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MS: Statistics

Master of science in statistics.

The Master of Science (MS) degree in Statistics provides advanced training in mathematical and applied statistics, exposure to statistics in a consulting or collaborative research environment and specialized coursework in a number of areas of emphasis. The program is intended to prepare students for careers as practicing statisticians, to provide enhanced research expertise for students pursuing advanced degrees in other fields, and to strengthen the mathematical and statistical training of students preparing for PhD studies in statistics or a related field. The MS degree requires 32-36 hours (8 or 9 courses) beyond the prerequisites. There is no thesis requirement for this degree. The entire program must be approved by the Graduate Advisor before a degree can be awarded.

Prerequisites

The prerequisites for the program include calculus through multivariable calculus, linear algebra equivalent to MATH 257 , and an introduction to mathematical statistics and probability equivalent to STAT 400 .

Course Requirements

A. four required courses (12 or 16 hours) (for course descriptions, visit the academic catalog .).

1. STAT 410 - Statistics and Probability II (4 hours)*

*This requirement can be waived if the student has already taken the course, or a course equivalent to it at another institution. (4 hours)

2. One of the following (4 hours)

• STAT 425 - Statistical Modeling I
• STAT 527 - Advanced Regression Analysis I

3. One of the following (4 hours)

• STAT 424 - Design of Experiments
• STAT 426 - Statistical Modeling II
• STAT 429 - Time Series Analysis
• STAT 431 - Applied Bayesian Analysis
• STAT 433 - Stochastic Processes
• STAT 528 - Advanced Regression Analysis II
• STAT 533 - Advanced Stochastic Processes
• STAT 556 - Advanced Time Series Analysis Note: For students who entered the program prior to Fall 2021, the listed options for this item are STAT 424, STAT 426, STAT 429, STAT 430, and 578.

4. STAT 510 - Mathematical Statistics (4 hours)

B. Five elective courses (20 hours) (For course descriptions, visit the Academic Catalog .)

At least 12 hours must be from the following list, and any course used to satisfy A2 or A3 may not also be used to satisfy B. Up to 8 hours may be from other units on campus, subject to the approval of the Graduate Advisor. All courses below are four hours except STAT 590 and STAT 593, which have a variable number of hours.

• STAT 427 - Statistical Consulting
• STAT 428 - Statistical Computing
• STAT 430 - Topics in Applied Statistics
• STAT 432 - Basics of Statistical Learning
• STAT 434 - Survival Analysis
• STAT 437 - Unsupervised Learning
• STAT 440 - Data Management
• STAT 443 - Professional Statistics
• STAT 447 - Data Science Programming Methods
• STAT 448 - Advanced Data Analysis
• STAT 458 - Math Modeling in Life Sciences
• STAT 466 - Image Analysis
• STAT 480 - Big Data Analytics
• STAT 511 - Mathematical Statistics II
• STAT 525 - Computational Statistics
• STAT 528 -  Advanced Regression Analysis II
• STAT 530 - Bioinformatics
• STAT 534 - Advanced Survival Analysis
• STAT 542 - Statistical Learning
• STAT 545 - Spatial Statistics
• STAT 546 - Machine Learning in Data Science
• STAT 551 - Theory of Probability I
• STAT 552 - Theory of Probability II
• STAT 553 - Probability and Measure I
• STAT 554 - Probability and Measure II
• STAT 555 - Applied Stochastic Processes
• STAT 556 - Advanced Time Series Analysis
• STAT 558 - Risk Modeling and Analysis
• STAT 571 - Multivariate Analysis
• STAT 575 - Large Sample Theory
• STAT 576 - Empirical Process Theory and Weak Convergence
• STAT 578 - Topics in Statistics
• STAT 587 - Hierarchical Linear Models
• STAT 588 - Covariance Structures and Factor Models
• STAT 590 - Reading Course (at most four hours total for this course)
• STAT 593 - Internship (at most four hours total for this course)

C. Experience with statistical practice in an interdisciplinary environment (For course descriptions, visit the Academic Catalog .)

This requirement is satisfied by any one of the following:

• STAT 427 - Statistical Consulting; or
• STAT 443 - Professional Statistics; or
• STAT 593 - Internship; or
• For students previously or concurrently admitted to another graduate program at the University of Illinois that uses statistics, completing at least 12 graduate hours (3 courses) in that program. The 12 hours would not count toward the MS degree in Statistics.

If STAT 427, STAT 443, or STAT 593 is taken to meet this requirement, those hours can count toward the 20 described in B.

D. Graduate level course requirement

At least 12 hours (3 courses) must be taken at the 500 level and at least 2 of the 500 level courses must be STAT courses.

Other Requirements: 

2.75 Minimum GPA

College of Liberal Arts & Sciences

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Admitted Student Statistics

Number of applications.

We typically receive  over 300 applications   to our program each year, averaging 370 over the last three years .

Current Students

We have over 100 Ph.D. students at all levels of the program. Visit our Graduate Student Directory to find out more about our current students.

Douglas Simpson

Douglas G. Simpson is a professor in the Department of Statistics at the University of Illinois Urbana-Champaign and an affiliate professor in the Beckman Institute for Advanced Science and Technology . His research interests include applied and computational statistics, quantitative image analysis, machine learning and functional data, and the general theory of robust and semiparametric statistical methods. He has served as Associate Editor of the Journal of the American Statistical Association (1996–1999), Biometrics (2000–2006) and Chemometrics and Intelligent Laboratory Systems (1999–2006), as a regular member of the Biostatistical Research and Design (BMRD) Study Section of the National Institutes of Health (2006–2010), as Chair-elect, Chair, and Past-Chair of the American Statistical Association Caucus of Academic Representatives (2007–2010). He served several terms as Chair of the Department of Statistics at the University of Illinois between 2000 and 2019 and as Associate Director of the Institute for Mathematical and Statistical Innovation (2020-2022). Dr. Simpson is a Fellow of the American Statistical Association , a Fellow of the Institute of Mathematical Statistics , and a Fellow of the American Association for the Advancement of Science .

Current Appointments

• Professor, Department of Statistics , University of Illinois at Urbana-Champaign (1997-Present)
• Affiliate Professor, Beckman Institute for Advanced Science and Technology (2008-Present)

Previous Appointments

• Associate Director, Institute for Mathematical and Statistical Innovation , National Science Foundation Mathematical Sciences Research Institutes (2020-2022)
• Chair, Department of Statistics, University of Illinois at Urbana-Champaign (2000-2019)
• Director, Illinois Statistics Office, Statistical Consulting Service, University of Illinois Urbana-Champaign (1995-2000)
• Research Fellow, National Institute of Statistical Sciences, Research Triangle Park, NC. (1993-1999)
• Associate Professor, Department of Statistics, University of Illinois Urbana-Champaign (1990-1997)
• Mathematical Sciences Postdoctoral Research Fellow, National Science Foundation (1987-1990)
• Assistant Professor, Department of Statistics, University of Illinois Urbana-Champaign (1985-1990)
• Mathematical Statistician, National Institute of Environmental Health Sciences, Research Triangle Park, NC. (1982-1985)
• BA, Mathematics, Carleton College, 1980
• MS, Statistics, University of North Carolina at Chapel Hill, 1983
• PhD, Statistics University of North Carolina at Chapel Hill, 1985

Professional Societies

• American Association for the Advancement of Science (AAAS)
• American Institute of Ultrasound in Medicine (AIUM)
• American Statistical Association (ASA)
• International Biometric Society (ENAR)
• Institute of Electrical and Electronics Engineers (IEEE)
• Institute of Mathematical Statistics (IMS)
• Society for Industrial and Applied Mathematics (SIAM)

Honors and National Committees

• Fellow, American Association for the Advancement of Science
• Fellow, American Statistical Association
• Fellow, Institute of Mathematical Statistics
• Biostatistical Methods and Research Design Study Section, NIH, 2006-2010
• Chair-elect, Chair and Past-Chair, American Statistical Association Caucus of Academic Representatives, 2007-2010.
• University of Illinois List of Teachers Ranked as Excellent by Their Students – 11 times

Professor, Department of Statistics

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Dept of Math, Stat, & Comp Sci

College of liberal arts and sciences.

Doctor of Philosophy in Mathematics

The PhD in Mathematics is designed to provide the highest level of training for independent research. Students may apply with or without a Masters degree. For those with a previous Masters degree in mathematics (or related field) the PhD is typically 5 years in duration, whereas for those without a previous Masters degree it is typically 6 years.

