Coursework

Statistics-PhD Coursework

*The below list of requirements are for those students admitted to Fall 2021 or after. Students admitted prior to Fall 2021 are allowed to follow the below list or the previous program requirements.  

Prerequisites

  • MATH 447 - Real Variables (*Waived if a course at an equivalent level has been taken at another institution and a grade of B or above is achieved)

Required courses:

PhD applied regression courses  (8 hours) 

  • STAT 527 - Advanced Regression Analysis I (4 hours) - Qualifying Exam Course
  • STAT 528 - Advanced Regression Analysis II (4 hours) - Qualifying Exam Course

PhD theory core courses (12 hours)

  • STAT 511 - Advanced Mathematical Statistics (4 hours) - Qualifying Exam Course
  • STAT 575 - Large sample theory (4 hours) - Qualifying Exam Course
  • STAT 553 - Probability and Measure I (4 hours)

Practicum course: select one (0-4 hours)

  • STAT 427 - Statistical Consulting (4 hours)
  • STAT 593 - Internship (0-4 hours)
  • STAT 595 - Preparing Future Faculty (2 hours)

Computing-related course: select one (4 hours)

  • STAT 525 - Computational Statistics (4 hours)
  • STAT 542 - Statistical Learning (4 hours)
  • Approved Substitutions for Computing:
    • IE 521 - Convex Optimization (4 hours)
    • IE 534 - Deep Learning (4 hours)
    • CS 573 - Algorithms (4 hours)
    • CS 574 - Randomized Algorithms (4 hours)
    • CS 583 - Approximation Algorithms (4 hours)

Stochastic Processes and Time Series courses: select one (4 hours)

  • STAT 556 - Advanced Time Series Analysis (4 hours)
  • STAT 555/MATH 564 - Applied Stochastic Processes (4 hours)
  • STAT 533 – Advanced Stochastic Processes (4 hours)
  • STAT 554 - Probability and Measure II (4 hours)
  • STAT 576 - Weak Convergence and Empirical Processes (4 hours)

Select at least five elective courses with at least three 500 levels, not selected above (20 hours)

  • STAT 427 - Statistical Consulting (4 hours)
  • STAT 428 - Statistical Computing (4 hours)
  • STAT 429 - Time Series Analysis (4 hours)
  • STAT 431 – Applied Bayesian Analysis (4 hours)
  • STAT 433 - Stochastic processes (4 hours)
  • STAT 434 - Survival Analysis (4 hours)
  • STAT 448 - Advanced Data Analysis (4 hours)
  • STAT 466 - Image and Neuroimage Analysis (4 hours)
  • STAT 480 - Data Science Foundations (4 hours)
  • STAT 525 - Computational Statistics (4 hours)
  • STAT 530 - Bioinformatics (4 hours)
  • STAT 534 – Advanced Survival Analysis (4 hours)
  • STAT 538 - Clinical Trials Methodology (4 hours)
  • STAT 542 - Statistical Learning (4 hours)
  • STAT 545 – Spatial Statistics (4 hours)
  • STAT 546 – Machine Learning in Data Science (4 hours)
  • STAT 551 - Theory of Probability I (4 hours)
  • STAT 552 - Theory of Probability II (4 hours)
  • STAT 554 - Probability and Measure II (4 hours)
  • STAT 555 - Applied Stochastic Processes (4 hours)
  • STAT 571 - Multivariate Analysis (4 hours)
  • STAT 578 - Topics in Statistics (4 hours)
  • STAT 587 - Hierarchical Linear Models (4 hours)
  • STAT 588 - Covariance Structures and Factor Models (4 hours)
  • STAT 593 - Internship (0-4 hours)
  • STAT 595 - Preparing Future Faculty (2 hours)

Approved elective courses offered by other departments (other courses subject to approval by the PhD committee)

  • CS 512 - Data Mining Principles
  • CS 543 - Computer Vision
  • CS 546 - Machine Learning in NLP
  • CS 573 - Algorithms
  • CS 583 - Approximation Algorithms
  • ECE 547 - Topics in Image Processing
  • ECE 561 - Detection and Estimation Theory
  • ECE 563 - Information Theory
  • ECE 566 - Computational Inference and Learning
  • ECE 580 - Optimization by Vector Space Methods
  • ECON 536 - Applied Econometrics
  • ECON 574 - Econometrics I
  • ECON 575 - Econometrics II
  • ECON 576 - Time Series
  • ECON 590 - Applied Macroeconometrics
  • ECON 590 - Applied Financial Econometrics
  • IE 510 - Applied Nonlinear Programming
  • IE 521 - Convex Optimization
  • IE 528 - Computing for Data Analytics
  • IE 529 - Stats of Big Data & Clustering
  • MATH 540 - Real Analysis
  • MATH 580 - Combinatorial Mathematics
  • MATH 585 - Probabilistic Combinatorics
  • MATH 588 - Optimization in Networks
  • MATH 589 - Conjugate Duality and Optimization

Thesis and Individual study courses

  • STAT 590 - Individual Study and Research (0-16 hours, repeatable)
  • STAT 599 - Thesis Research (0-8 hours, repeatable) 

Total Hours: 96 Credit Hours for Stage 1 Admit; 64 Credit Hours for Stage 2 Admit

Entering with an approved Baccalaureate degree (Stage 1)
The above requirement applies. At least 52 required and elective course credits at UIUC.
Thesis research and individual study courses (min-max applied toward degree): 0-44
A student cannot deposit a thesis without record of registration in thesis research credit (599)

Total number of credits required: 96 (at least 64 residency credits)

Entering with an approved Master degree (Stage 2)
Entering with an approved MS degree in Statistics or related field from peer institution will make student eligible to waive STAT 527/STAT 528/STAT 511/STAT 575 (Qualifying Exam courses). PhD committee must approve prior degree after admission and before Sept. 1st of the first term of enrollment. Student must still pass Qualifying Exam after first year of enrollment.
At least 32 required and elective course credits at UIUC. Thesis research and individual study courses (min-max applied toward degree): 0-32. A student cannot deposit a thesis without record of registration in thesis research credit (599)

Total number of credits required: 64

Other Requirements: Other requirements may overlap

  • Minimum 500-level hours required 24
  • Qualifying Exam Yes
  • Preliminary Exam Yes
  • Final Exam/Dissertation Defense Yes
  • Dissertation Deposit Yes
  • Minimum GPA 3