phd public health data science

Health Sciences Informatics, PhD

School of medicine.

The Ph.D. in Health Sciences Informatics offers the opportunity to participate in ground-breaking research projects in clinical informatics and data science at one of the world’s finest biomedical research institutions. In keeping with the traditions of the Johns Hopkins University and the Johns Hopkins Hospital, the Ph.D. program seeks excellence and commitment in its students to further the prevention and management of disease through the continued exploration and development of health informatics, health IT, and data science. Resources include a highly collaborative clinical faculty committed to research at the patient, provider, and system levels. The admissions process will be highly selective and finely calibrated to complement the expertise of faculty mentors.    

Areas of research:

• Standard Terminologies
• Precision Medicine Analytics
• Population Health Analytics
• Clinical Decision Support
• Translational Bioinformatics
• Health Information Exchange (HIE)
• Multi-Center Real World Data
• Telemedicine

Individuals wishing to prepare themselves for careers as independent researchers in health sciences informatics, with applications experience in informatics across the entire health/healthcare life cycle, should apply for admission to the doctoral program.

Admission Criteria

Applicants with the following types of degrees and qualifications will be considered:

• MA, MS, MPH, MLIS, MD, PhD, or other terminal degree, with relevant technical and quantitative competencies and evidence of scholarly accomplishment; or
• In exceptional circumstances, BA or BS, with relevant technical and quantitative competencies, with some combination of scholarly accomplishment and/or professional experience in a relevant field (e.g., biomedical research, data science, public health, etc.)

Relevant fields include: medicine, dentistry, veterinary science, nursing, ancillary clinical sciences, public health, librarianship, biomedical science, bioengineering and pharmaceutical sciences, and computer and information science. An undergraduate minor or major in information or computer science is highly desirable. Professional work experience in one of these fields is also highly desirable. 

The application is made available online through Johns Hopkins School of Medicine's website . Please note that paper applications are no longer accepted. The supporting documents listed below must be received by the SOM admissions office by December 15 of the following year. Applications will not be reviewed until they are complete and we have all supporting letters and documentation.

• Curriculum Vitae (including list of peer-reviewed publications and scientific presentations)
• Three Letters of Recommendation
• Statement of Purpose
• Official Transcripts from undergraduate and any graduate studies
• Certification of terminal degree
• You are also encouraged to submit a portfolio of published research, writing samples, and/or samples of website or system development

Please track submission of supporting documentation through the SLATE admissions portal.

If you have questions about your qualifications for this program, please contact [email protected]

Program Requirements

The PhD curriculum will be highly customized based on the student's background and needs. Specific courses and milestones will be developed in partnership with the student's advisor and the PhD Program Director.

The proposed curriculum is founded on four high-level principles:

• Achieving a balance between theory and research, and between breadth and depth of knowledge
• Creating a curriculum around student needs, background, and goals
• Teaching and research excellence
• Modeling professional behavior locally and nationally.

Individualized curriculum plans will be developed to build proficiencies in the following areas:

• Foundations of biomedical informatics: e.g., lifecycle of information systems, decision support
• Information and computer science: e.g., software engineering, programming languages, design and analysis of algorithms, data structures.
• Research methodology: research design, epidemiology, and systems evaluation; mathematics for computer science (discrete mathematics, probability theory), mathematical statistics, applied statistics, mathematics for statistics (linear algebra, sampling theory, statistical inference theory, probability); ethnographic methods.
• Implementation sciences: methods from the social sciences (e.g., organizational behavior and management, evaluation, ethics, health policy, communication, cognitive learning sciences, psychology, and sociological knowledge and methods), health economics, evidence-based practice, safety, quality.
• Specific informatics domains: clinical informatics, public health informatics, analytics
• Practical experience: experience in informatics research, experience with health information technology.

Basic Requirements

• "Core" courses
• Student Seminar & Grand Rounds
• Selective and Elective courses
• Mentored Research (in Year 1)
• Qualifying Exam (in Year 2)
• Proposal Defense (in Year 2 or 3)
• Dissertation (Years 2-4)
• Final Dissertation Defense (Year 4)
• Research Ethics

Academic Catalog

Phd in biostatistics and data science.

The PhD in Biostatistics and Data Science degree program is a minimum 48 semester credit hours and emphasizes advanced statistical theory and application, statistical consulting and independent research and prepares students to be independent investigators in the development and application of biostatistical analyses to problems of human health and disease. Graduates of the program go on to assume senior statistical posts in governmental or private health research agencies, or pursue careers in teaching and research.

Special Entrance Requirements

Applicants to the PhD program should have mathematical training beyond the introductory calculus level, including advanced calculus and linear algebra. Preference will be given to applicants with coursework in more advanced mathematics as well as statistics. They should hold degrees in areas that emphasize the development of strong quantitative skills, such as, degrees in mathematical, biomedical, physical, or social sciences. For more information, see the  Admissions section .

Course of Study

The following courses are required for the PhD in Biostatistics and Data Science:

Course List

Code	Title	Hours
Required for all School of Public Health Students
PH 101	Foundations of Public Health	0
Leveling Courses		0
	Introduction to R Programming for Biostatistics and Data Science
	Introduction to Python Programming for Biostatistics and Data Science
	Intermediate Biostatistics
	Applied Linear Regression
	Applied Multivariate Analysis for Biostatistics
	Categorical Data Analysis
	Probability and Distribution Theory
	Introduction to Data Science
	Fundamentals of Data Analytics and Predictions
Major Courses
	Survival Analysis	3
	Statistical Inference	3
	Linear Models I	3
	Generalized Linear Models	3
	Statistical Computing	3
	Stochastic Processes in Biostatistics I	3
	Biostatistics Seminar	1
Minor		9
Second Minor or Breadth		9
Electives		5
Research Practice Experience
	Research Practice Experience for Biostatistics Students	3
Dissertation
	Dissertation Research	3
Total Hours		48

Online, not-for-credit course

Academic credits from leveling courses do not count towards the total required number of credits for the degree program.

Students must successfully complete each course prior to sitting for the preliminary exam. 

Students are required to elect a minor outside of their department. Students should consult with their advisor and the minor’s department for requirements. Students may choose to complete a breadth or second minor. Students who do not elect an epidemiology minor must complete a three (3) credit hour epidemiology course as part of the breadth (2500-2999). Students who do complete an epidemiology minor must complete a three (3) credit hours course outside of both epidemiology and biostatistics for the breadth. Students who choose to complete a breadth should consult with their advisor to determine which courses are most appropriate for their academic and professional goals. Students who choose to complete a second minor should consult with their advisor and the minor’s department for requirements. 

Students are required to complete a minimum of 5 credit hours of electives from any biostatistics course above the 1700L level that is not already required on the degree planner. Students should consult with their advisor when selecting elective courses coursework appropriate for the student’s research and career goals. 

 For sample course of study, see the PhD in Biostatistics and Data Science degree planner . 

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Structured PhD Program in Health Data Sciences

The PhD Program in Health Data Sciences at the Charité is hosted in English and aimed at qualified young scientists interested in:

• deepening their methodological knowledge in the fields of biostatistics, epidemiology, public health, meta-research, population health science and medical informatics.
• further expanding their competence in research and teaching.

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• Academic Programs .
• PhD in Health Data Sciences

PhD Program in Health Data Sciences

Upon successful completion of the program, students will be awarded the academic degree of "Doctor of Philosophy" (PhD).

We are no longer accepting applications for entrance into our October 2024 cohort.

The deadline to submit applications for entrance into the October 2024 HDS PhD cohort was February 29, 2024 (23:59 CET).

We plan to host an informational session for prospective applicants in the fall of 2024. The application window for our October 2025 HDS PhD Cohort will open in the beginning of 2025. Please check back in the autumn of 2024 for further details. 

