Be at the forefront of data advancements

Engage in cutting-edge research, develop innovative solutions to real-world challenges, and receive dedicated mentorship from faculty as you navigate the dynamic fields of statistics, actuarial science, and data science.

Explore graduate programs

MS in Actuarial Science

The program in actuarial science prepares students for actuarial careers by emphasizing the theory that underlies risk processes and the application of this theory to practical problems of insurance pricing and management.

MS in Data Science

The program in data science prepare​s​ students for careers that involve data visualization and modeling, managing reproducible data analysis workflows, and collaborating and communicating with scientists and other data stakeholders.

MS in Statistics

The program statistics prepares students for careers in many fields as professional statisticians or for entry into a PhD program.

PhD in Statistics

The doctoral program in statistics prepares students for careers in research, applications, and teaching.

Graduate Minor in Statistics

The graduate 'minor' examination in statistics is offered to graduate students enrolled in non-departmental programs which require proficiency in the area of statistics.

Why statistics and actuarial science?

Immerse yourself in advanced studies at the University of Iowa's Department of Statistics and Actuarial Science, where graduate students have the opportunity to pursue specialized tracks in statistics, actuarial science, and data science.

Our comprehensive curriculum combines coursework with hands-on experiential learning and research, preparing you for diverse career paths in industry, academia, government, and beyond. Benefit from the expertise of our esteemed faculty members, who are leaders in their fields and dedicated mentors committed to guiding you toward academic and professional success.

Whether you aspire to become a data scientist, actuary, statistician, or pursue a career in research, our graduate programs provide the training, support, and resources you need to thrive in today's data-driven world.

Frequently asked questions about our graduate programs

To learn more about the MS graduate program in Statistics please go to this web page.

To learn more about the PhD graduate program in Statistics please go to this web page.

To learn more about the graduate program in actuarial science please go to this web page.

To learn more about the MS graduate program in data science please go to this web page.

We do not send materials by postal mail, since all of the needed information and required forms are available online. Decisions for graduate financial support are typically made by April 15 for the upcoming academic year, so completed applications from students requesting financial support should reach us by Jan. 15, 2024.

Academic requirements for actuarial science

Students who apply to our MS program should have 3 semesters of calculus, a semester of linear algebra, at least one (preferably two) semesters of mathematical statistics (i.e. statistics with a calculus prerequisite).

For students who know that they eventually want a PhD, it is advisable, to state this in your personal statement.

Academic requirements for data science

Students who apply to our MS program should have 3 semesters of calculus, a semester of linear algebra, and a semester of computer programming.

Academic requirements for statistics

Students who apply to our MS program should have 3 semesters of calculus, a semester of linear algebra, at least one (preferably two) semesters of mathematical statistics (i.e. statistics with a calculus prerequisite), a semester of applied statistics or linear regression analysis, and a semester of computer programming.

For students who know that they eventually want a PhD, it is advisable, though not strictly required, that they have a semester of real analysis.

Please note: All applicants are considered for financial support and graduate assistantships include: teaching assistants (TA), research assistants (RA), and graders. No additional form is necessary.

If you are interested in financial support your application must be complete on Jan. 15, 2024 and include all 3 letters of recommendation, the statement of purpose, unofficial GRE scores, and unofficial transcripts from all previous educational colleges and universities. Further, TOEFL is required if you seek support and English is not your first language.  

Regarding TOEFL

For students seeking financial support and English is not a first language, TOEFL is required and a score of at least 105 is desirable. For students not seeking financial support and English is not a first language, we require either a bachelor's degree (or higher) at an English-speaking institute or a TOEFL score of at least 85. We also would like a CV or RESUME.

MS in Data Science Application

  • Fall Semester–June 15 (April 15 for international students)  
  • Spring Semester—Not offered
  • Summer Session—Not offered

MS in Actuarial Science Application

  • Fall Semester–June 15 (April 15 for international students)  
  • Spring Semester—Dec. 1 (Oct. 1 for international students)
  • Summer Session—Not offered

MS or PhD in Statistics Application

  • Fall Semester—Jan. 15 (Applications after this date MAY be considered is positions are available, however, contact the department to get permission for us to accept a late application)
  • Spring Semester—Not offered
  • Summer Session—Not offered

We strongly discourage applications to begin the program in the spring and rarely admit students who submit applications. The reasons are that (1) courses that are offered only in the fall semester are often prerequisites for spring semester courses; (2) funding is rarely available in the spring for new students; and (3) class space is limited in the spring.