To earn the PhD, the student must fulfill the Graduate College requirements specified in the Graduate College catalogue as well as departmental requirements detailed in the MSCS Graduate Handbook , which includes:

• Provide proof of an equivalent MS degree or earn a high pass on the Department's written Master's Examination.
• Fulfill the doctoral preliminary examinations and minor sequence requirement.
• Pass the doctoral oral examination (Probability and Statistics students only).
• Produce and defend a thesis that makes a contribution to original research.
• Earn 96 semester hours of graduate credit including:
• 32 credit hours for a previously earned master's degree (requires DGS approval), or earn a high pass on the Department's Master's Exam
• 40 credit hours of departmental 500-level courses which may include 500-level courses taken from the MS degree earned in residence but may NOT include thesis research (MATH 599, STAT 599, or MCS 599)
• 32 hours of thesis research (MATH 599, STAT 599, or MCS 599)

College of Liberal Arts & Sciences

Program of Actuarial and Risk Management Sciences

Phd in mathematics – concentration in actuarial science and risk analytics.

This PhD concentration is intended for students with strong quantitative skills who want to acquire advanced analytical tools for academic careers and research and development careers  in insurance, consulting, investment, pension, healthcare, banking and financial services .

The University of Illinois is an ideal place for education and research in actuarial, financial and risk management, due to its well-established system of interdisciplinary collaborations among various units. Highlights of the University’s achievements in these areas can be found  here . Based in the renowned Department of Mathematics, this program offers a unique blend of a world-class rigorous mathematical education with practical research and professional training in actuarial science, quantitative finance and risk analytics. Read more at our  poster for graduate studies in actuarial science and risk management .

PhD candidates cover most of the material for professional exams, and build on that foundation to receive in-depth education on modern techniques and challenges in the financial, actuarial and risk management professions through coursework, seminars, internships and research. Due to the interdisciplinary nature of actuarial and financial research, PhD candidates are encouraged to broaden their knowledge by taking courses in statistics, finance, insurance, risk management, data analytics, etc.

The Department of Mathematics offers a unique multi-year internship program where PhD candidates are encouraged and supported to receive internship experience in summer breaks during their PhD studies. More information can be found  here . There are also internship opportunities on campus at the  University of Illinois Research Park . Students are also exposed regularly to cutting-edge research development in industry and academia by attending and presenting at the  Actuarial Science and Financial Mathematics Seminar , where a wide range of prominent researchers are invited to speak and visit. In addition, students can participate in the seminar series on  Mathematical Finance, Risk and Uncertainty , jointly organized with the Department of Industrial and Enterprise Systems Engineering.

Our program offers opportunities for PhD candidates to work in a wide range of research areas, including stochastic analysis in actuarial and financial modeling, quantitative risk management of equity-linked insurance, pension and social security, industry solvency assessment, collective risk theory, Monte Carlo simulations, and more. The  Illinois Risk Lab  also provides a channel for practical research to address emerging problems from industrial partners and professional organizations.

Financial support is offered for up to six years to every student admitted to our PhD program, in the form of teaching assistantships, research assistantships and corporate-sponsored fellowships. We also provide full reimbursement of exam fees for those who choose to take professional exams. To help students develop communication and networking skills, we also provide financial support for PhD candidate to travel to research summer schools and conferences in related areas.

Admissions Requirements

Students with a Bachelor’s degree in any quantitative field can apply directly to the PhD program. Applicants are expected to demonstrate competence in real analysis, linear algebra, and probability and statistics, either through undergraduate coursework or by means of Graduate Record Examination (GRE) mathematics subject test.

Complete information regarding graduation requirements can be found in the  Guide for Graduate Students in Mathematics . The following list only serves as a summary for prospective students.

PhD candidates in this concentration are required to complete the core courses: MATH 540 (Real Analysis) MATH 561 (Theory of Probability I) MATH 563 (Risk Modeling and Analysis) STAT 510 (Mathematical Statistics I) One additional course from a list of approved core courses for all PhD students.

PhD candidates in this concentration must also demonstrate competence in three additional supporting courses: MATH 564 (Applied Stochastic Processes) STAT 425 (Applied Regression and Design) FIN 591 (Theory of Finance)

and in two of the following actuarial graduate courses: MATH 565 (Actuarial Models for Life Contingencies) MATH 567 (Actuarial Models for Financial Economics) MATH 568 (Actuarial Loss Models)

Although not required, many PhD candidates in the Actuarial Science and Risk Analytics Concentration take elective courses in functional analysis, partial differential equations, linear and nonlinear programming, multivariate analysis, statistical computing, macro and micro economics, portfolio management, predictive modeling, machine learning.

How to Apply

The application process for this concentration is the same as the regular Mathematics PhD program. Detailed instruction as well as general requirements can be found  here . Candidates should clearly identify the Actuarial Science and Risk Analytics Concentration on the application form, and in their personal statement.

Frequently Asked Questions

If I want to become a practicing actuary, should I consider PhD education in Actuarial Science?

The actuarial profession in North America highly values professional credentials obtained through passing professional exams with credentialing bodies such as the Society of Actuaries and Casualty Actuarial Society. A PhD education in Actuarial Science is not a necessary component for a career path as an actuary. Students who are interested in career paths in traditional actuarial roles should pursue our MS program in Actuarial Science. The PhD concentration prepares students for academic careers and research and development offices/departments in the insurance and financial service industries. For example, a graduate may find a tenure-system position in a university, work as a quantitative reinsurance analyst, a catastrophe modeling analyst, a quantitative strategy researcher in a proprietary trading firm, and so on.

What qualifications are you looking for in admissions?

A typical PhD applicant should have a bachelor’s degree or its equivalent in a quantitative field, including but not limited to pure mathematics, applied mathematics, statistics, engineering, quantitative finance, physics, etc. Students should take the GRE General test.

How much do GRE scores get weighted into the application of a prospective student? What other factors weigh the most in an application?

We consider the whole application to find students with sufficient preparation and motivation to succeed in our program. Applicants typically perform strongly on the GRE General exam (80th percentile and above on the quantitative portion, and we like to see good scores on the other sections too). Scores on the GRE Mathematics subject exam (if taken) vary quite widely. Coursework relevant to actuarial science and risk analytics is valuable. 

The transcripts from your bachelor’s and master’s institutions are important. We pay close attention to courses and grades. If you come in with an actuarial or financial mathematics background, we do consider your track record of passing professional exams. However, students in this concentration also come from other quantitative fields.

What funding is available to students ?

All admitted students are offered a full tuition waiver, and a teaching assistantship (the stipend is $20,000 for the academic year; most students get some summer funding too). The funding offer runs for 5 or 6 years, depending on your level of preparation. Fellowship and RA support is available to some continuing students who perform strongly in the program.

How do the requirements for those pursuing the Actuarial Science emphasis differ from those pursuing other fields of study?

Notably, students in the Actuarial Science and Risk Analytics Concentration are not required to take Math 500 Abstract Algebra. Instead, they take Stat 510 Mathematical Statistics I. The full requirements for students in the Concentration are listed  here .

What should be my focus to prepare for this program beyond admissions?

Take as much undergraduate real analysis as possible (called “advanced calculus” at some universities), and tackle the hardest problems you can get your hands on. Learn metric space topology, with normed vector spaces being an important example. Get familiar with spherical coordinates, the divergence theorem (Gauss theorem), and  Green’s first and second identities .

Real analysis lays the foundation for probability, stochastic processes, and differential equations, at the graduate level. If you are strong in real analysis, then you can learn and pass the material in the first year comprehensive courses in a PhD program like ours.

Course Catalog

Data science & engineering concentration.

for the Graduate Concentration in Data Science & Engineering

The Data Science & Engineering (DSE) Transcriptable Graduate Concentration is designed primarily for graduate students at the Ph.D. levels with an interest in data intensive computing. Data science plays a major role in many areas of computational science and engineering (CSE) — the DSE Concentration is open to domain scientists working in this area. This concentration requires students to complete 16 graduate credit hours spanning data science, from topics in mathematical foundations (MF), computational thinking (CT), statistical thinking (ST), as well as data management, description, and modeling (DX). Courses taken toward this concentration will count towards the student’s graduate degree if permitted by the curriculum of their major, and the concentration will be listed on their transcript upon graduation.

To fulfill the requirements of the graduate concentration, students will take courses selected from an established list of core courses, along with a courses from a selection of elective courses that span a range of domain areas. Students may select any course in the list of electives, regardless of their enrolled degree program.

Additionally, understanding the ethical and societal implications of the application of data science is paramount, and CSE will integrate the latest topics to help educate future data scientists on appropriately developing and applying data science algorithms that impact society. To ensure that students in the Data Science & Engineering Graduate Concentration are exposed to current topics in this area and to highlight the how data science decisions can have real-world significance, CSE will (1) require that all DSE-seeking students attend at least one seminar on data science and social justice and (2) complete the self-paced Practical Data Ethics course developed by the UCSF Center for Applied Data Ethics. Students must affirm that they completed the course and will be required to report on their experience in order to receive the DSE Concentration. CSE will annually evaluate this requirement as additional on- and off-campus resources become available.