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Public Health Data Science

The MS in Biostatistics Public Health Data Science Track (MS/PHDS) is designed for students interested in careers as biostatisticians applying statistical methods in health-related research settings. The MS/PHDS Track provides core training in biostatistical theory, methods, and applications, but adds a distinct emphasis on modern approaches to statistical learning, reproducible and transparent code, and data management. It is an appropriate program for students who intend to conclude their studies with the MS degree as well as those who want to pursue a PhD in biostatistics

All MS/PHDS candidates begin their studies in the fall semester. The length of the MS/PHDS program varies with the background, training, and experience of the candidate, but the usual period needed to complete the 36 credit MS/PHDS degree is two years (four semesters). In addition to fulfilling their course work, all MS/PHDS students also complete a one-term practicum and capstone experience.

Competencies

Through a curriculum of 36 credit hours of course work, a practicum, and the capstone experience, the MS/PHDS track provides students with the skills necessary for a career as a public health data scientist and a rigorous grounding in traditional biostatistics.

In addition to achieving the MS in Biostatistics core competencies, students in the PHDS Track gain the following specific competencies in the areas of public health and collaborative research, the foundations of applied data science, teaching biostatistics and biostatistical research. Upon satisfactory completion of the MS/PHDS, graduates will be able to:

Public Health and Collaborative Research

• Formulate and prepare a written statistical plan for analysis of public health research data that clearly reflects the research hypotheses of the proposal in a manner that resonates with both co-investigators and peer reviewers;
• Prepare written summaries of quantitative analyses for journal publication, presentations at scientific meetings, grant applications, and review by regulatory agencies;

Foundations of Applied Data Science

• Develop expertise in one or more statistical software and database management packages (often R and SQL, among others) routinely used by data science professionals;
• Implement a reproducible workflow for data analysis projects, including robust project organization, transparent data management, and reproducible analysis results;
• Develop and execute analysis strategies that use traditional statistical tools or modern approaches to statistical learning, depending on the nature of the scientific questions of interest;
• Identify the uses to which data management can be put in practical statistical analysis, including the establishment of standards for documentation, archiving, auditing, and confidentiality; guidelines for accessibility; security; structural issues; and data cleaning;

Teaching Biostatistics

• Review and illustrate selected principles of study design, probability theory, estimation, hypothesis testing, statistical learning, and data analytic techniques to public health students enrolled in introductory level graduate public health courses; and

Biostatistical Research

• Apply probabilistic, statistical, and data scientific reasoning to structure thinking and solve a wide range of problems in public health.

Course Requirements

MS/PHDS graduates are expected to master the mathematical and biostatistical concepts and techniques presented in the curriculum’s required courses. Each student's program is designed on an individual basis in consultation with a faculty advisor taking into consideration the student's prior educational experience.

Students who have mastered an academic area through previous training may have the corresponding course requirement waived. Some students, such as those with undergraduate majors in statistics or mathematics, may apply to have several courses waived. Students wishing to waive one or more courses must request approval in writing from their advisors and the Director of Academic Programs. These students must still complete a minimum of 36 points to earn the MS/PHDS degree.

Required Courses

Below is the required course work. Students consult their faculty advisors before registering for classes to plan their programs based on their individual background, goals, and the appropriate sequencing of courses. Waiver of any required courses (with prior written approval of their faculty advisor and the Director of Academic Programs) enables students to take other, higher level classes.

Course #	Course Name	Points
P6400	Principles of Epidemiology	3
P8104	Probability	3
P8105	Data Science I	3
P8106	Data Science II*	3
P8109	Statistical Inference	3
P8130	Biostatistical Methods I	3
P8131	Biostatistical Methods II	3
P8180	Relational Databases and SQL Programming for Research and Data Science	3
P8185	Capstone Consulting Seminar	1

*Students who have strong math background and/or have taken basic machine learning methods, can substitute the P8106 Data Science II with P9120 Topics in Statistical Learning and Data Mining I. 

Students choose four or more courses from the list below or from alternatives approved by their academic advisors.

Course #	Course Name	Points
P6110	Statistical Computing with SAS	3
P8108	Survival Analysis	3
P8119	Advanced Statistical and Computational Methods in Genetics and Genomics	3
P8124	Graphical Models for Complex Health Data	3
P8157	Analysis of Longitudinal Data	3
P8158	Latent Variable and Structural Equation Modeling for Health Sciences	3
P8160	Topics in Advanced Statistical Computing	3
P9120	Topics in Statistical Learning and Data Mining	3

Sample Timeline

Below is a sample timeline for MS/PHDS candidates. Note that course schedules change from year to year, so that class days/times in future years will differ from the sample schedule below; you must check the current course schedule for each year on the course directory page .

Fall I	Spring I	Fall II	Spring II
P6400: Principles of Epidemiology	P8109: Statistical Inference	P8180: Relational Databases and SQL Programming for Research and Data Science	P8185: Capstone Consulting Seminar
P8104: Probability	P8106: Data Science II	Elective
P8105: Data Science I	P8131: Biostatistical Methods II	Elective
P8130: Biostatistical Methods I	Elective	Elective

Practicum Requirement

One term of practical experience is required of all students, providing educational opportunities that are different from and supplementary to the more academic aspects of the program. The practicum may be fulfilled during the school year or over the summer. Arrangements are made on an individual basis in consultation with faculty advisors who must approve both the proposed practicum project prior to its initiation, and the report submitted at the conclusion of the practicum experience. Students will be required to make a poster presentation at the department’s Annual Practicum Poster Symposium which is held in early May.

Capstone Experience

A formal, culminating experience for the MS degree is required for graduation. The capstone consulting seminar is designed to enable students to demonstrate their ability to integrate their academic studies with the role of biostatistical consultant/collaborator, which will comprise the major portion of their future professional practice.  

As part of the seminar, students are required to attend several sessions of the Biostatistics Consulting Service (BCS). The Consultation Service offers advice on data analysis and appropriate methods of data presentation for publications, and provides design recommendations for public health and clinical research, including preparation of grant proposals. Biostatistics faculty and research staff members conduct all consultation sessions with students observing, modeling, and participating in the consultations.

In the capstone seminar, students present their experience and the statistical issues that emerged in their consultations, developing statistical report writing and presentation skills essential to their professional practice in biomedical and public health research projects.

  [email protected]

Welcome to the  Harvard University PhD in Population Health Sciences  (PHS). Our full-time doctoral degree is a joint collaboration between the  Harvard Faculty of Arts and Sciences (FAS)  and the  Harvard T.H. Chan School of Public Health   and offer s a  Doctorate of Philosophy (PhD)  in  P opulation Health Sciences .   Our research program is designed to allow students to benefit from connections between public health disciplines and a broader range of academic disciplines represented across the Harvard Griffin Graduate School of Arts and Sciences (GSAS).  

A  PHS   PhD   offers advanced doctoral-level research and training that builds on multiple disciplinary perspectives to understand the origins and determinants of health and disease across populations. Our students are based at the Harvard T.H. Chan School of Public Health, and belong to one of the following department-based   Fields of Study :  

• Environmental Health (EH)
• Epidemiology (EPI)
• Global Health & Population (GHP)
• Nutrition (NUT)
• Social & Behavioral Sciences (SBS)

This  PhD in Population Health Sciences (PHS)  is intended for students who are looking to pursue careers in academia in one of five   Fields of Study   as well as in organizations related to population health or research-related positions beyond academia. In addition to nurturing the development of the next generation of population health researchers and scientists , PHS provides tremendous opportunities for students to build scientific communication and mentoring, and teaching skills – while all along, building lasting connection s ac ross students, alumni, and faculty at our world- r enown ed Harvard University .  

Harvard University and the PHS PhD program do not discriminate against applicants or students on the basis of race, color, national origin, ancestry or any other protected classification.

