The Master of Science in Statistics is designed to train students in the design and application of quantitative models to decision making in business, finance, pharmaceutical and other industries, and government. The MS program provides students with the concepts and skills that form the fundamental base of knowledge essential to statistics professionals in today's sophisticated business environment including the technical background and capabilities required for the newer approaches to overall business analytics and data mining. The MS program is designed to provide a concentrated, in-depth study of the field for those who wish to be technical specialists in statistics. Students completing the MS degree successfully go on to careers as statisticians and sometimes continue to pursue a Ph.D. in statistics. The MS is a 31.5 credit program consisting largely of statistics courses and some related business courses which can be completed either part-time or full-time. The MS program conforms with the DHS - STEM program so that international students who graduate from the MS program may be eligible for an additional 24-month extension on their optional practical training (OPT).
MS in Statistics Program Learning Goals
| General Statistical Competence | Students will be able to apply appropriate probability models and statistical techniques when analyzing problems form business and the other fields. |
| Statistical Practice | Students will become familiar with the standard tools of statistical practice for multiple regression, along with the tools of a subset of specialized statistical areas such as multivariate analysis, applied sampling, time series analysis, experimental design, data mining, categorical analysis, and/or stochastic processes. |
| Technology Competency | Students will learn to use one or more of the benchmark statistical software platforms, such as SAS or R. |
MS in Statistics Curriculum
Preliminary Courses (9 credits) Students with appropriate academic background will be able to reduce the number of credits in preliminary requirements. Grades in undergraduate mathematics courses are not calculated in the grade point average. | | Calculus I | 3 credits | | Calculus II | 3 credits | | Managerial Statistics | 3 credits | | *MTH 2610 and MTH 3010 are undergraduate courses. Entering students are strongly advised to complete a minimum of six credits of calculus before starting the MS programs in Statistics, in order to waive these math requirements. | | Courses in Specialization (31.5 credits) | Required for the General and Data Science Track (13.5 credits) | | Business Communication I | 1.5 credits | | Applied Regression Analysis | 3 credits | | Applied Probability | 3 credits | | Foundations of Statistical Inference | 3 credits | | Software Tools for Data Analysis (cross-listed with OPR 9750) | credits | General Track: Choose 12-18 credits from the list below. If you plan to specialize in the Data Science concentration, please ensure you take the appropriate electives specific to that track.
| | CIS/STA 9665 | Applied Natural Language Processing | 3 credits | | Time Series: Forecasting and Statistical Modeling | 3 credits | | Multivariate Statistical Methods | 3 credits | | Analysis of Categorical and Ordinal Data | 3 credits | | Statistical Methods in Sampling and Auditing | 3 credits | | Advanced Linear Models | 3 credits | | Financial Statistics | 3 credits | | Experimental Design for Business | 3 credits | CIS/STA 9760 | Big Data Technologies | 3 credits | | Stochastic Processes for Business Applications (cross-listed with OPR 9783) | credits | | Special Topics in Statistics | 1 credit | | Special Topics in Statistics | 1.5 credits | | Special Topics in Statistics | 2 credits | | STA 9794 | Special Topics in Statistical Analysis | 3 credits | STA 9797 | Advanced Data Analytics | 3 credits | STA/OPR 9850 | Advanced Statistical Computing | 3 credits | STA 9890 | Statistical Learning for Data Mining | 3 credits | STA 9891 | Machine Learning for Data Mining | 3 credits | | | Concentration in Data Science (16.5 credits): In addition to the 13.5 credits of required MS courses, students must take the following three required data science courses:
| STA 9705 | Multivariate Statistical Methods | 3 credits | STA 9890 | Statistical Learning for Data Mining | 3 credits | STA 9891 | Machine Learning for Data Mining | 3 credits | | Data Science Electives: Choose one course from the following | CIS/STA 9760 | Big Data Technologies | 3 credits | STA 9797 | Advanced Data Analysis | 3 credits | CIS/STA 9665 | Applied Natural Language Processing | 3 credits | | | Business Electives (6 credits): | Choose 6 credits of 9000-level courses from the graduate offerings of the Zicklin School of Business, with the exception of STA 9708; courses applied towards a prior master's degree; or courses that do not allow credit to be given for both that course and another statistics course. Students may take additional statistics courses as their business electives. |
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