This graduate concentration is only available for students enrolled in these participating graduate degree programs:

• Aerospace Engineering, PhD
• Agricultural & Biological Engineering, PhD
• Bioengineering, PhD
• Civil Engineering, PhD
• Computer Science, PhD
• Electrical & Computer Engineering, PhD
• Industrial Engineering, PhD
• Materials Science & Engineering, PhD
• Mechanical Engineering, PhD
• Nuclear, Plasma, & Radiological Engineering, PhD
• Physics, PhD
• Statistics, PhD

For more information regarding the Data Science & Engineering (DSE) Graduate Concentration, visit the  Computational Science and Engineering website , or contact the CSE Office at 217-333-3247 or by email .  

Other Requirements

Admission For more information regarding the Data Science & Engineering (DSE) Graduate Concentration, visit the  Computational Science and Engineering website , or contact the CSE Office at 217-333-3247 or by  email . 

Data Science & Engineering Concentration Director of Program: Luke Olson Data Science & Engineering Concentration Program website Data Science & Engineering Concentration Program Admissions Data Science & Engineering Concentration Program faculty

Computational Science & Engineering Computational Science & Engineering website 1205 W Clark St, Suite 2102, Urbana, IL 61801 (217) 300-5696 Contact: Bryan Wang CSE email

Grainger College of Engineering Grainger College of Engineering website

Graduate Admissions Graduate College Admissions & Requirements

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• My.SiebelSchool

PhD Requirements

Requirements presented as a Table.

Breakdown of Credit Hours

• Total number of hours required for a Ph.D.: 96 (64 with an approved M.S.)
• Must complete a minimum of 48 credit hours of coursework (16 with an approved M.S.), of which 20 credit hours must be CS coursework (12 with an approved M.S.).
• Must complete a minimum of 24 credit hours of 500-level coursework (16 with an approved M.S.), of which 12 credit hours must be CS 500-level coursework. Courses in computer science numbered CS 500-CS 590 or CS 598 are considered advanced coursework.
• Must complete a minimum of 32 credit hours of Thesis Research (CS 599).

Note: CS 597 (Independent Study) and CS 591/491 (Seminar) may be applied towards bullet 2 above, but cannot be applied to bullet 3 above. Independent study coursework completed at other departments will be treated similar to CS 597 hours. A maximum of 16 credit hours of independent study coursework can be applied toward the degree.

Advanced Coursework

Courses in computer science numbered CS 500 - CS 590 or CS 598 are considered Advanced Coursework. Students must complete 24 credit hours (16 with an approved M.S.) of advanced coursework. In addition, 12 hours of the advanced coursework must be computer science courses.

Program of Study (Core Requirements)

The Program of Study is designed to allow students some flexibility to develop their curriculum in accordance with the Ph.D. graduation requirements outlined above and with the expectations of their advising/thesis committee. The Academic Office assigns students three committee members within the first month of starting the Ph.D. program. Students setup a time with their Program of Study committee to discuss their area of interests and determine what required courses must be completed and develop a strategy for meeting educational and career goals as well as the Ph.D. coursework requirements. For more detailed information, visit Program of Study .

Thesis Hours

A minimum of 32 hours of Thesis Research (CS 599) is required. Students may register for the advisor’s section of CS 599 after they successfully complete their qualifying examination. The CS 599 Thesis Advisor Agreement form must be on file prior to enrolling for thesis hours and prior to taking the qualifying exam.  ( Effective Fall 2022, registration in CS 599 hours during the term of the Qualifying Examination will not be approved. Students can register for CS 597 Individual Study credits until they pass the qualifying examination. )

Additional Requirements

• CS 591 section PHD must be taken in the first semester. A maximum of 4 credit hours of CS 591 can be applied toward the Ph.D. degree.
• The minimum program GPA is 3.0.

Effective Spring 2022, CS TA appointments which meet the following criteria will fulfill the PhD TA requirement : A 50% teaching assistantship or a 25% solo teaching assistantship for an entire term completed by the end of the 5th year, with a satisfactory performance evaluation by the department.  TAships for any CS 591 course will not count towards the TA requirement.

Ph.D. with Computational Science and Engineering (CSE) Option

The Ph.D. with a concentration in Computational Science and Engineering (CSE) is an interdisciplinary program which focuses on computationally oriented research. All Ph.D. degree requirements apply PLUS the following additional requirements.

• Students must take 12 credit hours of coursework relevant to their CSE research areas, selected with the approval of their advisor, from one or more departments outside the Siebel School of Computing and Data Science.
• CSE 500-level courses may be used to satisfy the advanced coursework (500-level) requirement.
• The Ph.D. thesis must address some aspect of CSE.
• The doctoral committee should include a faculty member from outside computer science whose interests are relevant to the student's research.

Ph.D. Requirements Table

A) phd students admitted for fall 2022 (or later).

• A teaching assistantship for an entire term, with a satisfactory performance evaluation by the department, is required by the end of the 5th year.
• International Students must show demonstration of English proficiency (equivalent to that necessary to be a TA-see Financial Aid) before taking the Qualifying Exam.
• Qualifying exam
• Preliminary exam
• Final exam or dissertation defense
• Dissertation deposit
• Minimum GPA: 3.0

PhD Students Admitted Fall 2021 or Prior

Graduate Advising

The Graduate Academic Office, a guiding hand for graduate students, offers weekday assistance.

The Graduate College at the University of Illinois at Urbana-Champaign

Graduate admissions - minimum requirements, to be eligible for admission to the graduate college at the university of illinois at urbana-champaign, applicants must satisfy the below minimum requirements:.

• Applicants must have earned at least a bachelor's degree from a regionally accredited college in the United States or a comparable degree from a recognized institution of higher learning abroad. A grade point average (GPA) of 3.0 (A=4.0), or comparable GPA for an international applicant, for last two years of undergraduate study is a minimum requirement for admission. If your undergraduate study is longer than 4 years, additional semesters may be used to calculate the admission GPA. Please note that proposed programs of study may require a higher GPA than the Graduate College's minimum standard.  
• Applicants enrolled in the final year of a bachelor's degree from an accredited college in the United States or a or comparable degree program from a recognized institution of higher learning abroad, and who meet the GPA requirements stated above will be admitted conditionally pending receipt of final academic credentials showing the undergraduate degree as conferred.
• International applicants must meet minimum requirements based on their country of origin. Please note that proposed programs of study may require a higher GPA than the Graduate College's minimum standard.  
• International applicants are required to submit proof of English proficiency. Specific requirements and waiver options are listed at  https://grad.illinois.edu/admissions/instructions/04c . 

A student who does not meet one or more of the admission requirements:

• may qualify for limited status admission with support from the academic program and approval from the Graduate College.  
• may qualify for full status admission based on a master’s or doctoral degree from a regionally accredited college in the United States or a comparable graduate degree from a recognized institution of higher learning abroad with a cumulative graduate GPA of 3.0 (A=4.0), or comparable GPA for an international applicant.  
• may qualify for full status admission after completion of a minimum of 12 credit hours as a non-degree or graduate certificate student at the graduate level within the proposed academic major at the University of Illinois at Urbana-Champaign and received a cumulative GPA that meets the department minimum for good standing.  
• may qualify for full status admission with support from the academic program and approval from the Graduate College if they have completed 10+ years of professional work experience in the field corresponding to the proposed academic major.

DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. Cross-disciplinary work is encouraged. The PhD program prepares students for careers as university teachers and researchers as well as research statisticians and data scientists in industry, government and the non-profit sector.

Requirements

Students are required to fulfill the Department requirements in addition to those specified by The Graduate School (TGS).

From the Graduate School’s webpage outlining the general requirements for a PhD :

In order to receive a doctoral degree, students must:

• Complete all required coursework. .
• Gain admittance to candidacy.
• Submit a prospectus to be approved by a faculty committee.
• Present a dissertation with original research. Review the Dissertation Publication page for more information.
• Complete the necessary teaching requirement
• Submit necessary forms to file for graduation
• Complete degree requirements within the approved timeline

PhD degrees must be approved by the student's academic program. Consult with your program directly regarding specific degree requirements.

The Department requires that students in the Statistics and Data Science PhD program:

• Meet the department minimum residency requirement of 2 years
• STAT 344-0 Statistical Computing
• STAT 350-0 Regression Analysis
• STAT 353-0 Advanced Regression
• STAT 415-0 I ntroduction to Machine Learning
• STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3
• STAT 430-1, 2 Probability for Statistical Inference 1, 2
• STAT 440 Applied Stochastic Processes for Statistics
• STAT 457-0 Applied Bayesian Inference

Students generally complete the required coursework during their first two years in the PhD program. *note that required courses changed in the 2021-22 academic year, previous required courses can be found at the end of this page.

• Pass the Qualifying Exam. This comprehensive examination covers basic topics in statistics and data science and and is typically taken in fall quarter of the second year.

Pass the Prospectus presentation/examination and be admitted for PhD candidacy by the end of year 3 . The department requires that students must complete their Prospectus (proposal of dissertation topic) before the end of year 3, which is earlier than The Graduate School deadline of the end of year 4. The prospectus must be approved by a faculty committee comprised of a committee chair and a minimum of 2 other faculty members. Students usually first find an adviser through independent studies who will then typically serve as the committee chair. When necessary, exceptions may be made upon the approval of the committee chair and the director of graduate studies, to extend the due date of the prospectus exam until the end of year 4.