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Data Science Pathway

The Biostatistics data science pathway combines rigorous statistical training with the development of advanced computational skills to solve the public health challenges of tomorrow. Required courses cover epidemiology, regression models, databases, machine learning and more. Students will become familiar with data science programming tools (e.g. R, Python, SQL and NoSQL databases). Data science pathway graduates can find careers analyzing large volumes of health data in government (e.g. public health agencies), hospitals, industry (e.g. pharmaceutical companies) or research.

Students pursuing this pathway will graduate with the key skills of any Biostatistician. Unlike the traditional pathway, data science pathway students will have more experience using computational techniques to store, manipulate and analyze large volumes and varieties of data. This pathway trains biostatisticians; as such, it emphasizes the development and application of rigorous statistical theory to extensive health data sets, as opposed to the application of the latest computational techniques that are prioritized in the health informatics masters. The focus on health applications differentiates this pathway from the MS in Data Science and Statistics.

Requirements - Data Science Pathway

The M.S. Biostatistics Standard Pathway degree requires a total of 16-course units from the curriculum below (BIS 525/526 and EPH 100/101 are not for credit). Course substitutions must be approved by the student’s advisor and the DGS. Electives not listed below must be approved by the BIS Data Science Pathway Director.

Full-time students must carry a minimum of 4 course units each semester. Course schedules with more than 5 courses for credit will not be approved. If students have fewer than 4 required courses to take in their last term, it is acceptable to register for just the courses needed to fulfill the degree requirements.

2024-25 Matriculation

All courses count as 1 credit unless otherwise noted.

MS Required Courses (10 course units)

• BIS 525 Seminar in Biostatistics and Journal Club - 0 units
• BIS 526 Seminar in Biostatistics and Journal Club - 0 units
• BIS 620 Data Science Software Systems
• BIS 623 Advanced Regression Models [or S&DS 612 Linear Models] *BIS 623 is a 1st year class only; BIS 623 is a prerequisite for BIS 630
• BIS 628 Longitudinal and Multilevel Data Analysis
• BIS 630 Applied Survival Analysis [or BIS 643 Theory of Survival Analysis] *BIS 630 is a 1st year class only; BIS 623 is a prerequisite for BIS 630
• BIS 678 Statistical Practice I *2nd year class only
• BIS 687 Data Science Statistical Practice- Capstone Experience *2nd year class only
• EPH 509 Fundamentals of Epidemiology
• EPH 608 Frontiers of Public Health*
• S&DS 541 Probability Theory [or S&DS 600 Advanced Probability or S&DS 551 Stochastic Process]
• S&DS 542 Theory of Statistics [or S&DS 610 Statistical Inference]
• BIS 695 Summer Internship in Biostatistical Research - 0 units
• EPH 100 (Fall); EPH 101 (Spring) Professional Skills Series - 0 units

Biostatistics/Statistics/Computer Science Electives: Minimum of 2 of the following REQUIRED

• BIS 536 Measurement Error and Missing Data
• BIS 537 Statistical Methods for Causal inference
• BIS 540 Fundamentals of Clinical Trials
• BIS 550/CB&B 750 Topics in Biomed Informatics and Data Science
• BIS 555 Machine Learning and Biomedical Data **
• BIS 567 Bayesian Statistics
• BIS 629 Advanced Methods for Implementation and Prevention Science
• BIS 634 Computational Methods for Informatics **
• BIS 645 Statistical Methods in Human Genetics
• BIS 646 Nonparametric Statistical Methods & their Applications
• BIS 662 Computational Statistics **
• BIS 692/S&DS 645 Statistical Methods in Computational Biology
• CB&B 562 Modeling Biological Systems II
• CB&B 752 Biomedical Data Science: Mining and Modeling
• CPSC 519 Full Stack Programming
• CPSC 526 Building Distributed Systems
• CPSC 539 Software Engineering
• CPSC 565 Theory of Distributed Systems
• CPSC 577 Natural Language Processing
• CPSC 588 AI Foundation Models
• CPSC 640 Topics in Numerical Computation
• CPSC 642 Modern Challenges in Statistics: A Computational Perspective
• EMD 553 Transmission Dynamic Modeling of Infectious Diseases
• HPM 573 Advanced Topics in Modeling Health Care Decisions
• S&DS 541 Probability Theory with Applications (Cannot fulfill elective if taken as a requirement)
• S&DS 551 Stochastic Processes (Cannot fulfill elective if taken as a substitute for S&DS 541)
• S&DS 611 Selected Topics in Statistical Decision Theory
• S&DS 661 Data Analysis
• S&DS 664 Information Theory

Electives in Machine Learning: Minimum of 1 of the following REQUIRED

• BIS 568 Applied Machine Learning in Healthcare
• BIS 691 Theory of Generalized Linear Models
• CB&B 555/ AMTH 553/CPSC 553 Unsupervised Learning for Big Data
• CB&B 663/ CPSC 552/AMTH 552 Deep Learning Theory and Applications
• CPSC 569 Randomized Algorithms
• CPSC 583 Deep Learning on Graph-Structured Data
• CPSC 644 Geometric and Topological Methods in Machine Learning
• CPSC 670 Topics in Natural Language Processing
• S&DS 517 Applied Machine Learning and Causal Inference
• S&DS 538 (Bayesian) Probability and Statistics
• S&DS 562 Computational Tools for Data Science
• S&DS 565 Introductory Machine Learning
• S&DS 569 Numerical Linear Algebra: Deterministic and randomized algorithms
• S&DS 631 Optimization and Computation
• S&DS 632 Advanced Optimization Techniques
• S&DS 665 Intermediate Machine Learning
• S&DS 674/ F&ES 781 Applied Spatial Statistics
• S&DS 684 Statistical Inference on Graphs
• S&DS 685 Theory of Reinforcement Learning
• S&DS 686 High-dimensional phenomena in statistics and learning

Electives in Databases: Minimum of 1 of the following REQUIRED

• BIS 550/CB&B 750 Topics in Biomed Informatics and Data Science **
• BIS 638 Clinical Database Management Systems and Ontologies
• BIS 679 Advanced Statistical Programming in SAS & R
• CPSC 537 Introduction to Database Systems
• MGT 656 Management of Software Development
• MGT 660 Advanced Management of Software Development

Additional Electives: 2 REQUIRED

Take two additional course units from the electives listed above. Other courses from YSPH or another department must be approved by the Data Science Pathway Director.

Other Courses

• CPSC 566 Blockchain and Cryptocurrency
• S&DS 506 Introduction to Statistics: Data Analysis
• S&DS 617 Applied Machine Learning and Causal Inference Research Seminar
• S&DS 530 Data Exploration and Analysis
• S&DS 563 Multivariate Statistical Methods for the Social Sciences
• MGT 556 Big Data and Customer Analytics
• MGT 575 Social Media Analytics
• MGT 595 Quantitative Investing

Public Health Data Science Concentration

What You'll Learn

Data science draws upon multiple disciplines, combining the statistical skills to manipulate data and make inferences, the mathematical skills to model phenomena and make predictions and the computer science skills to manage and analyze large data sets.

Steeped in the public health context, our program offers a unique focus on leveraging the foundational statistical, mathematical and computer science elements of data science to generate useful information from data sources relevant to public health. As a student in this concentration, you will benefit from the instruction and mentorship of top-ranked faculty in the biostatistics department and across the Gillings School. Our chief focus is to optimize data science to help address the most critical public health problems in the world today.

This program will empower you with the knowledge and skills to achieve the following core competencies:

Required Courses & Sample Plan of Study

In addition to the interdisciplinary, 14-credit Gillings MPH Core , students will take six concentration-specific courses on topics such as experimental analysis, machine learning and epidemiology. 

