THE PROGRAM
The demand for trained statisticians and analysts continues to increase as the world becomes more dependent on predictive data and numerical reasoning. With a Master of Science in Statistics and Analytics degree students can begin or advance their career in almost any field, including education, science, technology, health care, government, or business. Alternatively, the master’s program can serve as preparation for additional doctoral studies.
To help you gain the necessary credentials to progress in this flourishing field, the Master of Science in Statistics and Analytics program provides an advanced curriculum designed by experts in the field and hands-on experience with the latest innovative software programs. This program can help students develop data analysis skills and explore applied statistics without delving too deeply into the foundations of mathematical statistics.
The program has three specializations designed to meet specific interests.
The Applied Statistics specialization is designed to provide you with hands-on, practical experience in statistical methods - methods you can apply to solving real-world problems. In contrast to programs that focus on pure theory, this degree may help you with a career in big data in the public or private sector. Applied statistics degrees are a mix of foundational theory and elective topics.
The Data Analytics specialization integrates a multidisciplinary approach that combines technical skills needed to interpret and optimize data with soft skills like storytelling to effectively communicate the narrative behind the data. This holistic approach to data analytics allows students to learn practical application of principles while using critical thinking to identify key questions and gain experiences in using data to extract value.
The Biostatistics specialization will train students in biostatistical methods for study design, data analysis, and statistical reporting for scientific and lay audiences. This degree will train students in key areas including data management, statistical reasoning, the interpretation of numeric data for scientific inference in studies in medicine and public health, and the ability to collaborate and communicate effectively with scientists and other public health stakeholders across disciplines. Graduates of the program are prepared to work as statisticians in a variety of professional environments including government, academic, healthcare, and industry.
FACULTY RESEARCH AND PUBLICATIONS
The faculty members of the Department of Mathematics and Statistics conduct applied and theoretical research in a variety of disciplines, including health and health care, criminology, policy research. In particular faculty have expertise in a variety of statistical techniques including big data techniques, structured and unstructured data, regression and classification, clinical trial research and design, Bayesian, classical hierarchical models, clustering methods, meta-analysis, mixture models, multilevel growth curve models, survival analysis, machine learning, supervised and unsupervised learning, natural language processing, deep learning, neural networks, decision trees and random forests, and spatial and spatio-temporal models. Additionally, faculty are well versed in a wide range of statistical and programming languages including R, SAS, SPSS, Minitab, Python, JMP, WinBUGS, and JAGS. Faculty also have backgrounds in computer science including database architecture, management, SQL processing, and other data tasks. In the past few years, faculty members have co-authored several dozen journal articles. This research has been supported by several large federal and foundation research grants and a number of smaller state and local awards. Opportunities exist for student involvement in this work, particularly since most of it is conducted in the Cleveland area. The Department is home of the Statistical Consulting Center, which provides statistical assistance to faculty, graduate students, and outside entities, and the College is home to the Applied Data and Modeling Center, which a first-of-its-kind interdisciplinary center in the heart of Northeast Ohio. These centers can provide valuable opportunities to graduate assistants to work on projects.
Current faculty information can be located on the Cleveland State University Faculty Profile page.
ADMISSION INFORMATION
In addition to meeting College of Graduate Studies requirements for admission, applicants are expected to have a baccalaureate degree with a minimum overall grade-point average of 3.0. An applicant for the applied statistics or biostatistics specializations should hold a bachelor’s degree in mathematics, statistics, computer science, engineering, data science, or other technical area. An applicant for the data analytics specialization may hold a bachelor’s degree in a field other than the ones listed above, but there should be evidence of preparation in these disciplines.
All applicants are also required to submit scores for the Graduate Record Examination.
All applicants must have an undergraduate course in inferential statistics and a programming course which are essential preparatory courses for all tracks.
Students applying to the Applied statistics or Biostatistics tracks are required to have a more developed mathematical background that includes coursework in multivariate calculus and linear algebra.
An applicant without this preparation may be admitted on a probationary basis, subject to removing deficiencies during the first year of graduate study. Applicants without an undergraduate or graduate course in inferential statistics must first complete STA 323 or an equivalent course in Statistical Methods prior to entry into the program.
Students applying for admission must submit transcripts and two letters of reference, at least one of which should be from a professor who can discuss the student’s potential in this program.
FINANCIAL ASSISTANCE
Graduate assistantships and tuition grants are available to qualified students. Interested students should contact the Mathematics and Statistics Department Graduate Program Director.