• Successfully complete and defend a doctoral dissertation. After the prospectus is approved, students begin work on the doctoral dissertation, which must demonstrate an original contribution to a chosen area of specialization. A final examination (thesis defense) is given based on the dissertation. Students typically complete the PhD program in 5 years.
• Attend all seminars in the department and participate in other research activities . In addition to these academic requirements, students are expected to participate in other research activities and attend all department seminars every year they are in the program.

Optional MS degree en route to PhD

Students admitted to the Statistics and Data Science PhD program can obtain an optional MS (Master of Science) degree en route to their PhD. The MS degree requires 12 courses: STAT 350-0 Regression Analysis, STAT 353 Advanced Regression, STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3, STAT 415-0 I ntroduction to Machine Learning , and at least 6 more courses approved by the department of which two must be 400 level STAT elective courses, no more than 3 can be approved non-STAT courses.

*Prior to 2021-2022, the course requirements for the PhD were:

• STAT 351-0 Design and Analysis of Experiments
• STAT 425 Sampling Theory and Applications
• MATH 450-1,2 Probability 1, 2 or MATH 450-1 Probability 1 and IEMS 460-1,2 Stochastic Processes 1, 2
• Six additional 300/400 graduate-level Statistics courses, at least two must be 400 -level

• Applied Mathematics (Ph.D.)

Applied mathematics addresses problems in science, engineering, and society. Find new ways to solve real-world problems through original, creative research in Illinois Tech’s applied mathematics Ph.D. program.

• Academic Programs

Illinois Tech’s Ph.D. program in Applied Mathematics is a flagship graduate program that prepares talented mathematicians and statisticians for careers in research or academia through a rigorous education, which includes advanced coursework, independent study, and original research. With almost 100 percent job placement at graduation, our alumni work at Goldman Sacks, UBS, Amazon, University of Michigan, DePaul University, United Airlines, Grant Thornton, as well as start-ups and early stage companies.

The Department of Applied Mathematics and the College of Computing offer generous scholarships in the form of teaching or research assistantships that cover tuition and provide a competitive monthly stipend. 

Courses cover a wide range of topics in applied mathematics and statistics including mathematical courses offered in popular graduate programs such as Data Science , Financial Technology , and Computational Decision Science and Operations Research . 

The Department of Applied Mathematics is a vibrant research hub with internationally recognized faculty working in a variety of applied research areas such as computational mathematics, stochastic analysis, statistics, data science, applied discrete mathematics, and optimal control. 

In addition to numerous academic activities, the Department of Applied Mathematics is home to several student organizations such as Illinois Tech SIAM Student Chapter, American Statistical Association, Association for Women in Mathematics, Machine Learning @IIT, and Fun Math Problems.

Program Overview

Prepare for a career in industrial research or academia through a rigorous education that includes advanced coursework, independent study, and original research to make a significant contribution to the field of applied mathematics.

Career Opportunities

Career opportunities exist across industries, as so many need mathematics experts.

• Operations researcher/analyst
• Mathematician/statistician
• Post-secondary mathematics/science teacher
• Post-secondary mathematics/science administrator

View Details

Admission Requirements

The program typically requires a bachelor’s degree in mathematics or applied mathematics. Candidates whose degree is in another field and whose background in mathematics is strong are also eligible for admission and are encouraged to apply.

Applicants should have a bachelor’s degree from an accredited university. A cumulative GPA of 3.5/4.0 is usually required.

TOEFL scores, if required, should be a minimum of 80/550 (internet-based/paper-based test scores).

A two-page professional statement of goals/objectives and a curriculum vitae must be submitted.

Three letters of recommendation are required.

All applications are automatically considered for full scholarship in from of Teaching or Research Assistantship, with no additional application process for such funds. The scholarships are awarded to top candidates based on the strength of the entire portfolio, the departmental needs and the availability of funds. Full consideration are given to applications for Fall semesters received before the priority deadline of January 31.

Ask a Professor

What do climate change, finance, data science, sports analytics, engineering, and software development all have in common? They all have foundations in mathematics. Discover how a degree in applied mathematics can open doors to these careers, and many more, by speaking with Professor Igor Cialenco, director of graduate studies at Illinois Tech’s Department of Applied Mathematics. These virtual visits occur on Wednesdays from 3 p.m. to 4 p.m. CST.

Featured Faculty

Igor Cialenco

Jinqiao (Jeffrey) Duan

Sonja Petrović

Fred J. Hickernell

Maggie Cheng

Our alumni hit the ground running

By planning together, the Department of Applied Mathematics and Jeffery Mudrock forged a plan allowing him to work full-time while pursuing a Ph.D. and his career goals.

Learning experiences from a network of collaborators helped Lluís Antoni Jiménez Rugama evaluate mathematics from a variety of perspectives.

After emigrating to the United States, Kabre ran her own business before pursuing a Ph.D. in applied mathematics and a career in academia.

Yicong Huang finds himself incorporating his research experience into his daily work.

With a combination of theoretical and practical research experience, Xiao Huang can’t find a work problem he is unable to solve.

Learn more...

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The Doctor of Philosophy Degree program is planned by the student with the advisor to develop the student's ability to conduct research in a specialized field of education.  The College of Education offers the Doctor of Philosophy degree on-campus  only.

Program Plan

Upon admission to a Doctor of Philosophy program, each student is assigned an academic advisor in the student's area of specialization. The student and advisor plan a program of study to meet the student’s individual goals and general degree requirements. Departments may require that a copy of the program plan be kept on file.

Time Limits

Graduate students and advisors should be guided by the Graduate College policy on  doctoral degree time limits . If a time extension is desired, it may be requested by completing a Graduate Student Petition, including an Academic Progress Plan . These petitions are considered and acted upon by the student’s advisor, the department, and the Graduate College. Each individual who has authority to act on the petition may either approve or deny the petition.

Course Requirements for Ph.D. Students

All students admitted to a Doctor of Philosophy degree program must fulfill  Graduate College requirements for the doctoral degree , departmental requirements, and the following College of Education minimum requirements on the Urbana campus or through Urbana off-campus or online courses:

Completion of at least 64 hours beyond the master's degree including:

• A minimum of 32 hours of coursework in the major subjects.
• At least 4 hours, but no more than 20 hours of dissertation research (599) credit.
• No more than 12 hours of independent study (595) credit.
• A minimum of 16-20 hours, depending on area of methodology focus, in research coursework. The student should submit a plan of study, approved by the advisor, for completion of the Research Area Requirement.

The College of Education follows the Graduate College's rules on  residence credit. 

For students entering a doctoral program already holding a master's degree ( Stage II ), transfer of credit from outside institutions into this degree is not allowed.  This is in accordance with the Graduate College's policy on  transfer credit.  

Ph.D. Research Area Requirement

The purpose of the Ph.D. Research Area Requirement is to ensure that all Ph.D. students in the College of Education have sufficient coursework to attain proficiency in at least one research methodology and are able to conduct independent dissertation research.

The four areas of research—Interpretive, Qualitative, Quantitative, and Mixed Methods—were chosen to represent the domain of methodologies prevalent in educational research and pursued by our graduate students and faculty. It is intended that the choice of a focus area will be consistent with a student’s dissertation research. All areas require a combination of introductory and advanced methods coursework. All students choose coursework in consultation with their academic advisor. Students must maintain a B average for all methodology courses. 

All students will take a minimum of 16 and a maximum of 20 credit hours in research methods courses towards the research area requirement.

Interpretive methods of research and analysis play a role in educational research in different senses.  They are used in many disciplines and fields as primary means to creating a narrative, making meaning, or making cultural or policy critiques.  For example, in humanistic studies of education, such as philosophy of education and history of education, the interpretation of texts, events, human actions, narratives, and concepts forms the basis of research. In these or other cases, the analysis of language can play a central role.  In legal analysis in education, for example, the use of interpretive methods involves the analysis of case law, legislation, and administrative policy.  In cultural studies or discourse analysis in education, the interpretation of culture, practices and artifacts, or language itself plays a central role in studying social patterns of inclusion, exclusion, and the dynamics of power.  In some varieties of curriculum theory, the interpretation of textbooks and other materials plays a key role in explaining how society reflects judgments about knowledge and value in their curricular choices.  In each of these contexts (philosophy, history, legal analysis, cultural studies and discourse analysis, or the studies of textbooks or other texts) the meaning and skills of "interpretation" very - for example, the interpretation of a school textbook or classroom film is of a very different sort than the interpretation of a human action, a historical event, or a legal text, etc. In some cases the skills of interpretation are inseparable from disciplinary knowledge and expertise.