Typically, students who wish to pursue a PhD in Statistics will be admitted initially to the MS program. After passing the PhD candidacy review, the student will be formally admitted to the PhD program. However if you have a MS in Statistics some students may be directly admitted into the PhD program.

The MS and the PhD in Statistics are awarded by the Graduate College. Instruction is offered through the College of Liberal Arts and Sciences. The MS is offered without a thesis. 

Please note the minimum requirements for graduate program:

  • A U.S. bachelor's degree from a regionally accredited college or University, or an equivalent degree from another country as determined by the Office of Admissions.
  • A minimum grade-point average (GPA) of 3.00, or foreign equivalent as determined by the Office of Admissions, on the completed undergraduate degree or on at least 12 hours of a graduate degree.
  • The GRE is required; however, there is no set minimum GRE score. The subject test is not required. Your GRE scores must be reported directly from the testing agency. Those dated within the last five years are acceptable. The University of Iowa institution code is 6681 (you do not need department codes). Please not our department does not accept GMAT scores.  We may request an interview (by telephone, Skype, or in person) prior to making an admission decision.

Yes–please see the requirements of Graduate College on English proficiency and a waiver on the Graduate College's website. In addition, for international applicants that do not meet the requirements for the waiver, a minimum TOEFL score of 85 is required on the internet-based test for the MS and PhD programs in statistics, and for the MS program in actuarial science. (If you are interested in financial support, you are encouraged to have a TOEFL score of at least 105.) 

Your TOEFL scores must be reported directly from the testing agency. Those dated within the last two years are acceptable. The University of Iowa's institutional code is 6681 (you do not need departmental codes). Students whose TOEFL scores are below 600 (250 on the computer-based test; 100 on the internet-based test) will be required to sit for an English evaluation upon arrival in Iowa City. 

The Graduate College will require these students to take and pass recommended course work in English usage. This requirement is waived for applicants who have completed a bachelor's degree (or higher) at an accredited university in the United States, the United Kingdom, Canada (excluding French-speaking Quebec), English-speaking Africa, Australia, or New Zealand.

Please note regarding TOEFL if not seeking financial support: This requirement is waived for applicants who have completed a bachelor's degree (or higher) at an accredited university in the United States, the United Kingdom, Canada (excluding French-speaking Quebec), English-speaking Africa, Australia, or New Zealand.  

On the official application it will state that TOEFL is required, however, this is not waived until the University of Iowa has received notice of your degree (or degrees) which can happen much later (after you graduate). So we just say DO NOT WORRY about it and that this will be waived at a later date.

TOEFL scores in general are only valid for 2 years after the test date. So if you took the exam on April 2, 2020 the scores will only be valid until April 2, 2024, at which point ETS will not send out official scores for that particular test. As long as the official scores are sent from ETS prior to invalidating, we will accept them.

Applicants from India with the 3-year bachelor's degree must complete at least the first year of a master's program from an institution recognized by the Association of Indian Universities in order to meet our minimum eligibility requirement of a 4-year U.S. undergraduate degree equivalent.

Application volumes can vary substantially from year-to-year. For fall 2020 we received 140 statistics and 36 actuarial science applications. Incoming cohorts are generally 8-15 students in each program. The number of students admitted usually depends on the availability of funding to support the students.

Currently, most of the students admitted to our program have some form of funding. Most receive teaching or research assistantships. Some have external (i.e. non-University of Iowa) funding. A very small number of students receive fellowships from the University of Iowa's Graduate College. Research assistantships are primarily reserved for our PhD candidates. Funding is very competitive, many very well qualified and deserving students will not be granted support. Our needs are greater for students who have a background in statistics.

All students for whom English is not a first language and who have first-time appointments as graduate teaching assistants (TAs) are required to take a test to assess their effectiveness in speaking English before they are assigned assistantship responsibilities. The English Speaking Proficiency Assessment (ESPA) is a test that assesses students' oral language and listening skills. To take the test, students must be enrolled in a graduate program at the University of Iowa, and an academic department must be considering hiring them as teaching assistants. Details will be given to all students.