BIOS 512: Data Science Basics

BIOS 645: Principles of Experimental Analysis

BIOS 650: Basic Elements of Probability and Statistical Inference, Part I

BIOS 635: Introduction to Machine Learning

EPID 710: Fundamentals of Epidemiology

BIOS 992: Public Health Data Science MPH Culminating Experience

Fall 2023 Cohort: Public Health Data Science Degree Requirements and Plan of Study (PDF)

Fall 2024 Cohort: Public Health Data Science Degree Requirements and Plan of Study (PDF)

A Note About our MPH and MS

The MPH degree is considered a professional degree with the expectation that most students will pursue employment after graduation. The MS degree is considered a research degree in that students are prepared to pursue a doctoral degree after graduation. Note, however, that there can be exceptions to both.

The MPH in Public Health Data Science at UNC is different from our MS program in Biostatistics in several ways:

The MPH student will take a variety of core public health courses in addition to courses in data science, biostatistics, and epidemiology, plus electives. The electives can be taken across campus with the only requirement that they have a data science component and are eligible for graduate course credit. To date, our MPH students have taken electives in the business, economics, computer science, and informatics departments, for a few examples. In contrast, the MS student will focus primarily on biostatistics courses plus electives in public health. The MS curriculum involves more probability and statistical theory and requires a more rigorous mathematical background. As an example, the MPH curriculum includes a 3-credit hour course in probability and statistical inference in the fall and a 3-credit hour course in experimental design and data analysis in the spring. The parallel requirement for MS students requires 6 credit hours each semester, enabling more topics to be covered, and in more detail.

Note that these comparisons are just for the two programs at UNC. Programs at other universities may differ in their requirements, curricula and career opportunities.

Ready to Apply?

Click below to learn more about the admissions process for this concentration.

Putting research into action

Scientific discoveries made by Gillings School biostatistician Dr. Michael Kosorok (at left) were put into practice by pediatric pulmonologist Dr. George Retsch-Bogart. The result was better hospital care and better health for children with cystic fibrosis.

Hosted by the Gillings School’s internationally renowned Department of Biostatistics, the Master of Public Health (MPH) concentration in Public Health Data Science is designed for students with a strong mathematical background who wish to develop advanced data science skills — including machine learning, data visualization and statistical inference — and apply them in a public health context.

Your education will equip you with the advanced skills needed to:

• Manipulate, mine, search and visualize data from large, complex public health data sets (such as disease registries, population data and health care claims data);
• Apply probability and statistical inference to understand relationships or test hypotheses relevant to health problems and interventions; and
• Contribute data science leadership and expertise to advance practical solutions to public health challenges.

Data science skills are in high demand among employers across a wide array of sectors. Graduates of this concentration may embark on careers as data scientists, data analysts and statistical analysts, among other options.

Career Opportunities

99% of Gillings graduates  have a job or continue their education within one year of graduating.

Learn more about the opportunities that await you with an MPH from the Gillings School.

Students fund their education through a combination of sources, including loans, fellowships and awards, assistantships and grants. Several awards and scholarships are supported by generous contributions to the School and University who value the current and future contributions of our students.

Contact: Sarah Kitchens, Funding and Awards Coordinator,  [email protected]

Office of Scholarships and Student Aid

The UNC Office of Scholarships and Student Aid advises, assesses and approves students for a variety of financial aid opportunities. These include scholarships, grants and loans.

Funding from the Graduate School

The Graduate School offers resources that are designed to help students compete for internal and external grants and fellowships critical to the financial support of our graduate students.

Costs and Residency

Tuition and Fee information can be found on the Office of the University Cashier’s website. You can also view your student account and bill in Connect Carolina. Residency information can be found on the University Registrar website.

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Advanced Certificate in Public Health Data Science

Public Health Data Science draws upon methods from statistics, epidemiology and computer science. The advanced certificate in Public Health Data Science will provide students and practitioners with training in biostatistics, epidemiology, regression and data science, as applied to public health research and practice.  This will prepare them to work at the intersection of these fields to advance public health research and practice.

The advanced certificate is currently open to non-student professionals and GPH students in the MPH and MS programs.

Upon completion of the certificate and the MPH or the MS in Epidemiology, students will have a total of 53 credits. The certificate is 16 credits, 9 credits of which may be double-counted with the 46-credit MPH or MS. The courses that are double counted include GPH-GU 2995 or 5995, GPH-GU 2106 or 5106, and GPH-GU 2353, and then students will take an additional 7 certificate credits. 

Upon completion of the certificate and the MS in Biostatistics , students will have a total of 49 credits. The certificate is 16 credits, 13 credits of which may be double-counted with 46-credit MS. The courses that are double counted include GPH-GU 2995 or 5995, GPH-GU 2106 or 5106, GPH-GU 2183, GPH-GU 2184, and GPH-GU 2353, and then students will also take GPH-GU 2338 (3 credits).

International Students who pursue the certificate  with the MPH or MS  are not allowed to receive a Program Extension as the certificate is not required to complete the MPH or MS program. Therefore, they must complete the certificate by the time they graduate from their MPH or MS.

Students will work in many possible settings.  The certificate training provides a very broad base of knowledge that will gain them entry into several types of positions:

• Academic Medical Center, e.g., Biostatistics Departments, Predictive Analytics Cores
• Pharmaceutical Company
• Health Insurance Company
• Financial Consulting
• UNICEF, WHO, etc.

Program Outcomes

It is absolutely necessary for students to have strong competencies in the analytical tools of both public health and modern data science in order to be competitive for several types of jobs in public health and in other industries that require modern data analysis and manipulation. The certificate program provides an organized framework for students to obtain the skillset needed to perform well in these areas. Each course in this certificate teaches students both technical fundamentals and tools, and highlights their ties to public health data sets and research questions. This is done through teaching examples and analyses of real public health data in homework and projects. 

Upon graduating from the Advanced Certificate, students will have acquired the following skills: 

• Apply descriptive and inferential methodologies according to the type of study design for answering a particular research question. [2995, 2106]
• Harness basic concepts of probability, random variation and commonly used statistical probability distributions. [2995,2353,2106, 2338]
• Distinguish among the different measurement scales and the implications for selection of statistical methods to be used based on these distinctions. [2995,2353,2106, 2338]
• Implement the appropriate analytic methods for calculating key measures of association. [2995,2353,2106, 2338]
• Understand and apply ethical principles to data acquisition, management, storage, sharing, and analysis [2183,2184, 2338]
• Interpret results of statistical analyses found in public health research studies. [2995,2353, 2106, 2338]
• Utilize relevant statistical software for data analysis. [2183,2184,2353,2338]

The advanced certificate may be taken as a hybrid of online and classroom-based courses. The courses focus on methods for study design and analysis and on statistical computing and data science tools.  GPH MS-Biostatistics students planning to earn the advanced certificate must take a total of 49 credits , 13 credits of which will  double count with the MS, plus 3 additional credits taken as GPH-GU 2338 Machine Learning in Public Health. GPH MS-Epidemiology and GPH MPH  students planning to earn the advanced certificate must take a total of 53 credits, 9 credits of which will double count with the MS, plus 7 additional credits. Listed below are the required six courses that provide the training for the Public Health Data Science advance certificate. 

Please note the pre-requisites and co-requisites for each course to avoid being closed out of certain courses.

GPH-GU 2995 Biostatistics for Public Health (3 credits) 1  *

This course covers basic probability, descriptive and inferential statistics, and the role of biostatistics in the practice of public health. Specific attention will be given to common probability distributions in public health and medicine, t-tests, Analysis of Variance, multiple linear and logistic regression, categorical data analysis, and survival analysis. Statistical topics are presented conceptually with little derivation, and applications are demonstrated using common statistical software, Stata.