Therefore, no single set of courses will suit all of these discrete sets of skills. For this reason, the list of requirements is organized around areas of emphasis. It is for the student and advisor to identify suitable courses; many (for example, legal analysis) will entail course work outside the College of Education.

The Interpretive Research requirement: 

• Provides a foundation for students to be able to understand general methodological issues and problems in educational research;
• Includes basic course work in conceptual analysis, documentary and other kinds of discursive analysis, and epistemological analysis;
• Emphasizes coursework that connects method to disciplinary study;
• Helps students develop critical and interpretive tools to be used to analyze both the limitations of educational research itself and substantive problems in the field of educational policy and practice;
• Prepares students to interpret and analyze a variety of texts and other cultural artifacts, including but not limited to documents, curricula, discursive products, film, theory, policy, and law; and,
• Provides students with opportunities to develop skills they will use as independent researchers-either using interpretive methods alone or in conjunction with other research skills. 

Foundational Methods Course (4 hours)

All students will take the foundational methods course, Methods of Educational Inquiry (CI/EPOL/EPSY/SPED 550).  It is recommended that students take this introductory foundations course in their first year of the doctoral program.

Additional Courses (at least 12 hours)

At least three additional courses should be selected from offerings in the College of Education or from other Colleges, subject to approval by the student's advisor. Advisors have discretion if the best training for each student should reflect deep training in a single area or broad training across several areas of interpretive methodology.

Students must provide a rationale in writing for how chosen coursework meets the research requirement. This rationale will be reviewed by the student's advisor and the departmental Director of Graduate Studies.

Selected courses should address theory and applications in interpretive methods as practiced in the humanistic disciplines (e.g., philosophy of education and history of education), curriculum theory, cultural or literary studies, or policy and legal analysis. So for example:

• courses in interpretive methods in philosophy might pertain to logic and the analysis of arguments, the different forms and genres of philosophical texts (e.g. treatises versus philosophical dialogues), or philosophy of language;
• courses in interpretive methods in history might pertain to logic and the analysis of archival materials, methods of oral history, the use of public, social, or intellectual history, or historiography more generally;
• courses in interpretive methods in curriculum theory might pertain to the structure, forms, and purposes of textbooks, childrens literature, or uses of film in the classrooms;
• courses in interpretive methods in cultural and ethnic studies might pertain to methods of discourse analysis, intersectional analysis, structural/post-structural theories, or social and critical theories;
• courses in interpretive methods in legal and policy analysis might pertain to legal interpretation, judicial reasoning, civil rights, or constitutional law;
• courses that may cut across several of these areas might pertain to studies of narrative or studies of the nature of language itself, these may also include courses in social semiotics, media theory, multimodal representation, spatial and geographical analysis (including GIS), data modelling, information theory, computer-based semantics, and art theory

The field of qualitative research in education (also spoken of as ethnography, qualitative field study, case study, naturalistic research, and interpretive research) is extremely rich and diverse and encompasses several different versions of its aim and methods as influenced by the Chicago School of Sociology, the Verstehen tradition in sociology (including symbolic interactionism, ethnomethodology, ethnomusicology, the ethnography of communication, and other types of micro-ethnography), the ethnographic tradition in cultural anthropology and fieldwork sociology, and notions of educational connoisseurship and criticism. In addition, ideas drawn from philosophical hermeneutics, social constructionism, postmodern theory, feminist theory, and critical theory of society shape conceptions of qualitative research as a way of studying the social world.

Some forms of qualitative research involve empirical investigation of the social world by means of field study or fieldwork employing the approach of participant observation.  Qualitative research as field study emphasizes observation in situ-that is, learning by means of a (relatively) sustained presence in a situation or setting and observing the goings-on there.  Moreover, participant observation is not merely a methodology but an epistemology: the inquirer-as-fieldworker assumes that immersion in, intimate familiarity with, and/or empathetic participation in the human action studied is necessary for grasping, understanding, and eventually portraying the meaning of social action.  

Not all qualitative studies, however, are fieldwork in this traditional sense. In fact, some contemporary forms of qualitative research are actually quite critical of the traditional approach to fieldwork as participant observation. Some qualitative studies employ life history methodologies, examine the constitution and meaning of cultural artifacts, or focus on the constitution and operation of various discourse practices.

Qualitative research offers an array of meaningful methodological frameworks for exploring a range of educational matters: e.g., examining the intersection of language, culture, and schooling; the relationship between schools and their communities; the formation and enactment of school and curricular reform and other policy initiatives, and so on.  Therefore, students aiming to develop a special focus in qualitative research must seek out opportunities to explore the use of qualitative research in investigating substantive issues in their particular field of interest (e.g., curriculum design, educational policy, language education, higher education, adolescent development).

 The Qualitative Research focus area is intended to help students develop: 

• Competence in understanding and addressing methodological, epistemological, ethical, and political issues that cut across the field of qualitative research (and across all of social science research, more generally).
• Competence in multiple means of generating, interpreting, and reporting qualitative data.
• Competence in locating/situating/linking the understandings and skills comprising (1) and (2) within an interpretive frame of reference (cultural anthropology; traditional naturalistic, Verstehen sociology; feminist epistemology; post-structural theory; critical theory of society, etc.

The following are recommended courses offered in the College of Education.  Other courses may be chosen but are subject to approval by the student's advisor.

Basic Courses (4-8 hours)

It is recommended that a student take a course from the following list and a basic quantitative course.

• CI 509 Curriculum Research: QRM Qualitative Research Methodology
• CI 509 Curriculum Research: AR Action Research
• CI 519 Methods of Child Study
• ERAM 551 Philosophy and Educational Research
• ERAM 555 Ethnographic Methods in Education
• ERAM 556 Program Evaluation
• EPSY 577 Foundations of Qualitative Methods
• EPSY 578 Qualitative Inquiry Methods

Advanced Courses (8 hours)

• CI 537 Discourse in STEM Classrooms
• CI 538 Qualitative Analysis of Video Data
• CI 552 Qualitative Writing
• CI 562 Linguistics in the School Curriculum
• CI 580 Qualitative Research in Language and Literacy Education
• SPED 575 Mixed Methods Inquiry
• ERAM 575 Action Research and Educational Leadership
• ERAM 576 Discourse Analysis

Expertise in the design, analysis and interpretation of research employing quantitative techniques underlies a substantial portion of educational research.  The purpose of this research methodology is to provide a programmatic approach to developing scholarly expertise in quantitative methodologies.

The area of quantitative methodology has the following purposes: 

• To provide a foundation for students to be able to interpret and judge the appropriateness of quantitative aspects of educational research;
• To prepare students to conduct quantitative analyses, to articulate the methodology employed, and to interpret and discuss the meaning of the results in lucid discourse; 
• To help students understand the strengths and limitations of quantitative methodology;
• To help students develop a quantitative research base by becoming familiar with journals and seminal sources of research methodology; and, 
• To build a base upon which students can independently extend their knowledge and expertise in quantitative methods as demanded by their own research.

The Quantitative Research focus area enables the student to further specialize in one of three sub areas of quantitative methodology: 

• Statistical and/or quantitative analysis and appropriate interpretation of data collected through experimental or quasi-experimental research. 
• The development and psychometric analysis of measurement instruments.
• The design of experiments.

 All students will take the foundational methods course, Methods of Educational Inquiry (CI/EPOL/EPSY/SPED 550).  It is recommended that students take this introductory foundations course in their first year of the doctoral program.

Basic Courses (8 hours)

If a course is not offered when the student needs it, courses across departments can be taken (e.g. PSYC 506 followed by EPSY 581) and are subject to approval by the student's advisor.

The student must take 8 basic research hours. The student must demonstrate a basic level of statistical knowledge by satisfactorily completing either: 

EPSY 580 Statistical Inference in Education AND EPSY 581 Applied Regression Analysis

PSYC 506 Statistical Methods I AND PSYC 507 Statistical Methods II

STAT 400 Statistics and Probability AND STAT 425 Applied Regression and Design

The student must demonstrate expertise in a sub area by satisfactorily completing a minimum of 8 hours from one of the following specializations: 

Statistical/Quantitative Analysis Methodology

• CI 539 Educational Data Mining
• EPSY 574 Quasi-Experimental Design
• EPSY 582 Advanced Statistical Methods
• EPSY 584/PSYC 594/SOC 584 Multivar Analysis in Psych and Ed
• EPSY 587 Hierarchical Linear Models
• EPSY 588 Covar Structure and Factor Models
• EPSY 589 Categorical Data in Ed Psych 
• STAT 426 Sampling and Categorical Data or EPSY 589 Categorical Data in Ed Psych (Credit is not given for both STAT 426 and EPSY 589.)