All students to be tested must first register for the ESPA test through their departments. When a department offers (or is thinking about offering) a teaching assistantship to a student who has never before been a TA at the university, the department sends a Request for Evaluation form to the ESL Programs Office. The ESL Office sends the student (via the department) a letter indicating the time and place of the appropriate ESPA testing.

A fellowship generally includes a 12-month stipend and a full tuition scholarship, renewable for four or five years (provided that the student is making adequate progress in the program). Usually, the first and last year of a fellowship are free from assisting faculty with research and teaching (while the middle years require that the student work with faculty on assignments related to teaching or research). In contrast, teaching and research assistantships are generally 9-month appointments, carrying a stipend and full tuition scholarship. Students who receive teaching or research assistantships are expected to work an average of 10 (if 1/4 -time) or 20 (if 1/2-time) hours per week on assignments related to teaching or research.

Yes. Students who are not U.S. citizens are eligible for teaching assistantships, and in most cases, research assistantships. Note, however, that students who receive teaching assistantships must be certified by the English as a Second Language Program.

We do not have a required minimum for the GRE scores. Of the 300 students who applied to our program for the 2018-19 academic year they had an average GRE: 151 Verbal, 167 Quantitative score.  

We read the entire application file. Consequently, what a student may perceive as a deficiency in one component of the application (say, a low quantitative GRE score) can be compensated with exceptional strengths in others (say high grades in quantitative college courses like mathematics that are reflected on a transcript). If a student feels that their GRE scores do not accurately reflect their ability, then it is a good idea for the student to explain that in their statement of purpose (pointing to other evidence of their ability in other application materials).

Your GRE scores are no longer valid after five years. Thus, if your scores are five years or older, you must re-take the GRE.

No. We must have an official report sent to us by ETS (the organization that administers the GRE). We must have the official report by the application deadline to ensure that your application receives full consideration.

You may take the GRE as many times as you would like. We will consider all of your scores as an aggregate. That is, we will examine the range, the median, and the trends in the scores. Note that taking the GRE multiple times and scoring higher on one attempt than others does not necessarily mean that we will consider only that highest attempt.

The estimated costs can be better reviewed on this website.

In 2023-24, a 1/4 time appointment (10 hours of teaching or grading) receives a full-tuition scholarship and $10,680 stipend for the school year. A 1/2 time appointment (20 hours of teaching or grading) receives a full-tuition scholarship and $21,360 stipend for the school year. Students holding an assistantship (quarter-time or more) are classified as residents for fee purposes for the terms during which their appointments are held and any adjacent Summer Sessions in which they are enrolled. They are not assessed a technology fee, but will be responsible for miscellaneous fees assessed each semester (student health, student activities fee, building fee, student union fee, arts and cultural fees, recreation fee, etc., which totals approximately $475 per semester).

More information can be found on the Graduate College website.

Yes, we do. You just need to enter your recommender email address and the admissions office will send a request on your behalf.

Please contact us by e-mail at or by telephone at 319-335-2082, ask to speak to Margie.

Creative component presentation titles and student, by year

  • 2024

    Payel Ghosal, Online Bayesian Variable Selection in Logistic Regression for High-dimensional Streaming Data
    Lyle Paukner, An Exploration of Tensor-on-tensor Regression
    Emillia Thedens, Envelope Model Based Calibration
    Ying Xiang, Analysis of Elevated Machine Learning Model Accuracy Due to Neglecting Batch Effects in Data Splitting
    Jinwei Yao, Transfer Learning based on Multi-outputs Stochastic Kriging
    Yiran Zeng, Transfer Learning based on Multi-outputs Stochastic Kriging