GPH-GU 2106 Epidemiology (3 credits) 3  *

Epidemiology is the study of the distribution and determinants of health and disease in different human populations and the application of methods to improve disease outcomes. As such, epidemiology is the basic science of public health. This course is designed to introduce students in all fields of public health to the background , basic principles and methods of public health epidemiology. Topics covered in this course Include: basic principles of epidemiology; measures of disease frequency; epidemiologic study designs: experimental and observational; bias; confounding; outbreak investigations; screening; causality; and ethical issues in epidemiologic research. In addition, students will develop skills to read, interpret and evaluate health Information from published epidemiologic studies. 

GPH-GU 2183 Introduction to Statistical Programming in R (2 credits)

R is one of the most popular programming languages in statistics and data science. This course will introduce various R programming topics, including data visualization, exploration, and transformation, via illustrations with public health datasets. Students will learn how to program in R effectively and efficiently for data analysis with popular R packages including dplyr, tibble, readr, and ggplot2. By the end of the course, students will be able to write R codes from scratch for data visualization, exploratory analysis, transformation, and import & export. This course does not require prior experience in programming or statistics and serves as a foundation for other courses in data science. Students are recommended to take the follow-up course: Intermediate Statistical programming in R.

GPH-GU 2184 Intermediate Statistical Programming in R (2 credits)

R is one of the most popular languages in data science. This course is the follow-up of GPH-GU 2183 Introduction to Statistical programming in R, and covers intermediate R programming topics, including data wrangling, R Markdown, and simple statistical simulations. The course will focus on public health datasets as illustrations to best meet the practical needs of GPH students but is also open to those of other backgrounds. By the end of the course, students will be able to comfortably program in R for effective data preprocessing, analysis and presentation. In addition, students will be able to write statistical reports with reproducible codes using R Markdown. This course serves as a good preparation for courses in statistics and machine learning. Prerequisite: GPH-GU 2183

GPH-GU 2353 Regression I: Linear Regression and Modeling (3 credits) 2

Regression models are one of the most important statistical techniques used in public health. This course focuses on data analysis that use linear regression models for continuous outcomes. The first part of this course introduces simple and multiple linear regressions, principles of ordinary least square regression models, model assumptions, and inferences about model parameters. The second part of the course focus on important practical matters, such as prediction, variable selection, moderated effects, and mediation. These two parts together provide the foundations for more advanced statistics modeling. Examples are drawn from broad areas of public health research. All the analyses will be taught and performed using Stata statistical software. Prerequisite: GPH-GU 2995/5995

GPH-GU 2338 Machine Learning in Public Health (3 credits)

This course provides students with a strong foundation in machine learning relevant to public health and biomedical applications. Topics include the data generating process, model selection and evaluation, generalized linear models, common supervised and unsupervised machine learning algorithms such as support vector machines, decision trees, random forests, neural networks, and k-means, and ethics and communication. Students will learn methods for optimal and proper implementation of machine learning, such as assessment of assumptions about the data generating process, feature generation, treatment of missing data, and reduction of bias. Students will gain familiarity with the potential power of machine learning in public health, as well as its particular challenges inherent to public health applications. Prerequisite: GPH-GU 2184 Prerequisite or corequisite: GPH-GU 2353

Note: Students who have taken the equivalent of any of these courses prior to their enrollment  at GPH will substitute advanced courses on the same topics:

1 substitute GPH-GU 3225 Statistical Inference

2 substitute GPH-GU 2354 Regression II: Categorical Data Analysis

3 substitute GPH-GU 2450 Intermediate Epidemiology, GPH-GU 2930 Epidemiological Methods and Design , APSTA-GE 2012 Causal Inference, GPH-GU 2363 Causal Inference: Design and Analysis.

* this course is also offered online.

Prerequisites

Applicants must have already obtained an undergraduate degree. They should have work experience in data science OR they should be currently enrolled in or have completed a Masters of Public Health, a Masters of Science in Biostatistics, a Masters of Science in Epidemiology, a PhD in Public Health, a Masters of Public Policy, Masters of Public Administration, Law or Medical, Dental or Nursing degree program. Other graduate degree programs will be considered on a case-by-case basis. They should be able to articulate a clear interest in and understanding of Public Health Data Science.

Current GPH MS and MPH students can apply through this application .

All other applicants must submit applications online through SOPHAS Express , the common application for schools and programs of public health. In order to be eligible for the certificate, you must hold the following:

Bachelor's degree or US equivalent from an accredited institution

Minimum 2.75 cumulative undergraduate GPA

To apply, you must submit your application as well as the following materials:

Scanned copies of transcripts for all post-secondary education completed, regardless of whether a degree was awarded

Resume or CV

Personal statement of no longer than 1200 words expressing a rationale for pursuing the certificate

1 letter of support from either a professional or academic reference

Financial Aid

You may be eligible for federal financial aid and/or private educational loans to pursue the certificate program. Learn more about your options for aid from GPH’s Office of Financial Aid .

For More Information

For additional information please email [email protected] 

Online Learning at GPH

The School of Global Public Health is dedicated to providing a connected, professional, and scholastic environment for our online courses.

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Join a dynamic community of future healthcare leaders, where evidence-based research meets impactful community engagement. Explore our state-of-the-art facilities, build close-knit relationships, and make a difference in public health.

Step into a dynamic world of health promotion sciences with our PhD degree at OU Hudson College of Public Health. Our graduates are making a difference in public health, addressing social determinants of health, promoting health equity, and reducing health disparities.

This advanced, research-oriented 60-credit hour program immerses you in an in-depth study of health promotion sciences, preparing you to make significant contributions in this field. Envision yourself mastering courses like Health Promotion Theory I: Individuals and Small Groups, where you'll delve into the nuances of health promotion at a micro level. In Social Marketing, you'll learn to apply marketing strategies for health promotion, while Advanced Research Methods in Social and Behavioral Sciences will equip you with the tools to conduct rigorous research.

Course Requirements

Core Courses
HPS 6633	Health Promotion Theory I: Individuals and Small Groups
HPS 6643	Health Promotion Theory II: Communities, Organizations, and Government
HPS 6943	Advanced Program Evaluation

Methods Courses
15 credit hours, not including any required pre-requisites

Required Specific Courses:
HPS 6933	Qualitative Research Methods in Public Health
5663   5183	Analysis of Frequency Data   Intermediate Biostatistical Methods for Health Professionals
HPS 6953	Advanced Research Methods in Social and Behavioral Sciences

Two additional courses in either Qualitative or Quantitative Methods Electives, such as the following options:

Qualitative Methods Electives (Prerequisite: HPS 6933):
HPS 6453	Focus Group Research
HPS 6833	Social Marketing
SOC 5313	Mixed Methods

Quantitative Methods Electives (Prerequisite: BSE 5173 or BSE 5663):
BSE 5643	Regression Analysis
BSE 5653	Nonparametric Methods
BSE 6643	Survival Data Analysis
BSE 6663	Analysis of Multivariate Data

Substantive Area (Major): 15 credit hours

A minimum of fifteen credit hours in a substantive area of public health / health promotion will comprise the primary area of concentration specific to the student’s interests. Examples of concentration areas relevant to this department include social determinants of health, minority health, health disparities, nutritional health/food security, workforce development, health and aging, and social justice. Students and advisors must identify sufficient courses to satisfy the declared major.

Related Area (Minor): 9 credit hours
A minimum of nine credit hours from relevant areas of public health or an established discipline in the social and behavioral sciences will comprise a secondary area of concentration specific to the student’s interests.

Dissertation: Minimum of 12 credit hours
Dissertation work occurs in steps. Close collaboration with the faculty advisor and dissertation committee members is required throughout the process.

Other Opportunities and University Requirements: 1 credit

Course Sequencing

Not all courses are offered on an annual basis and certain courses are important prerequisites for other courses.  In order to assure that students are following the proper course sequence, all students must meet with their advisor each semester in order to complete enrollment for the next semester.  In addition, all students are requested to enroll for at least six credit hours per semester to facilitate students progressing through the curricula as a cohort.

