.
Minimum Credit Units
30
Core Coursework Requirements
Core MS Courses (18 units):
course / course: Advanced Statistical Regression Analysis* (3)
course: Statistical Machine Learning (3)
*These four courses will prepare a student to pass the qualifying exam following the completion of that spring semester. In order to receive a MS degree in Statistics, a student must pass a Qualifying Exam at the Master's Degree level. The exam, given in May and January, has two parts: Theory (covering course and course) and Methodology (covering course and course).
Elective Coursework
Elective Courses; minimum 9 units from any of the following:
course: Statistical Genetics for Quantitative Measures (3)
course: Biostatistics for Research (3)
course: Applied Biostatistics Analysis (3)
course: Data Management and the SAS Programming Language (3)
course: Analysis of Categorical Data (3) OR course / course: Categorical Data Analysis (3)
course: High Dimensional Health Data Analysis and Machine Learning (3)
course: Clinical Trials and Intervention Studies (3)
course: General Linear and Mixed Effects Models (3)
course: Special Topics in Biostatistics (3)
course: Survival Analysis (3)
course: Biostatistics Seminar (1)
course: Detection and Estimation in Engineering Systems (3)
course: Spatio-Temporal Ecology (3)
course: Introduction to Econometrics (3)
course: Econometrics (3) OR course: Advanced Applied Econometrics (4)
course: Econometrics (3)
course: Statistical Package for Research (3)
course: Educational Tests and Measurements (3) OR course: Statistical Methods in Psychological Research (3)
course: Multivariate Methods in Educational Research (3)
course: Theory of Measurement (3)
course: Theory of Measurement (3)
course: Advanced Data Analysis: Structural Equation Modeling (3)
course: Advanced Data Analysis: Dyadic Data and Bivariate Systems (3)
course: Advanced Data Analysis: Multilevel Modeling (3)
course / course / course: Spatial Statistics and Spatial Econometrics (3)
course: Applied Time Series Analysis (1-3)
course: Statistical Natural Language Processing (3)
course: Advanced Statistical Natural Language Processing (3)
course: Topics in Modern Analysis (3)
course: Theory of Graphs and Networks (3)
course: Stochastic Processes (3)
course: Stochastic Processes (3)
course: Stochastic Differential Equations (3)
course: Applied Stochastic Processes (3) OR course / course: Stochastic Methods in Surface Hydrology (3)
course: Topics in Applied Mathematics (3)
course: Bioinformatics and Functional Genomic Analysis (3)
course: Multivariate Analysis in Management (3)
course: Adaptive Optics and Imaging Through Random Media (3)
course: Principles of Image Science (3)
course: Statistical Mechanics (3)
course: Research Design & Analysis of Variance (3)
course: Graphical Exploratory Data Analysis (3)
course / course: Advanced Geographic Information Systems (3)
course: Stochastic Modeling I (3)
course: Engineering Decision Making Under Uncertainty (3)
course: Queuing Theory (3)
course: Simulation Modeling and Analysis (3)
course: Fundamentals of Optimization (3)
course: Advanced Quality Engineering (3)
course: Social Statistics (3)
course: – Bayesian Statistical Theory and Applications (same as course) (3)
course: Survey Sampling (3)
course: Statistical Computing (3)
Additional Requirements
An MS Thesis or in lieu of a Thesis, advanced statistical coursework; minimum 3 units as follows:
course: Thesis (3) OR any one of:
course: High-Dimensional Health Data Analysis and Machine Learning (3)
course: General Linear and Mixed Effects Models (3)
course: Survival Analysis (3)
course: Statistical Machine Learning (3)
course: Bayesian Statistical Theory and Applications (same as course) (3)
course: Survey Sampling (3)
course: Time Series Analysis (3)
course: Statistical Computing (3)