  • 2023

    Sam Dannels, Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data, Advisor: Kung-Sik Chan
    Shamriddaha De, Robust Bayesian Variable Selection Using a Hyperbolic Error Model, Advisor: Joyee Ghosh
    Zhenhan Fang, Density Estimation using Nonlinear Independent Components Estimation, Advisor: Jian Huang and Aixin Tan
    Chenyang Li
    Ian Lundy, A Comparison of Frequentist and Bayesian Model Selection Methods, Advisor: Luke Tierney
    Alfonso Martinez, An EM Algorithm for Beta Item Factor Analysis, Advisor: Joseph Lang
    (Anh) Phuong Anh Nguyen, Selecting Markov Chain Monte Carlo Algorithms with Kernel Stein Discrepancy, Advisor: Aixin Tan
    Collin Nill, ZIEL: A Simple Yet Powerful Approach to Zero Inflated Data, Advisor: Boxiang Wang
    Sreya Sarker,  Tree-Based Regression for International Classification of Diseases, Advisor: Sanvesh Srivastava
    Nathan Tansey, Optimal Acoustics, Advisors: Matt Bognar and James Traer (Department of Psychological and Brain Sciences)
    James Thomas, Cancer Pathology Classification with Radiomic Imaging Covariates, Advisor: Sanvesh Srivastava
    Yilin Wang, Online Statistical Inference and Conformal Prediction with Nonstationary Streaming Data, Advisor: Lan Luo

  • 2022

    Qian Tang,  A Fast Algorithm for Kernel Quantile Regressio
    Justin Kelana, A Comparison of Likelihood Free Inference Methods of the Approximate Bayesian Computation Methods
    Ian Hultman, Variational Autoencoders for International Classification of Diseases Codes

  • 2021

    Tyler Dennis, Can an Empty March Madness Bracket Give Enough Information to Predict a Bracket?
    Jessie Lu, Solving a Two-Stage Stochastic Linear Program with Complete Recourse with Varying Distributions of Random Variables
    Yujing Lu, Fast Prediction of the Frequency of Tropical Cyclones
    Yue Pan, The Semi-analytic Method in Lasso, 
    Tyler (William) Reynolds, "All Things to All People”: Modeling Attitudes to Social and Moral Issues in America and Western Europe Using Religious Attitudes
    Max Sampson, Comparing Doubly Robust Estimators with Other Estimators in Causal Inference and a Brief Check of a Proposed Missing-at-Random Data Estimator
    Yikai Zhang, Generalized Tic For Large-Margin Classifiers Strategies Using Spacial Analysis
    Haiyang Zhu, Reparameterization of Hierarchical Model Using Rating Dataset

  • 2020

    Brady Anderson, "Game, Set, Stat: A Comparison of Model Performance Using a Variety of Tennis Metrics"
    Brad Dougherty, "A Custom Package for STAT:6560: UIAppliedTS"
    Alexander Liebrecht, "Feedforward Neural Network Exploration on Ames Housing Data Tuesday"
    Xingzhi Wang, "Tensor, a natural representation of multi-dimensional and high-order data"
    Zhen Wang, "A Composite Topic Model: HMM-LDA and Its Applications in Language Learning."
    Fei Wu, "Bayesian Models for Multiclass Classification"
    Zongyi Xu, "Gaussian Process Regression Using Tree Kernel as Covariance Function"

  • 2019

    Willam Chew, "Comfort of Home: Components of the Home Court/Field Advantage."
    Ben Jacobs, "N-Gram Modeling in R."
    Jiayue Li, "Comparing the classification methods using the breast cancer cell dataset."
    Chris Penney, "CPU Parallel Processing and Rcpp Optimization in R."
    Chunlei (Jonah) Wang, "Simple and Scalable Posterior Interval Estimation."
    Ling Zhang, "K-Logistic Clustering."
    Xingyu Zhou, "Dual Regression with LASSO penalty." 