Courses outside the College of Public Health can support a concentration and are acceptable curricular elements with advisor approval.  Students will work with faculty advisors to determine the optimal selection of coursework.

STEM Designation

The Doctor of Philosophy in Health Promotion Sciences program is NOT considered a STEM-designated degree program.

A STEM-designated program is an academic program that falls under at least one of the approved categories from the U.S. Department of Homeland Security (DHS). These categories are recognized by the government for their focus on science, technology, engineering, and math (STEM) topics. DHS's updated STEM-designated degree list can be found here:  https://www.ice.gov/doclib/sevis/pdf/stemList2022.pdf .

Explore the Hudson College Further

Academic Resources

At the Hudson College of Public Health, students are empowered with a diverse range of accredited degree programs, hands-on learning experiences, and state-of-the-art research opportunities, all guided by our multidisciplinary faculty.

The Hudson College of Public Health is enriched by its research partnerships with esteemed institutions and organizations, providing students with a robust, real-world learning experience that complements their academic journey.

At the Hudson College of Public Health, students apply their curriculum through immersive field experiences and practicums, fostering a hands-on, real-world understanding of public health challenges and solutions.

Department of Health Promotion Sciences

The Department of Health Promotion Sciences provides a community-engaged and innovative program that prepares students to become leaders in health promotion and policy. Our department offers a diverse range of degrees, including the only MPH/MSW dual degree with the OU School of Social Work. Students benefit from our faculty’s real-world experience, our commitment to social justice and improving health outcomes, and our strong partnerships with key public health leaders and tribal communities.

About the Department  

Health Promotion Sciences Faculty

Ashlea Braun, PhD, RD

Assistant professor.

Elizabeth (Betsy) Charron, PhD, MS, MPH

Lois Coleman, PhD, MCHES

Karla Finnell, Ph.D.

Neil Hann, MPH, CHES

Chair & lecturer director, center for public health practice.

Robert John, Ph.D.

Sara King, MPH

Associate director,  bachelor of public health program lecturer, health promotion sciences.

Dan Li, Ph.D., NCC, LSC-NC (K-12)

Associate professor.

Zachary Massey, PhD

Catherine Nagawa, Ph.D.

Zachary C. Pope, PhD, ACSM-EP

Cassandra Querdibitty, Ph.D., MPH

Kerstin M. Reinschmidt, PhD, MPH

Jamie Rhudy, PhD

Veronica Richards, PhD, MPH

Lancer Stephens, Ph.D. (Wichita/Creek)

Associate dean for sovereignty, opportunity, belonging & engagement associate professor.

Thomas A Teasdale, DrPH, FGSA, FAGHE

Presidential professor emeritus.

Michelle vanDellen, PhD

Gabrielle Westbrook, MPH

Instructor, associate director of the center for public health practice.

Marianna Wetherill, Ph.D., MPH, RDN/LD

George kaiser family foundation chair in population healthcare henry zarrow presidential professor associate professor, take the next step.

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Leveraging Data Science and Biomedical Informatics to Improve Health Equity and Population Health

Leveraging Data Science and Biomedical Informatics to Improve Health Equity and Population Health featuring Jiancheng Ye, PhD 

Jiancheng Ye, PhD 

Assistant Professor of Health Informatics

Department of Emergency Medicine

Weill Cornell Medicine

Certificate in Data Science

The rise of data science has driven advances in technology across almost all areas of our life, including health. Modern computational tools give us the ability to manage, process, and analyze data on previously unthinkable scales. Recent advances in statistics and machine learning allow us to glean new insights for these data. These new advances demand an innovative approach to training public health practitioners of the future. Trainees should be equipped with a skill set that allows them to address challenges raised by modern approaches to data collection and analysis. Trainees must also be equipped with an understanding of the challenges, limitations, and ethical implications of these novel approaches. Students in the Data Science Certificate program at RSPH will be trained to meet the needs of a rapidly advancing health research field. Pursuing data science training within a top public health school will allow students to see how modern data science can be used towards advancing the public good, rather than increasing corporate profits.

This certificate will be available for MPH & MSPH degree candidates in all departments of the Rollins School of Public Health. There are no pre-requisite courses for the Certificate.

Certificate Learning Objectives/Competencies

The certificate in data science has five specific competencies that students who complete the certificate are expected to master.

• Use open-source software to analyze data.
• Apply modern software tools to construct reproducible data science workflows.
• Identify settings where machine learning can be used to inform public health and clinical decision making and apply common machine learning frameworks to data.
• Develop data science products that increase accessibility and interpretability of analytic findings.
• Communicate effectively with public health stakeholders.

Certificate Requirements

This certificate program has 4 required courses (R programming, data science toolkit, machine learning, and a current topics course) which is 8-9 credit hours plus 3-4 hours of elective credits. Students must additionally ensure that their APE and ILEs can be related to data science, as described below. In extenuating circumstances, students may replace the APE and/or ILE requirement with additional elective courses in lieu of these requirements.

Programming:

	Introduction to R Programming for Non-BIOS Students	2	Fall/Spring	None
	R Programming for BIOS Students	2	Spring	None

  Data Science Toolkit:

	Data Science Toolkit	2	Fall	BIOS 544/545 (concurrent enrollment OK)

Machine Learning (Choose One):

	Machine Learning	3	Fall/Spring	BIOS 585 + Multivariate Calculus + Linear Algebra
	Applied Machine Learning in Public Health	2	Fall/Spring	BIOS 500 + (BIOS 544 or BIOS 545)

Current Topics:

	Current Topics in Data Science	2	Spring	None

All certificate students should enroll in DATA 555 in the spring semester of their second year. This course will facilitate the integration of the development of an approved data science product into the students’ existing ILE requirements.

All students should make a good faith effort to complete a data science component as a part of their ILE and enroll in DATA 555. However, if extenuating circumstances preclude a student from identifying an appropriate data science component for their ILE, then an additional 4 credit hours of electives may be completed in lieu of DATA 555.

Applied Practice Experience (APE)

To satisfy the certificate APE requirement, either:       1) A data science-related APE should be completed; or       2) 3 additional credit hours from the list of electives above should be completed

Integrated Learning Experience (ILE)

We will offer a 2 credit Current Topics in Data Science (DATA 555) that students will complete in the spring semester of the second year. This course must be taken in addition to each degree program’s specific ILE requirements. If a project cannot be identified then the student must complete an additional 4 credit hours of electives from the list of acceptable elective courses.

Certificate Declaration of Intent

Each fall semester students will declare their interest in the certificate by submitting a formal Declaration of Intent. The Declaration will ask students to answer specific questions to gauge the student’s interest and desire to complete the Data Science Certificate. 

The Declaration of Intent form is available from August 22, 2024, to August 28, 2024, pm.

Please note: the declaration of intent form requires that you sign in using your Emory.edu email. Students are responsible for enrolling in required and elective courses each semester prior to add/drop/swap ends. Additionally, the department is unable to increase enrollment capacity.

Data Science Certificate Administrators

David Benkeser, PhD Associate Professor, Department of Biostatistics and Bioinformatics   Director, Data Science Certificate RSPH GCR, Room 322 [email protected]

Angela Guinyard Assistant Director of Academic Programs in Biostatistics Coordinator, Data Science Certificate RSPH GCR, R00 270 Office: (404)712-9643 [email protected]

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Statistics Data Science: Ph.D.

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Our Ph.D. in Statistics Data Science program offers you the opportunity to hone your skills in mathematical reasoning, statistical modeling, computation, and methodology development.

Through this new doctoral program, you will gain a thorough understanding of probability and statistics as well as machine learning methods. You’ll apply statistical methods and theory to real-world data challenges in an interdisciplinary manner. This program will expose you to cutting-edge research and developments in statistics, machine learning, artificial intelligence and data sense, preparing you for statistics and data science careers in academia, the public sector and industry.