Measurement Methodology

• EPSY 585/PSYC 595 Theories of Measurement, 1
• EPSY 586/PSYC 596 Theories of Measurement, 2
• PSYC 490 Measurement and Test Development Lab
• PSYC 509 Psych Scaling: Multidimensional Methods 

Experimental Methodology

• EPSY 574 Quasi-experimental design
• SPED 583 Case Experimental Design 

Mixed methods research in education provides an in-depth, flexible approach to examining a world where interdisciplinary research is exceedingly more commonplace. It enables researchers to combine qualitative and quantitative approaches to design rigorous research for a nuanced understanding of complex phenomena (Creswell & Clark, 2011). Mixed methods research engages multiple paradigms, methodological traditions, modes of data collection, and analysis techniques (Greene, 2007) and is particularly appropriate in areas of social science research that translates to real-world application and/or policy, such as education, politics, technology, race, and more. 

Mixed methods researchers can ask and answer research questions that a single method research study may not be able address. Researchers in this area collect and analyze both qualitative and quantitative data rigorously and in a contextually appropriate manner. Additionally, researchers can adopt mixed methods to combine research across fields, theories and worldviews.  Mixed methods can be applied in a single research study or in multiple phases of a research.

Students in this specialization will:

• Understand the methodological, epistemological, ethical, and political issues underpinning mixed methods research, and more generally, social science research;
• Design rigorous mixed methods studies that are guided by the research questions, and that are contextually appropriate and socio-culturally responsive;
• Articulate the methodology employed, conduct and assess mixed analyses, and interpret and report the meaning of the results;
• Identify the strengths and limitations of mixed methods;
• Interpret and judge the appropriateness of basic quantitative and qualitative aspects for mixed method study;
• Build knowledge and skills from which they can independently extend their use of mixed methods as demanded by their own research as well as by the current evolution of social needs; and
• Familiarize themselves with journals and seminal sources of research methodology.

The Mixed Methods focus area requires 20 hours of course work (4 hours in Foundations and 16 hours of additional coursework, as distributed below).

All students will take the foundational methods course, Methods of Educational Inquiry (CI/EPOL/EPSY/SPED 550). It is strongly recommended that students take this course in their first year of the doctoral program.

Focus Area Courses (16 hours)

The following are recommended courses offered in the College of Education.  Other courses may be chosen but are subject to approval by the student's advisor. 

Students will also take the following three courses. It is strongly recommended that students take EPSY 578 and EPSY 580 before EPSY 575.

• EPSY 577 Foundations of Qualitative Methods or EPSY 578 Qualitative Inquiry Methods
• EPSY 580 Statistical Inference in Education
• EPSY/SPED 575 Mixed Methods Inquiry

Additionally, students will take one course from the following list:

• CI 509 Curriculum Research: QRM Research Methodology
• CI 509 Curriculum Research: Action Research
• EPSY 471 Introduction to Evaluation Method

In collaboration with their advisor, the student must develop and annually maintain a Research Area Plan.  Discussions should center around why the student has chosen the focus area and the ways in which it is expected to contribute to the student’s doctoral research and future career plans.

Since courses may change from the original plan due to course offerings, the student will provide an updated Research Area plan during each annual  Evaluation of Academic Progress . The advisor will approve the plan as part of their evaluation process.

Research area coursework must be satisfactorily completed before the student can submit a request for the preliminary examination. The Graduate Student Services Office will confirm the milestone has been completed upon receipt of the committee request form.

If there is a problem related to the research requirement process, the student can choose to appeal to the Director of Graduate Studies at the department level. More information on the grievance process can be found in the  Grievance Policy and Procedures section  of the handbook.

Early Research Project

All Ph.D. students shall conduct and present an educational research study by the end of their third full year in their graduate programs. This research should be undertaken with the expectation that it will contribute to knowledge in the area of the student’s Ph.D. program. In addition, an important objective of the Early Research Project is to familiarize faculty members with new Ph.D. students and their research interests and to examine ways in which these interests might be pursued in the doctoral program.

By the end of the first full year of doctoral study, or soon thereafter, all Ph.D. students should consult with their advisors about the formation of an Early Research Project (ERP) committee consisting of the advisor and two other faculty members. ERP committee members should be members of faculty who have been admitted to the Graduate College. With the approval of the head or chair of a department, up to one member of the committee may be approved from outside the university. Committee members are expected to provide counsel as the early research project develops. Whenever extended work with a faculty member is anticipated, the student should arrange for independent study credit.

The student shall formally present to their committee a written and oral report on the early research project. For the work to satisfy the ERP requirement for the Ph.D., all three members of the committee must approve and sign the ERP form. The completed  Early Research Project form  must be filed with the Graduate Student Services Office.

A student who has completed a master’s thesis as part of earlier graduate work may, upon the advice of their advisor, present that research as the ERP. After hearing the presentation, the ERP Committee may accept the written and oral report as satisfying the early research requirement, or the committee may recommend that the work be revised or that another line of inquiry be pursued for the early research requirement.

A student who enters a master’s degree program with the intent of subsequently pursuing the Ph.D. is encouraged to discuss with their advisor the possibility of forming the ERP Committee prior to conducting the master’s thesis research (Graduate Faculty Action, December 9, 1988). For all early research involving the use of human subjects, approval for use of human subjects or confirmation that human subjects review is not required must be obtained from the  Institutional Review Board . A letter showing approval from the Campus Institutional Review Board (IRB) must be provided to the department contact in the Graduate Student Services Office prior to the scheduling of the ERP. Students should begin the approval process eight weeks prior to the ERP. 

Qualifying Examinations

The Qualifying Examinations are written comprehensive examinations administered to doctoral students near the completion of their coursework and after completion of the Early Research Project. The Early Research Project must be completed and the results submitted to the Graduate Student Services Office by the academic advisor prior to a student beginning the Qualifying Examinations.

Purposes of the qualifying examinations in the College of Education include:

• assessment of the student's breadth in the discipline and depth in areas of interest
• provision of an opportunity to explore, make connections, and integrate content in the discipline

General Field Qualifying Exam

Each Ph.D. student will take a General Field Examination covering the field of study embraced by the home department or division. General Field Exam questions will be developed as per each department's internal procedures. General Field Exams will be evaluated by a faculty committee that is determined as per each department's internal procedures. 

Special Field Qualifying Exam 

All Ph.D. students will take a Special Field Examination covering an area of specialization proposed by the student with the concurrence of the advisor. The Special Field should be a scholarly specialization more broadly conceived than the anticipated dissertation topic.

The advisor will be responsible for developing questions for the Special Field Exam, drawing upon the expertise of other faculty when needed. The advisor, in consultation with the student, will also determine the format of the examination and select at least two additional faculty readers with expertise in the field being examined.

Formats should be decided well in advance of exam dates. In particular, students should discuss the format of the Special Field Exam with their advisors to arrive at a recommendation that best meets student needs as well as the expectations of the advisor and Department. The three formats are:

• On-Site Format. A room and proctor are scheduled by the department. Normally, the General Field and the Special Field are each scheduled for a four-hour block of time. The time limit will be set by each department (or division).
• Take-home Format. A take-home format may be used for the General Field exam (at the option of the department) and for the Special Field exam (at the option of the advisor). In the take-home format the student, with the approval of the advisor, writes the exam at a place of their choosing with no restriction on books or other written materials to be used. Because the purpose of the qualifying examination is to assess individual competence, students should not discuss the exam with anyone other than their advisor after they have picked up the questions. The time limit for this take-home exam will be set by each department (or division or program area).
• Portfolio Format (Special Field only). This format consists of assembling a focused collection of 3 or 4 high quality papers and/or projects which are then defended before three faculty readers. The number, subject, and length of the required papers or projects are decided by the advisor in consultation with the student. Portfolio submissions can include collaborative work, but independent work must also be reflected in the portfolio. The portfolio may include work completed to satisfy other requirements for the doctoral degree such as course papers, early research papers, master’s theses, and Research Specialization papers. The portfolio must be submitted with an original, independently written synthesis paper that defines the special field and articulates how each piece of the portfolio connects or contributes to the special field and its literature. If the portfolio option is selected, the student will discuss this work at an oral defense before the three faculty readers, after which the readers will determine whether the student has demonstrated competence in the special field.
• Student will confer with advisor to determine exam format, dates, and readers.  Readers should be contacted prior to submission of the  Qualifying Examination Information Form  to determine availability.
• Student will submit the Qualifying Examination Information Form prior to beginning exam
• Staff in the Graduate Student Services Office will confirm dates and reader agreements.
• Advisor will email the question(s) no less than three days prior to the first date of the exam to the Graduate Student Services Office, unless prior arrangements are made.
• The Graduate Student Services Office will email question(s) to the student, unless prior arrangements are made.
• Students will submit their qualifying exam via email to the Graduate Student Services Office by 5 pm on the deadline date.
• The Graduate Student Services Office will send the exam and evaluation form to the faculty readers.
• Faculty readers will have 2 weeks to submit their evaluations unless prior arrangements are made.
• The Graduate Student Services Office will notify the student and advisor of passing exam results, including comments.  If revisions are required, the Graduate Student Services Office will contact the advisor and the advisor will work with the student and readers to determine revision requirements and deadlines. 
• A copy of the exam and reader comments will be placed in the student’s file.
• The Graduate Student Services Office will schedule a room once the Qualifying Examination Information Form has been received.
• Following the exam, the readers of the exam will be notified to complete the evaluation form.
• The Graduate Student Services Office will notify the student and advisor of the exam results.
• A copy of the exam and exam results will be placed in the student’s file.