  • 2018

    Tim Ambrose, "Confidence Interval Coverage for Small Sample Sizes."
    Yoon Joo Cho, "Identifying Political Party from a Candidate's Speech Patterns: Naive Bayes versus Logistic Regression."
    Carter Huggins, "Modeling Incurred Loss for Trucking Accidents in the United States."
    Tylar Jia, "Bayesian Variable Selection with Stan."
    Seung Wook Kim, "Correlation Matrices with the Positive Dominant Eigenvector and PCA Portfolios."
    Ben Lim, "Exchangeable Copulas with no Large-d Consistent Estimators: Investigating the Multivariate-t".
    Rebecca Rachan, "Comparison of Classification Methods in 2-dimensional Predictor Space."
    Alex Richter, "An Overview of Mark-Recapture Methods of Population Estimation.”
    Ruida Song, "Analysis of Time Series for Soil Moisture with Dynamic Linear Model."
    Liyang Sun, "Penalized Regression Model for Life Expectancy of Geriatric Patients".
    Chuyi Wang, “Pseudo Marginal Approach for Metropolis Hastings in Smooth Transition Auto-Regressive Model."
    Qing Xie, "Investigation of Sequential Maximum Likelihood Estimation in Multistage Testing Design."

  • 2017

    Shiyang Chen, "Flexible Canonical Correlation Analysis"
    Andrea Harlan, "An Investigation and Application of Random Forests"  
    Baosheng He, "A Bayesian SPC Method for Count Data in the Presence of Outliers"
    Elizabeth Held, "Dimensionality Reduction Methods and Classification: An Empirical Study"
    Samuel Justice, "Some General Methods for Analyzing the COM-Poisson Distribution"
    Xun Li, "Forecasting of North Atlantic Tropical Cyclone Activity with Bayesian Model Averaging"  
    Yiheng Liu, "New performance measure for PD models" 
    Tyler Olson, "Examining the Biased and Unstable Behavior of Satterthwaite Degrees of Freedom"  
    Huan Qin, "Apply Multi-Output Support Vector Regression to Time Series Prediction"   
    Jin Rui, "Non-negative least squares for high-dimensional linear models"  
    Jun Tang, "A Pairwise Dependence Measure of Spatial Extremes by the Peaks Over Threshold Approach"
    Alex Zajichek, "Modeling the probability of an NHL goal for player-placement strategy: A Naive (Bayes') approach"
    Hongda Zhang, “A New Linear Regression Method with High-Breakdown Point”   
    Yue Zhao, "Sparse Linear Modeling of RNA Sequencing Data for Isoform Discovery"
    Qiansheng Zhu, "Solving convex relaxation to combinatorial 2-SUM problem”

  • 2016

    Emily Eck: “A Statistical Analysis of Baseball Spray Charts”
    Can Guo: “Comparison of Time Series ARIMA, random forest and boosted tree models”
    Rui Huang:  “Concavely Penalized Clustering”
    Yunju Im: "Estimating the number of components in Gaussian mixture models through a concave splitting method"
    Haoming Li: “Detection of Region with Temporal-Spatial Change of Vegetation Cover”
    Cunxian Ma: "Improved exact confidence intervals for a binomial proportion"
    Steve Manning: "A Comparison of Several Techniques for Longitudinal Data Analysis with Missing Observations"
    Jin Meng: “Separating Jump Variation from Integrated Volatility via Wavelet Analysis, with Applications to Assessing the Seasonal Fluctuations in Nitrate Concentration in River Water in Iowa”
    Munir Nayak: "Modelling number of winter extreme precipitation events over the central United States"
    Giang Nguyen: “Simulations Using Markov Chain Monte Carlo Methods”
    Reid Ronnander: "Statistical Applications in Music: Description and Classification"
    Tao Siyang: “The Exit Probabilities of a Renewal Risk Model”
    Tom Thanh: “Latent Dirichlet Allocation on Reddit Technology Posts”
    Wenda Tu: “Is Uranium a Good Surrogate for Radon in Predicting Lung Cancer: A Study on Bayesian Spatial Generalized Linear Model”
    Songtao Wan: “A Modification On No-Arbitrage Pricing Model Of Cat Bonds”
    Tyler Zemla: "An Analysis of Classification Tree-Determined Strike Zones in Major League Baseball"