Jump to:   Admission & Degree Requirements   |  Application Deadlines   |  Research Areas   |  Faculty  

Admission & Degree Requirements

Admission to this program is highly competitive and selective. We require you to submit the following with your application. 

All transcripts from undergraduate and graduate (if applicable) institutions. 

Three letters of recommendation.

Personal statement: Include research interests;do not exceed three pages.

GRE General test score (required but can be waived for students currently enrolled in or have already earned the MS in Statistics degree in UD)

GRE subject test in Mathematics or other STEM fields (optional).

Language scores (for international students whose native language is not English, and who have not received a degree at a U.S. college or university). A score of 100 or higher on the Test of English as a Foreign Language (TOEFL), or equivalently 7.5 or higher on the International English Language Testing System (IELTS). 

A department graduate committee will decide who is admitted to the program in compliance with University policies and procedures. The committee reserves the right to interview the applicants.

Students with an MS degree in Statistics or related fields are eligible for a 4-year accelerated track with a reduced course load. Eligibility is determined by the admission committee.

Degree Requirements

You must have, or expect to have a bachelor’s degree or higher in statistics, mathematics or a related field from an accredited college of university, by the date of admission.

Ready to Apply?

Apply now >, view course and exam requirements >, email the program director >.

* Disclaimer: The customized GPT is an experimental tool designed to provide real-time answers based on the official curriculum and commonly asked questions. GPT-generated answers may not always be accurate. Please verify all information through the official University of Delaware website.

Application deadlines

Regular admission is for each fall semester. Applicants must submit their application via the online link no later than February 1 .

Research Areas

The Statistics faculty within the department engage in a broad range of research topics. Our expertise spans classical statistical problems, such as hypothesis testing, high-dimensional data analysis, dimension reduction, time-series analysis, and nonparametric statistics, as well as contemporary topics, including network modeling, graph learning, neural networks, computational statistics, and optimization. Additionally, our faculty are actively involved in data-driven research applications across diverse fields, such as large language models, image data analysis, financial forecasting, health sciences, biology, and animal science.

The program also offers students the flexibility to pursue research in collaboration with our affiliated faculty or any other University of Delaware faculty whose work is closely aligned with statistics and data science. This interdisciplinary approach provides a unique opportunity for students to tailor their research experience to their academic and professional interests.

Core Faculty  

Dr. Shanshan Ding

Dr. Wei Qian

Dr. Jing Qiu

Dr. Cencheng Shen

Dr. Peng Zhao

Affiliated faculty

Dr. Austin Brockmeier

Dr. Rahmat Beheshiti

Dr. Yin Bao

Dr. Jeff Buler

Dr. Kyle Davis

Dr. Vu Dinh

Dr. Dominique Guillot

Dr. David Hong

Dr. Mokshay Madiman

Dr. Xi Peng

Dr. Guangmo (Amo) Tong

Dr. Xu Yuan

College of Agriculture & Natural Resources

Academic Departments

• Animal & Food Sciences
• Applied Economics & Statistics
• Entomology & Wildlife Ecology
• Plant & Soil Sciences

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College of Graduate Studies

Physical Address: Morrill Hall Room 104

Mailing Address: College of Graduate Studies University of Idaho 875 Perimeter Drive MS 3017 Moscow, ID 83844-3017

Phone: 208-885-2647

Email: [email protected]

Online Master’s and Doctoral Programs List

The University of Idaho’s online graduate degrees reflect our mission of making education accessible across the state and nation and responding to changing employment demands. Delivering the quality instruction expected of a research university, our online master’s and doctoral programs are taught by our esteemed faculty members and build upon our century-old reputation as an educational leader in the Northwest. 

Online graduate students can earn a degree from one of our top-ranked national programs around their existing obligations and have access to the same opportunities and resources, including the on-campus library, research projects, scholarships, and teaching assistantships. If you’ve thought about returning to school but require more flexibility due to your full-time job and raising a family, advance your career with any of the programs listed here.

Available via online or distance learning: 

• Climate Change , P.S.M.
• Geographic Information Skills, Mapping, and Monitoring , P.S.M.
• Precision Nutrition for Human and Animal Health , P.S.M.
• Scientific Communication and Leadership , Certificate
• Sustainable Soil and Land Systems , P.S.M.
• Water Resources , P.S.M.

College of Business and Economics

• Online Master of Business Administration , M.B.A.
• Adult Organizational Learning and Leadership , M.S.
• Educational Leadership , M.Ed.
• Curriculum and Instruction , M.Ed.
• Physical Education , M.Ed.
• Special Education , M.Ed.
• Education , Ed.D., Ph.D. (may vary from all to mostly online)

Engineering

• Advanced Semiconductor Design
• Civil Engineering , M.Engr.
• Computer Engineering , M.S., M.Engr.
• Computer Science , M.S.
• Cybersecurity , M.S.
• Electrical Engineering , M.S., M.Engr.
• Engineering Management , M.Engr.
• Geological Engineering, , M.S.
• High-Speed Circuits and Systems , Certificate
• Mechanical Engineering , M.Engr.
• Power System Protection and Relaying, Certificate
• Secure and Dependable Computing Systems, Certificate
• Smart Grid Cybersecurity
• Technology Management , M.S.

Letters, Arts and Social Sciences

• Criminology , M.S.
• Emerging Media , M.A.
• Music , M.Mus.
• Media Production , Certificate
• Public Administration , M.P.A.
• Psychology: Human Factors , M.S.
• Theatre Arts , MFA
• Strategic Media Management , Certificate

Natural Resources and Environmental Science

• Environmental Science, M.S.
• Environmental Education and Science Communication, Certificate
• Master of Natural Resources, M.N.R.
• Fire Ecology, Management and Technology , Certificate
• Remote Sensing of the Environment, Certificate
• Geographic Information Science: Geospatial Aspects of Sustainable Planning Application , M.S.
• Geographic Information Science: Geospatial Habitat Assessment Application , M.S.
• Geographic Information Science: Geospatial Intelligence Application , M.S.
• Geographic Information Science: Geotechnician Application , M.S.
• Geographic Information Science: GIS Programming Application , M.S.
• Geographic Information Science: Natural Hazards and Emergency Planning Application , M.S.
• Statistical Science , M.S. 
• Teaching Mathematics , M.A.T.

The University of Idaho General Catalog is available online.

Distance Learning Versus Online Graduate Degrees

U of I’s online graduate degrees utilize two distinct formats. Online learning uses pre-recorded, or asynchronous, lectures that students can then review on their own time. Distance learning simulates a real-time virtual classroom. Wherever an online student is located, they have direct access to a traditional classroom environment where they can interact and participate in discussions with their fellow students and instructors.

The Benefits of U of I’s Online Graduate Programs 

Whichever online degree you begin, being an online learning student at U of I comes with a host of benefits.

• Flexibility: We design our online master’s and doctoral programs around the schedules of working adults, allowing you to maintain existing employment, family, and seasonal commitments without altering your schedule or taking a hit in income.
• Quality: We’re one of the region’s most respected research universities. Whether the class is pre-recorded or virtual, our leading faculty members deliver the same depth and detail of instruction that you expect in an on-campus classroom.
• Place-Based Degrees: Especially where research is concerned, our faculty members partner with regional organizations to support our online graduate students as they complete a place-based thesis or non-thesis research project. You, in turn, can conduct guided research relevant to your geographic region without ever setting foot on campus and may even be able to do so through your existing employer.
• Scholarships: Many erroneously believe that scholarships are off the table to online graduate students. Supporting your endeavors and professional growth, the University of Idaho has opened up teaching assistant opportunities and place-based scholarships to online students.
• Affordability: No matter where you are in the country or world, all online courses for master’s and doctoral programs are charged at our in-state rate. 
• Ahead of the Curve: The University of Idaho championed distance learning ahead of everyone else with our innovative Engineering Outreach program. We introduced these degrees over 35 years ago, and while we’ve adjusted delivery methods to available technologies, our commitment toward advancing your career through quality education has not wavered.

