This is the first version of the 2025–26 General Catalog. Please check back regularly for changes. The final edition and the historical PDF will be published during the fall semester.
Learning Outcomes
Graduates will be able to:
- have a solid understanding of the mathematical and statistical theory that underlies statistical methods;
- conduct literature reviews to summarize the state of the art for specific theoretical and applied topics;
- formulate, implement, and assess appropriate statistical models for analyzing data;
- identify limitations of existing methods and independently develop and assess novel methods (e.g., for analyzing new types of data);
- appreciate the issues of uncertainty, reproducibility, and computability in data analysis;
- collaborate with non-statisticians to help collect and analyze data; and
- acquire effective communication skills for disseminating statistical findings.
The Doctor of Philosophy program in statistics requires a minimum of 76 s.h. of graduate credit, including work completed for the MS.
The Graduate College requires a minimum grade-point average (GPA) of 3.00 to graduate with a PhD; however, the Department of Statistics and Actuarial Science requires a higher GPA of at least 3.40 to earn the PhD in statistics. This includes all courses used to meet degree requirements plus additional courses that are relevant to a student's program.
PhD students complete required coursework, including four courses in one of four concentration areas: actuarial science/financial mathematics, biostatistics, data science, or probability/mathematical statistics (see the section titled "Concentration Areas" for area descriptions and course lists). They may take coursework or seminars in other departments to relate an area of specialization to other fields of knowledge, acquire the ability to use electronic digital computing equipment, or learn non-English language skills necessary for reading scientific journals and communicating with scholars in other languages.
PhD Qualifying Procedure
Statistics
After passing the MS final examination, a student who will choose biostatistics, probability/mathematical statistics, or data science as the selected concentration area can request to go through the PhD qualifying procedure. This request should be made by notifying the director of graduate studies. The qualifying procedure typically takes place in November or April each year. During the PhD qualifying procedure, the faculty evaluates the student's body of work, which includes the MS final examination in statistics, coursework, and evidence for research potential. Usually, a student needs to have demonstrated a high level of proficiency in their MS exam, earned a grade of A in at least one 7000-level course, completed at least 1 s.h. of STAT:6990 Readings in Statistics, and be enrolled in a second semester of STAT:6990. This evaluation and assessment results in one of three decisions: the student successfully passes the PhD qualifying procedure, the student must reapply to go through the PhD qualifying procedure after accumulating a larger body of work for evaluation, or the student fails to pass the PhD qualifying procedure and cannot continue in the PhD program.
In exceptional cases, a student may petition to go through the PhD qualifying procedure early or be admitted to the PhD program directly. However, passing the MS final exam is required before any student can take the PhD comprehensive exam (see the section below on PhD comprehensive exam/prospectus).
A student may be admitted directly to the PhD program. If direct admission is not granted, the student may petition to apply for PhD admission either after their MS examination or concurrently with the PhD qualifying procedure.
In exceptional cases, a student may petition to go through the PhD qualifying procedure early in their first year. However, passing the MS final exam is required before any student can go through the PhD qualifying procedure and take the PhD comprehensive exam.
Actuarial Science
After successfully passing the MS final examination in actuarial science (in exceptional cases, a student may petition to go through the PhD qualifying procedure early), a student who will choose actuarial science/financial mathematics as the selected concentration area, can request, by notifying the director of graduate studies, to go through the PhD qualifying procedure. Upon this request, the faculty evaluates the student's body of work and assesses the student's potential for research. The body of work will include the MS final examination in actuarial science, professional examinations passed, and coursework. This evaluation and assessment results in one of two decisions: the student is officially admitted into the PhD program in the actuarial science/financial mathematics concentration area, or the student is not admitted into the PhD program.
Students complete the program by passing the PhD final (comprehensive) examination and writing and defending a dissertation. Students usually complete the program three years after earning the MS.
A plan of study that does not conform to the requirements described as follows but is of high quality may be approved by the director of graduate studies.
Required Courses
The following course lists outline required courses for each concentration area.