  • 2015

    Allie Bishop: “Baseball Simulation: An Insight into the Cleveland Indians”
    Yaqin Deng: “A simulation and Comparison of Parametric and Nonparametric Bootstrap Methods on Time Series Data”
    Yuxing Hou: “Sequential Monte Carlo Methods and some of their Applications”
    Dirk Hugen: “Replication of Ang and Bekaert’s:  How Regimes Affect Asset Allocation”
    Zhijiang (Van) Liu: “Spatial AR Model on Household Dental Expenditure Based on Alternative Distance”
    Michael Mitsche: “The Elephant of Big Data”
    Anna Pritchard: “An Investigation of Dichotomous-Response Classification Methods”
    Jinbo Qi: “Pathwise Expectation-Maximization Algorithm” 
    Charlie Rowe: “Tony Gwynn's Unfinished Quest to Bat .400”
    Bo Wang: “An Application of the Composite Bridge Penalized Regression Algorithm to Linear Regression with Structural Changes”
    Sheng Wang: “Estimating Brood-specific Reproductive Inhibition Potency In Aquatic Toxicity Testing when A Toxicant Affects Both fecundity and Survival Of Organisms”
    Fuli Zhang: “Outlier Detection in Time Series via Penalized Likelihood Method”

  • 2014

    Yaqing Duan, "Data Analysis using Regression Trees"
    Chao Fan, "An Data-Augmentation Approach to Approximate Maximum Likelihood Function"
    Weichen Gao, "Explore the Robustness of the Median Probability Model to Model Misspecification"
    Riad Jarjour, "Pairing Time-Series for Treatment/Control Selection"
    Stephanie Kommes, "A Paper Review: Hidden Markov Models for Microarray Time Course Data in Multiple Biological Conditions"
    Stephanie Kriewall, "Efficiency of MCMC Sampling from a Multilevel Probit Regression on Polling Data"
    Andrew Meng, "Optimal Sampling Designs for Finite Population Block Kriging in Two-Dimensions and Stream Networks"
    Menghan Li, "Comparison of Variable Selection Methods for High Correlation Data"
    Samuel Lifton, "False Discovery Rates Under Conditions of Dependence"
    Jessica Orth, "User Perceptions of Sound in Simple Linear Regression Diagnostics"
    Owen Robinson, "Predicting County-Level Results in the 2012 Presidential Election: Is the South Different"
    Li Zhang, "Local Polynomial Estimation for Time Series Data"
    Yichi Zhang, "A Comparison Between Regression Estimator and Horvitz–Thompson Estimator using Faculty Survey Data-set"

  • 2013

    Sarah Bell, "Identification of Risk Factors for Targeted Screening of Depressive Symptoms"
    Elizabeth Brokken, "Bayesian Model Comparison for Binary Time Series: Interesting Simulation and Possible Explanation"
    Sean Callister, "Decisions for Implementing Factor Analysis Explored through an Empirical Data Set"
    Juan Cervantes, "Application of the SIMEX Algorithm on Spatial Point Patterns"
    Vikas Choudhary, "Comparison of the Lasso and Minimax Concave Penalty Approaches to Fitting Models"
    Andrew Ghattas, "Comparing Priors for Bayesian variable Selection"
    Chandler Minner, "A review of robust parameter design illustrated by an experiment with paper helicopters"
    Seong Hee Ryu, "Simulation study on the bounds for the bias of the empirical CTE"
    Alex Sawyer, "Ornament Plots: A Step Forward in Data Representation"
    Congrui Yi, "Support Vector Machine and Some Applications"
    Yinan Yu, "Variable Selection Using Lasso and Elastic Net"

  • 2012

    Jingxiang Chen, "Regression analysis with censored response data with the EM algorithm"
    Silver Chung, "Correcting for cross-sectional and time-series dependence"
    Kyung Yong Kim, "Paper review: 'Sure independence screening for ultra-high dimensional feature space' written by J. Fan and (2008)"
    Feiran Jiao, "An investigation of intrinsic Gaussian Markov random field models with application to satellite image data"
    Habib Kousse, "Propensity score matching methods in observational studies
    Shan Li, "Comparing two methods in modeling threshold autoregressive processes"
    Huan-Hsun Lin, "Variable selection with stepwise methods in the presence of multicollinearity"
    Cong Liu, "Multiparameter estimation for normal models"
    Barbara Monaco, "A review of response surface methods with a practical example"
    Harsimran Somal, "On classes of distributions that admit minimal & maximal members with respect to convex ordering"
    John Tiong, "Continuous wavelet analysis of time series data"
    Jing Wang, "Multiple imputation for missing data: Comparison of SAS and R methods"