Master of Science (MS) in Biostatistics with concentration in Data Science in Public Health (DSPH)
About the Program
The MS Biostatistics program with concentration in Data Science in Public Health prepares students to harness data management, mining and analytics, machine learning, and computational tools to analyze large-scale clinical and health data, identify trends, and create evidence-based solutions for improving population health. The program may be completed full-time (in four semesters) or part-time (completion varies) on-campus.
Students can enhance their skills through graduate certificates in one of the following areas, or create their own plan in consultation with an academic advisor:
- Health Analytics
- Population Health Informatics
- Health Systems Leadership
- Population Health
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Connect with our Admissions Team
Graduates of the program are employed as health data analysts in federal, state and local health departments, pharmaceutical companies, and nonprofit organizations and hospitals.
- Those with a strong interest in contributing to public and population health at the level of the community, region, nation or globe as a data science expert.
- Students with a desire for a deep understanding of underlying health issues and the ability to identify the most efficient computing algorithms and best analytical approaches.
- If you have interest in continuing as a doctoral student in Data Science or related fields, this degree is for you.
|
Meredith Ray, PhD |
Briana McNeil, MEd Coordinator, Recruitment and Admissions sphadmissions@memphis.edu 901.678.3740 |
Shirl Sharpe, MS Academic Services Coordinator II SPHadvising@memphis.edu 901.678.1710 |
Admission Information
Requirements:
- Completed
- Undergraduate degree with minimum GPA of 3.0 and have completed Calculus I and Calculus
II with a grade of B or
higher - Curriculum Vitae/Resume
- Statement of Purpose (400-500 words)
- Two (2) letters of recommendation
- Credentials evaluation for foreign transcripts
- ÃÛÌÒµ¼º½ accepts evaluated coursework from agencies certified by ) or . SpanTran and IEE are our recommended international transcript evaluation services. They have created a custom application for the University of ÃÛÌÒµ¼º½ that will help you select the right kind of evaluation at a discounted rate. You can access their application here: or
- Language proficiency test if language of instruction was not EnglishInternational applicants
Deadlines
International applicants should plan to have their applications by May 15 for Fall Semester and October 15 for Spring Semester to ensure sufficient time to receive your Form I-20 and visa.
- Fall Semester – August 15(a)
- Spring Semester – January 15(a)
Curriculum
Students learn to apply computational, machine learning and data mining techniques
to manage and analyze large,
complex and mutlisectoral health datatsets.The program requires a total of forty-two
(42) credit hours as follows:(a)
- CORE COURSES - 6 CREDIT HOURS
- PUBH 7170 | Epidemiology in Public Health I
- PUBH 7180 | Foundations of Public Health
- CONCENTRATION COURSES - 21 CREDIT HOURS
- PUBH 7150 | Biostatistical Methods I
- PUBH 7152 | Biostatistical Methods II
- PUBH 7310 | Mixed Model Regression Analysis
- PUBH 7311 | Applied Categorical Analysis
- PUBH 7410 | Biostatistical Machine Learning in Public Health
- COMP 7115 | Database Systems
- PUBH 7302 | SAS in Public Health
- ELECTIVE COURSES - 6 CREDIT HOURS
- In consultation with faculty and/or academic
advisors
- In consultation with faculty and/or academic
- CULMINATING EXPERIENCE - 3 CREDIT HOURS
- PUBH 7992 | Master’s Project Seminar OR
- PUBH 7996 | Master’s Thesis
Competencies
- Summarize public health data using statistical methods appropriate for the distribution of these data.
- Use statistical software to analyze clinical and public health data given appropriate for the given study design.
- Analyze large data using machine learning techniques.
- Use software for data cleaning and data management.
- Draw statistical inferences from different methodological approaches and communicate in writing the findings.