Actuarial Science/Financial Mathematics Concentration Area
Actuarial science/financial mathematics emphasizes the theory of actuarial science, finance, and risk management. It is excellent preparation for academic positions in universities that offer actuarial science programs and for positions in the insurance, pension, and financial industries.
| Course # | Title | Hours |
|---|---|---|
| All of these from the MS in actuarial science program: | ||
| ACTS:4130 | Quantitative Methods for Actuaries | 3 |
| ACTS:4180 | Life Contingencies I | 3 |
| ACTS:4280 | Life Contingencies II | 3 |
| STAT:5100 | Statistical Inference I | 3 |
| STAT:5101 | Statistical Inference II | 3 |
| STAT:6300 | Probability and Stochastic Processes I | 3 |
| All of these: | ||
| STAT:5120 | Mathematical Methods for Statistics | 3 |
| STAT:6200 | Predictive Analytics | 3 |
| STAT:7100 | Advanced Inference I | 3 |
| STAT:7200 | Linear Models | 4 |
| STAT:7300 | Advanced Probability | 3 |
| STAT:7400/DATA:7400/IGPI:7400 | Computer Intensive Statistics | 3 |
| STAT:7990 | Reading Research | 22 |
| DATA:7350 | High-Dimensional Probability for Data Science | 3 |
| Seminars chosen from STAT:7190, STAT:7290, and STAT:7390 | 2 | |
| At least four of these, with at least one numbered 7000 or above: | ||
| ACTS:6200/DATA:6200/STAT:6200 | Predictive Analytics | 3 |
| ACTS:7730 | Advanced Topics in Actuarial Science/Financial Mathematics | 3 |
| FIN:7110 | Finance Theory I | 3 |
| FIN:7130 | Finance Theory II | 3 |
| STAT:4560 | Statistics for Risk Modeling I | 3 |
| STAT:4561 | Statistics for Risk Modeling II | 3 |
| STAT:6301 | Probability and Stochastic Processes II | 3 |
| STAT:7560 | Time Series Analysis | 3 |
Biostatistics Concentration Area
Biostatistics emphasizes exposure to various biostatistical methods, such as survival analysis, categorical data analysis, and longitudinal data analysis. It prepares students for consulting and other positions in industry.
| Course # | Title | Hours |
|---|---|---|
| All of these from the MS in statistics program: | ||
| STAT:5090 | ALPHA Seminar | 1 |
| STAT:5100 | Statistical Inference I | 3 |
| STAT:5101 | Statistical Inference II | 3 |
| STAT:5200/IGPI:5199 | Applied Statistics I | 4 |
| STAT:5201 | Applied Statistics II | 3 |
| STAT:5400/DATA:5400/IGPI:5400 | Computing in Statistics | 3 |
| STAT:6220/DATA:6220 | Consulting and Communication With Data | 3 |
| STAT:6300 | Probability and Stochastic Processes I | 3 |
| STAT:6990 | Readings in Statistics (two consecutive enrollments) | 2 |
| All of these: | ||
| STAT:5120 | Mathematical Methods for Statistics | 3 |
| STAT:7100 | Advanced Inference I | 3 |
| STAT:7101 | Advanced Inference II | 3 |
| STAT:7200 | Linear Models | 4 |
| STAT:7300 | Advanced Probability | 3 |
| STAT:7400/DATA:7400/IGPI:7400 | Computer Intensive Statistics | 3 |
| STAT:7990 | Reading Research | 18 |
| Seminars chosen from STAT:7190, STAT:7290, and STAT:7390 | 2 | |
| At least four of these, with at least one numbered 7000 or above: | ||
| STAT:6530/IGPI:6530 | Environmental and Spatial Statistics | 3 |
| STAT:7510/BIOS:7410 | Analysis of Categorical Data | 3 |
| STAT:7570/BIOS:7210/IGPI:7210 | Survival Data Analysis | 3 |
| BIOS:6650/EPID:6655/IGPI:6650 | Causal Inference | 3 |
| BIOS:6720 | Statistical Machine Learning for Biomedical and Public Health Data | 3 |
| BIOS:7240 | High-Dimensional Data Analysis | 3 |
| BIOS:7310/IGPI:7310 | Longitudinal Data Analysis | 3 |
| DATA:7350 | High-Dimensional Probability for Data Science | 3 |
Data Science Concentration Area
The data science track emphasizes the theory, methodology, and application of techniques for working with and learning from data. This concentration area prepares students to develop new methods for visualizing and modeling data, managing reproducible data analysis workflows, and collaborating with scientists and other data stakeholders. It is excellent preparation for students interested in academic, industrial, or government positions that involve data visualization, modeling, and analysis.