Learn More About U of I’s Online Graduate Degrees

Thinking about enrolling in an online graduate degree program? To get your questions answered, reach out to the College of Graduate Studies by email or by phone at 208-885-2647, or request additional information today .

23 Best Public Health schools in Moscow, Russia

Updated: February 29, 2024

• Art & Design
• Computer Science
• Engineering
• Environmental Science
• Liberal Arts & Social Sciences
• Mathematics

Below is a list of best universities in Moscow ranked based on their research performance in Public Health. A graph of 138K citations received by 14.2K academic papers made by 23 universities in Moscow was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. Moscow State University

For Public Health

2. National Research University Higher School of Economics

3. Moscow Medical Academy

4. RUDN University

5. Russian National Research Medical University

6. Russian Presidential Academy of National Economy and Public Administration

7. Finance Academy under the Government of the Russian Federation

8. Plekhanov Russian University of Economics

9. Moscow Institute of Physics and Technology

10. New Economic School

11. N.R.U. Moscow Power Engineering Institute

12. Moscow State Institute of International Relations

13. Moscow State Pedagogical University

14. National Research Nuclear University MEPI

15. Russian State University for the Humanities

16. Bauman Moscow State Technical University

17. State University of Management

18. Moscow State University of Railway Engineering

19. Russian State Agricultural University

20. Russian State Social University

21. Moscow Aviation Institute

22. Moscow Polytech

23. Russian State University of Oil and Gas

Universities for public health near moscow.

	University		City
571		2	Sumy
635		4	Saint Petersburg
636		12	Saint Petersburg
636		14	Saint Petersburg
637		15	Saint Petersburg
682		1	Minsk
719		1	Tartu
719		9	Kazan
721		2	Tartu
757		1	Kyiv

Medicine subfields in Moscow

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2024 Recipients of the Wolfe Fellowship

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The Faculty of Arts is pleased to announce that six PhD candidates have been awarded the 2024 Wolfe Fellowship.

The Wolfe Chair in Scientific and Technological Literacy supports the Wolfe Graduate Fellowship for McGill graduate students in the Faculty of Arts. The Fellowship supports the research of PhD candidates whose thesis work reflects the themes of the Chair, whose mandate is to conduct research, teach, and perform public outreach regarding the intellectual foundations, nature and methods of scientific and technological innovation and to provide support to well-rounded students capable of making constructive contributions to debates surrounding science, technology, and society.

Congratulations to all of this year’s recipients.

Discover the 2024 cohort of Wolfe Fellows

Discover the 2024 Wolfe Fellows

Name	Department:	Thesis subject/title *:
	Communication Studies	“Psychoanalysis for a Blue Humanities.”
	Art History and Communication Studies	“Long Time, First Time: A History of Call-In Radio in the United States and Canada 1945-1975.”
Jay Ritchie	English	Intermedia and the effects of digitality on poetic production, circulation, and reception from 1970 to 2020
	Anthropology	Temporary marriage among disadvantaged women in Iran
	Communication Studies
	School of Information Studies	Technologies to better support the interrelated needs of older adults living alone for physical activity.

* title mentioned where specified on the Wolfe webpage.

Emma Blackett (she/they), is a PhD candidate in Communication Studies whose work is informed by queer/feminist studies, psychoanalytic theory, film studies, and ecocriticism. Her dissertation, “Psychoanalysis for a Blue Humanities”, offers a critique of environmental subjectivity, taking as its premise the failure of public communications about ecological collapse to provoke action adequate to halting it.

Sadie Couture is a PhD candidate in the Department of Art History and Communication Studies at McGill University working at the intersection of media history, sound studies, and science and technology studies. During her tenure as a Wolfe Fellow, she will be working on my dissertation project, entitled “Long Time, First Time: A History of Call-In Radio in the United States and Canada 1945-1975” which focuses on the origins, development, and conventionalization of call-in radio and traces how technologies, policies, economies, and cultural desires impacted the format and pummeled it—imperfectly—into the shape it is today. Calling-in—using a telephone to connect to a radio station and subsequently be broadcast live—is simultaneously a technical process, a feedback system, satisfies the ‘public good’ criterion of many regulatory regimes, offers an additional way to shape an audience, and generates cheap, usable content.

Jay Ritchie, is a PhD candidate in the Department of English. His SSHRC CGS-funded doctoral research examines how poets created what Fluxus artist Dick Higgins called “intermedia” art, where two or more different artistic media are combined to create an artwork both between and beyond the artwork’s component media. Situating the turn towards intermedia in the context of the emergence of digital technology, his research examines the effects of digitality on poetic production, circulation, and reception from 1970 to 2020.

“Apart from providing vital, sustaining support for research and dissertation writing in the final year of my PhD, the Wolfe Fellowship allows me to attend conferences on digital media, the digital humanities, and science and technology more broadly,” says Jay. “The opportunity to share the research I have conducted while supported by the fellowship and to learn from other academics deepens my intellectual engagement with science and technology in the arts.”

Maryam Roosta , is a PhD candidate in the department of Anthropology at McGill University. Her doctoral dissertation is focused on the practice of temporary marriage among disadvantaged women in Iran. In Twelver Shi’a Islam, temporary marriage or mut’ah is a contract lasting anywhere from an hour to 99 years between a man and an unmarried woman. While mut’ah has traditionally been an urban phenomenon, the introduction of internet has reshaped the social arrangements between men and women who intend to contract mut’ah. Maryam’s research shows that to better understand the boundaries between mut’ah and transactional intimate relations is necessary to attend to the ways in which digital technologies such as the internet both enable and constrain women in contracting such relationships. In addition to Wolfe fellowship, her doctoral research is supported by the Fonds de Recherche du Québec - Société et Culture (FRQSC) and Wenner-Gren foundation.

Mehak Sawhney (she/her) is a PhD candidate and Vanier Canada Graduate Scholar in Communication Studies at McGill University. Her doctoral project titled Audible Waters: Sounding and Surveilling the Indian Ocean traces the production of oceanic territory through underwater sonic technologies in postcolonial India and the subcontinental Indian Ocean. Through a focus on hydrography, military security, conservation, and resource extraction, the project explores the politics of underwater monitoring technologies such as sonars as well as scientific disciplines such as underwater acoustics and bioacoustics. In so doing the project offers media theoretical reflections on the idea of the planetary, ongoing submarine colonialisms, and geopolitically situated ways to think about the relationship between sound, media and the environment.

“The Wolfe fellowship will support me in completing my dissertation as a final year PhD candidate at McGill,” says Mehak. “My dissertation titled Audible Waters: Sounding and Surveilling the Indian Ocean focuses on the production of oceanic territory through underwater sonic technologies in postcolonial India and the subcontinental Indian Ocean. It is based on ethnographic and archival research in India and the US. The fellowship will be very helpful in supporting my work and stay for the next academic session as an international student in Canada.”

Muhe Yang is a PhD candidate in the School of Information Studies at McGill University. Her doctoral research investigates how to design technologies to better support the interrelated needs of older adults living alone for physical activity. Older adults engage in physical activity for myriad purposes, including health benefits, associated sensory pleasures, and increased opportunities of socializing. Yet, older adults, especially those living alone, often encounter various barriers to maintaining their exercise routines, contributing to inactivity and falling short of recommended physical activity levels. Those barriers, including health problems, lack of motivation and social support, lack of exercise resources, not only span across individual, social, and environmental levels but also are often interrelated, as revealed in Muhe’s research findings to date.

For more information on the Wolfe Fellows please visit the Wolfe Fellowship homepage . 

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