To pass the examination, the student must receive excellent or satisfactory ratings from all faculty readers on each of the General Field and Special Field exams. There are three possible ratings for all sections of the qualifying exams.

• Excellent doctoral work. This rating is given for excellent doctoral work. If more than fifty percent of a student's ratings are excellent, the student is given a letter of special commendation by the department head/chair.
• Satisfactory doctoral work. This rating is given for work that demonstrates competence expected of advanced students in the field.
• Unsatisfactory doctoral work - revision of original exam required: The recommendation is that the student be given an opportunity for revision of the unsatisfactory portion(s) of the exam paper. This rating is given for work that demonstrates competence but requires significant revisions in content and/or the development of ideas to be considered satisfactory as a doctoral examination.
• Unsatisfactory doctoral work - rewrite with new question required:  The recommendation that the student be given an entirely new exam question for a complete rewrite of the exam paper. This rating is given for work that the committee member does not consider well-developed to the degree that revisions alone could lead to a satisfactory outcome.
• Unsatisfactory doctoral work - student fails exam

If the examination is rated "unsatisfactory" by any member(s) of the committee, the member(s) making that evaluation shall communicate the major deficiencies to the student and make a collective decision as to the format and scope of the revised or new examination, if applicable. All faculty readers who rated the first exam "unsatisfactory" will evaluate the revised exam.  A satisfactory or excellent rating must be awarded by all readers for the student to pass the revised examination, and should one or more readers judge the second exam unsatisfactory, the readers shall meet to review the student's performance. If extenuating circumstances exist that warrant a third attempt, the advisor may request approval from the department head/chair or designee. Students shall normally be permitted two attempts to pass each of the qualifying examinations.

Students should receive results within 3 weeks from the date of the exam. The department will ensure timeliness of review and communicate results to students and to the advisor.

Faculty have two weeks upon receipt of the qualifying exam to submit their results to the  Graduate Student Services Office  (GSSO).  Shortly thereafter, GSSO will send the results to the student, copying the Director of Graduate Study and advisor.

After all readers for the exams have returned their excellent or satisfactory evaluations, a letter is sent to the student from their department indicating the readers’ decisions. A copy of the letter and evaluations are placed in the student's academic file.

The first person a student should consult concerning the qualifying exams is their advisor. If irreconcilable differences arise between the student and advisor concerning scheduling, format, content, or rating procedures, the student should consult the department head/chair or designee. If the problem cannot be resolved, consult the Associate Dean for Graduate Programs. Normal grievance procedures can be used (see  Grievance Policy and Procedures section of this handbook.) If a student wishes to postpone a scheduled examination, the request should be made through the advisor to the department office.

Human Subjects Approval

For all dissertations, approval for use of human subjects or confirmation that human subjects review is not required must be obtained from the  Institutional Review Board  prior to doing research on the dissertation topic. A letter showing approval from the Campus Institutional Review Board (IRB) must be provided to the department contact in the Graduate Student Services Office prior to the scheduling of the preliminary examination. Students should begin the approval process eight weeks prior to the examination.

Preliminary Examination

The preliminary oral examination (prelim) follows successful completion of all required coursework, the early research requirement, the qualifying examinations, the research methods requirement, and human subjects approval. In addition, all incomplete grades must be changed to letter grades prior to the oral examination.

The purpose of a prelim is for a student to present the rationale and format for the dissertation. During the examination, an agreement is reached between the student and the committee concerning the proposed dissertation. Thus, the examination is held prior to the collection of data or other major work on the dissertation. The student must be registered in order to take the prelim. The student should consult the department for additional requirements. 

Students should begin appointing the committee and scheduling the examination at least four weeks prior to the expected date.

The committee must meet the  preliminary examination committee requirements  of the Graduate College including:

• The chair must be a member of the  Graduate Faculty .
• The committee must include at least four voting members, at least three of which must be members of the Graduate Faculty, and two of which must also be tenured.
• If there are more than four voting members on the committee, at least half of the voting members must be members of the Graduate Faculty.

In addition to these requirements, College of Education requirements must be met:

• The committee chair must be on the tenure-track at the University of Illinois at Urbana-Champaign or have active tenure status awarded by the Graduate College after retirement or resignation that includes graduate faculty membership at the University of Illinois at Urbana-Champaign. 
• At least one member must be from outside the student’s field of specialization and the budgetary department of the student and advisor.  If the student is a member of the Education Policy, Organization and Leadership department, at least one member must be from outside the graduate concentration of the student and advisor in place of the budgetary department.

For committee members outside of the University of Illinois at Urbana-Champaign, a letter of justification and curriculum vitae must be submitted by the chair to the Graduate Student Services Office who will then submit it to the College of Education Associate Dean for Graduate Programs and the Graduate College for approvals. In appointing the committee, the student submits committee member information to the Graduate Student Services Office by completing the  Request for Appointment of Committee Form . This form must be submitted at least four weeks before the examination. The Graduate Student Services Office will retrieve the appropriate approvals.

The student must present the dissertation proposal to the prelim examination committee for reading two weeks before the examination; in some cases, it may be more reasonable to allow three weeks. Failure to do so may result in delaying or canceling the prelim.

The chair, student, and at least one additional voting member of the committee must be physically present for the entire duration of all oral components of the examination. If the committee has more than one chair, all chairs must be physically present; in these cases, no additional voting member is required to be physically present. All voting members of the committee must be present in person or participate via teleconference or other electronic communication media during the examination, deliberation and results determination.

The chair obtains the Preliminary Exam Result (PER) form from the Graduate Student Services Office before the examination and returns the form immediately after the examination. All voting members must sign the PER.

Decisions of the prelim committee must be unanimous. The committee may make one of the following decisions:

• Pass   the student.
• Fail the student. A program may, but is not required to, grant the student another opportunity to take the examination after completing additional course work, independent study, or research, as recommended by the committee. However, if a second attempt is given, a new committee must be appointed by the Graduate College. The new committee may, but does not have to, consist of the same members as the original committee.
• the same committee must re-examine the student,
• the second exam  must  occur within 180 calendar days of the date of first exam, and
• the outcome of the second exam must be pass or fail.

The result of the examination is communicated to the student and the Graduate Student Services Office as soon as possible at the conclusion of the examination.

Number of Attempts: After a fail result, a student will only be allowed to take the preliminary examination one additional time while working toward the completion of any one program of study. 

The preliminary examination must be retaken if the final examination is not passed within five years of the original examination.

Dissertation

The Ph.D. dissertation is intended to demonstrate the student’s capacity to conduct independent research. The student’s research should make an original contribution to knowledge (Graduate Faculty Action, February 15, 1973). The dissertation usually requires a year or more of study. Registration in dissertation research hours for on-campus or in-absentia students, after the completion of the required 64 hours beyond the master’s degree, is required. This registration typically comes after the coursework is completed and before the time limits are reached.

The dissertation must be prepared using one of the following commonly accepted editorial styles:

• American Psychological Association. (2019). Publication manual of the American Psychological Association (7th ed.). Washington, DC: Author.
• Harvard Law Review Association. (2020). The bluebook: A uniform system of citation (21st ed.). Cambridge, MA: Author.
• Modern Language Association. (2021). MLA handbook for writers of research papers (9th ed.). New York, NY: Author.
• Turabian, K. L. (2016). A manual for writers of term papers, theses, and dissertations (9th ed.). Chicago, IL: The University of Chicago Press.
• University of Chicago. (2017). The Chicago manual of style (17th ed.). Chicago, IL: The University of Chicago.

NOTE: LaTex is not an acceptable editorial style. However, this typesetting system can be used in conjunction with one of the above editorial styles.

The student will consult with the chair and dissertation committee members to choose the style to be used. In exceptional circumstances, style manuals not listed above may be used with prior approval of the Associate Dean for Graduate Programs. In addition, the  Thesis & Dissertation policies and procedures  issued by the Graduate College must be followed. In the case of explicit differences between the Graduate College instructions and the style manual selected, the Graduate College instructions take precedence. For example, students choosing the APA style manual should follow the Graduate College instructions rather than those included in the APA manual for insertion of tables and figures. Every dissertation in final manuscript form must be reviewed and approved by the dissertation director of research and/or chairperson of the dissertation committee to ensure that the dissertation meets the Graduate College and departmental requirements for deposit.

Abstracts for dissertations in the College of Education must include a synopsis of the following information to fully describe the completed study:

• The problem and its theoretical and educational significance.
• The research design and/or approach employed (include where appropriate descriptions of subjects and methods).
• An overview of the results.
• Conclusions, recommendations, and/or implications.