| Course # | Title | Hours |
|---|---|---|
| All of these from the MS in statistics program: | ||
| STAT:5090 | ALPHA Seminar | 1 |
| STAT:5100 | Statistical Inference I | 3 |
| STAT:5101 | Statistical Inference II | 3 |
| STAT:5200/IGPI:5199 | Applied Statistics I | 4 |
| STAT:5201 | Applied Statistics II | 3 |
| STAT:5400/DATA:5400/IGPI:5400 | Computing in Statistics | 3 |
| STAT:6220/DATA:6220 | Consulting and Communication With Data | 3 |
| STAT:6300 | Probability and Stochastic Processes I | 3 |
| STAT:6990 | Readings in Statistics (two consecutive enrollments) | 2 |
| All of these: | ||
| STAT:4540/BAIS:4540/DATA:4540/IGPI:4540 | Statistical Learning | 3 |
| STAT:4580/DATA:4580/IGPI:4580 | Data Visualization and Data Technologies | 3 |
| STAT:5120 | Mathematical Methods for Statistics | 3 |
| STAT:7100 | Advanced Inference I | 3 |
| STAT:7200 | Linear Models | 4 |
| STAT:7400/DATA:7400/IGPI:7400 | Computer Intensive Statistics | 3 |
| STAT:7500/BAIS:7500 | Statistical Machine Learning | 3 |
| STAT:7990 | Reading Research | 18 |
| DATA:7350 | High-Dimensional Probability for Data Science | 3 |
| Seminars chosen from STAT:7190, STAT:7290, and STAT:7390 | 2 | |
| At least two of these, with at least one numbered 7000 or above: | ||
| STAT:4750/DATA:4750 | Probabilistic Statistical Learning | 3 |
| STAT:6200/ACTS:6200/DATA:6200 | Predictive Analytics | 3 |
| STAT:6530/IGPI:6530 | Environmental and Spatial Statistics | 3 |
| STAT:6560 | Applied Time Series Analysis | 3 |
| STAT:6970 | Topics in Statistics | 3 |
| STAT:7101 | Advanced Inference II | 3 |
| STAT:7300 | Advanced Probability | 3 |
| STAT:7510/BIOS:7410 | Analysis of Categorical Data | 3 |
| STAT:7520 | Bayesian Analysis | 3 |
| STAT:7560 | Time Series Analysis | 3 |
Probability/Mathematical Statistics Concentration Area
Probability/mathematical statistics emphasizes a broad, solid foundation in techniques and underpinnings of mathematical statistics. Its focus on breadth and depth is intended to produce well-rounded, knowledgeable scholars. It is excellent preparation for academic positions in mathematical statistics and industrial or government positions that require broadly trained statisticians with a strong understanding of statistical theory.
| Course # | Title | Hours |
|---|---|---|
| All of these from the MS in statistics program: | ||
| STAT:5090 | ALPHA Seminar | 1 |
| STAT:5100 | Statistical Inference I | 3 |
| STAT:5101 | Statistical Inference II | 3 |
| STAT:5200/IGPI:5199 | Applied Statistics I | 4 |
| STAT:5201 | Applied Statistics II | 3 |
| STAT:5400/DATA:5400/IGPI:5400 | Computing in Statistics | 3 |
| STAT:6220/DATA:6220 | Consulting and Communication With Data | 3 |
| STAT:6300 | Probability and Stochastic Processes I | 3 |
| STAT:6990 | Readings in Statistics (two consecutive enrollments) | 2 |
| All of these: | ||
| STAT:5120 | Mathematical Methods for Statistics | 3 |
| STAT:7100 | Advanced Inference I | 3 |
| STAT:7101 | Advanced Inference II | 3 |
| STAT:7200 | Linear Models | 4 |
| STAT:7300 | Advanced Probability | 3 |
| STAT:7400/DATA:7400/IGPI:7400 | Computer Intensive Statistics | 3 |
| STAT:7990 | Reading Research | 18 |
| Seminars chosen from STAT:7190, STAT:7290, and STAT:7390 | 2 | |
| At least four of these, with at least one numbered 7000 or above: | ||
| STAT:6301 | Probability and Stochastic Processes II | 3 |
| STAT:7500/BAIS:7500 | Statistical Machine Learning | 3 |
| STAT:7520 | Bayesian Analysis | 3 |
| STAT:7560 | Time Series Analysis | 3 |
| BIOS:6650/EPID:6655/IGPI:6650 | Causal Inference | 3 |
| BIOS:7240 | High-Dimensional Data Analysis | 3 |
| DATA:7350 | High-Dimensional Probability for Data Science | 3 |
Committee
After admission to the PhD program and before taking the PhD comprehensive exam, the candidate chooses a committee of at least five members, which is approved by the advisor. At least three of the faculty members must be University of Iowa tenure-track faculty members. At least two of the faculty members must be from the major department (defined as faculty members who hold any appointment in the major department), and University of Iowa tenure-track faculty members.
The department may request the Graduate College dean's permission to replace one of the five committee members with a recognized scholar of professorial rank from another academic institution.
PhD Comprehensive Exam (Prospectus)
After passing the MS final exam and within 12 months of passing the PhD qualifying procedure, the candidate should present to the committee a written and oral prospectus, which serves as the PhD comprehensive exam. The prospectus describes the problems the student is considering for the thesis, an extensive review of relevant background materials, open problems of interest and ideas for solving problems, and any preliminary results. Failure to successfully complete the prospectus within 18 months of admittance to the PhD program will jeopardize the continuation of a student's financial support.
Each PhD committee member will sign the examination report as satisfactory, reservations, or unsatisfactory. A vote of "Reservations" should only be used when a faculty member feels that the deficiencies displayed by the student were modest and can be readily rectified. In the event of a report with two or more votes of "Reservations," the committee's requirements of the student to correct the deficiencies must be recorded and submitted to the Graduate College with the examination report form. The statement must specify the time allotted for completion of the aforementioned actions. For example, if additional coursework is required, a list of suitable courses must be presented. If the candidate must rewrite their research prospectus, the deficient areas must be identified. If the candidate satisfies the required actions within the specified period of time, the comprehensive examination will be recorded as "Satisfactory" as of that date. If the actions are not satisfied on time, or if the actions are not of sufficient quality, the comprehensive examination will be recorded as "Unsatisfactory" as of that date. The candidate will not be admitted to the PhD final examination of the dissertation until a grade of "Satisfactory" has been recorded for the comprehensive exam.
In the case of a report of unsatisfactory on a comprehensive examination, the committee may grant the candidate permission to attempt a reexamination no sooner than four months after the first examination. The examination may be repeated only once, at the option of the department.
PhD Final Exam (Dissertation Defense)
Students should plan to defend their dissertation within 24–36 months of passing the PhD qualifying procedure. Failure to successfully defend the dissertation within 48 months of passing the PhD qualifying procedure or within five years of starting the graduate program at the University of Iowa, whichever comes first, will jeopardize the continuation of a student's financial support.
Applicants must meet the admission requirements of the Graduate College; see the Manual of Rules and Regulations on the Graduate College website.
Statistics and probability are vital to many fields, so the demand for well-trained statisticians is strong. Statisticians work in medicine, engineering, law, public policy making, marketing, manufacturing, engineering, agriculture, varied social and natural sciences, and numerous other areas.
The program prepares students for careers in research, applications, and teaching. To learn more about job opportunities, see Your Career on the American Statistical Association website.
The Pomerantz Career Center offers multiple resources to help students find internships and jobs.
Sample Plan of Study
Sample plans represent one way to complete a program of study. Actual course selection and sequence will vary and should be discussed with an academic advisor. For additional sample plans, see MyUI.
Statistics, PhD
Data Science Concentration
This sample plan is currently being reviewed and will be added at a later date.