Final Examination

Final examinations are oral and open to the public. The final examination committee chair is responsible for convening the committee, conducting the examination, and submitting the Final Exam Result form to the Graduate Student Services Office.

Students (also known as "candidates" at this stage) should begin appointing the committee and scheduling the final examination (final) at least four weeks prior to the expected date.

The committee must meet the  final examination committee requirements  of the Graduate College including:

• The examination committee must include at least four voting members, of which at least three must be members of the Graduate Faculty and at least two must be tenured.
• If there are more than four voting members on the committee, at least half of the voting members should be members of the  Graduate Faculty .
• The committee chair must be on the tenure-track at the University of Illinois at Urbana-Champaign or have active tenure status awarded by the Graduate College after retirement or resignation that includes graduate faculty membership at the University of Illinois at Urbana-Champaign.
• At least one member must be from outside the candidate's field of specialization and the budgetary department of the student and chair.  If the student is a member of the Education Policy, Organization and Leadership department, at least one member must be from outside the graduate concentration of the candidate and advisor in place of the budgetary department.

The final is a public event to be conducted in a room that will accommodate the candidate, the committee, and any attendees. The public may not ask questions or give input during the examination.

The candidate must present the dissertation to the final examination committee for reading two weeks before the examination; in some cases, it may be more reasonable to allow three weeks.

The chair, candidate, and at least one additional voting member of the committee must be physically present for the entire duration of the examination. If the committee has more than one chair, all chairs must be physically present; in these cases, no additional voting member is required to be physically present. All voting members of the committee must be present in person or participate via teleconference or other electronic communication media during the examination, deliberation and results determination.

The chair obtains the Final Exam Result (FER) form and the Thesis/Dissertation Approval (TDA) form from the Graduate Student Services Office prior to the examination and returns the forms immediately after the examination. All appointed committee members' signatures are required on the forms; signatories must sign for themselves. Committee members have the right to review the final copy of the dissertation before signing. The committee should meet in private before beginning the examination. At the conclusion of the examination, the committee will meet in private to discuss the results and then inform the candidate of the decision.

Results: Unanimous decisions are not required. Decisions of the committee for the final are recorded on the FER form.  The voting members of the committee must make one of two decisions:

• Pass the candidate . The candidate passes the final exam if the Director(s) of Research vote pass and no more than one of the remaining committee members votes fail. The committee will indicate on the FER form if revisions are required. The committee will sign the TDA form after the completion of the examination and the completion of any required revisions.
• Fail the candidate . The candidate fails the final if the Director(s) of Research votes fail or if two or more committee members vote fail. A program may, but is not required to, grant the student another opportunity to take the examination after completing additional research or writing, as recommended by the committee. However, a new committee must be appointed by the Graduate College. The new committee may, but does not have to, consist of the same members as the original committee.

Number of Attempts: After a fail result a candidate will only be allowed to take the final examination one additional time while working toward the completion of any one program of study. 

After the passing the final, provide the dissertation in final form to the committee chair and visit the Graduate College  Thesis & Dissertation  web pages for further deposit instructions. 

All students who have successfully defended their dissertation must obtain departmental dissertation format approval prior to final deposit with the  Graduate College Thesis Office . Departmental format approval consists of three parts:

• Chair and dissertation committee,
• Head/Chair of department/program or authorized signatory, and
• Departmental thesis/dissertation format reviewer. Because revisions requested by the chair and dissertation committee may cause a change in pagination or format, students should submit a PDF of the dissertation the Graduate Student Services Office only after all revisions have been approved by the chair, dissertation committee, and the head/chair of department/program or authorized signatory. The departmental dissertation review process will not begin until prior approvals have been received. Dissertations must be received by the Graduate Student Services Office contact two weeks prior to the Graduate College doctoral dissertation deposit deadline to allow an opportunity for revisions. Late submissions may result in a delay of the student's graduation and degree conferral.   

After the departmental format review, dissertations are deposited in the Graduate College using the  ETD process .

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Graduate student spotlight: How STEM student Grace Maria Eberhardt became a historian

Graduate student Grace Maria Eberhardt didn’t think she would become a historian when she began as an undergraduate student at the University of Puget Sound. She initially wanted to study STEM, but after taking an African American studies class, she became more interested in ethnic studies and the societal aspects of science. The impetus for her interest in history was when a friend told her that the natural history museum on campus had been named after eugenicist James R. Slater.

Slater was a professor at the University of Puget Sound where he taught a class on eugenics until 1950. Eberhardt became concerned that this history wasn’t more widely known or discussed at the university, so she applied for a research grant to investigate the ethics of the name. The grant allowed her to experience archival research for the first time and she wrote a paper about her findings. During her research she worked with the director of the museum and biology professor Peter Wimberger, African American studies professor Dexter Gordon, and historian of science Kristin Johnson. Her experiences with them helped her realize that this was a field she could pursue and study.

Her research that summer became the catalyst for larger conversations at the university about renaming the museum. She collaborated with a group of students and faculty members to organize a symposium focusing on the legacy of eugenics at the University of Puget Sound and created a website . A renaming committee was formed, and the museum was renamed the Puget Sound Musuem of Natural History in May 2023.

The experience cemented Eberhardt’s desire to become a historian of science and race. After completing her BS degree in biology and African American studies with an emphasis in bioethics she chose to pursue her master’s and PhD in history at the University of Illinois. She chose Illinois because she felt it could provide the interdisciplinary research and learning experiences she was seeking.

“Even though I’m in the history department, and I do history, I’ve been able to take classes outside of history that I feel fit really well into my own research, including Latina/Latino studies classes, health, humanities, and even integrative biology.”

Eberhardt also recently completed a graduate minor in Latina/Latino studies. She works most closely with professor David Sepkoski in the Department of History and professor Natalie Lira in the Department of Latina/Latino studies.

“I feel like I’ve really been able to benefit from both of their different perspectives,” Eberhardt said. “I am really interested in intersecting and connecting the history of science and ethnic studies. I would love to further that in my career however I can.”

Eberhardt is eager to contribute to the field and collaborate with other scholars. She recently organized a roundtable for this year’s History, Science, and Society Annual meeting in Merida, Mexico, on the future of 20 th and 21 st century history of science and race in the Americas.

Right now, Eberhardt is working on readings for her preliminary exams and will write her dissertation proposal this year. She will continue to investigate the history of eugenics and will focus on 20 th century eugenic racializations of Latina/Latinos in the United States.

“I'm interested in this because I am Latina and Latinos are usually discussed or identified as an ethnicity, which I think is kind of unique in terms of like how the United States views other ethnic and racial groups,” she said. “So this project is personal to me, but in multiple ways, with my own identity and then within my own interests.”

This summer she visited the archives of the former Eugenics Records Office at Cold Spring Harbor Laboratory and the American Philosophical Society to investigate how Latina/Latinos were classified by U.S. eugenicists and bureaucrats in the early 20 th century. While there she discovered that many Latinos, like Cubans and Puerto Ricans, were being classified as “Spanish,” a subcategory of “Caucasian.”

“I scurried through the box of notecards to confirm this classification was not a fluke, and it became clear to me that my work here was far from over. It had just begun: now I needed to know why eugenicists had such a difficult time with the ‘racial’ characteristics of Latin Americans. With still so many questions left unanswered, and boxes left un-opened, I now know that I have many more exciting hours ahead in my archival journey,” she wrote in an essay for the Department about the experience .

This fall she’ll TA for HIST 103: A History of Everything: The Big Bang to Big Data and for LLS 279: Mexican-American History in the spring. She enjoys teaching and hopes to become a professor one day.

“It's been really fun to get to know students and see them grow,” she said. “I've had some repeat students that I've been able to really see grow over an entire academic year, so that's really rewarding to see how well they're doing and just be part of their academic journey as well.”

In her free time, she enjoys singing, playing the ukelele, and piano. She also enjoys taking nature walks around Champaign-Urbana.

“I really like going to Meadowbrook Park and Busey Woods, it’s all really nice. I do really like seeing especially the wildflowers in Meadowbrook,” she said.

Her enjoyment of native plants also led to a collaboration with history professor Rosalyn LaPier and graduate student Andy Stec on a unique project. Last year, they co-wrote the article “ Indigenous Gardens Cultivate Healing ” for Yes! about movements to incorporate native plants into landscaping at universities in the US.

“That was really cool to be able to work with Roz and Andy on that,” she said. “It was my first time writing a piece like that for a general audience. We got to interview some people as well. It was a fantastic experience. Roz is awesome.”

The relationships she’s made along the way have been an important part of Eberhardt’s experience in the history department.  

“Grad school is really difficult, but I couldn't do it without meeting really wonderful people here. Like, really great friends, great partner. I feel really lucky that I get to be in grad school and am able to do work that I'm really passionate about because I know not everyone gets that opportunity. So I feel very lucky that I can do research and teach subjects that I'm genuinely really interested in.”