Coursework Requirements for Ph.D. Minor and Graduate Certificate in Statistics & Data Science

Complete list of courses, including course descriptions, prerequisites, and semesters offered.

 

A minimum of 12 units of coursework (graded B or better) are required for the Minor and the Certificate (options are listed below).  Please note, students who do not receive a B or better grade for their minor coursework may instead have an overall 3.0 GPA for minor coursework & pass the qualifying exam, theory version, at the MS level.  In any case a B is required for STAT 566 or the student must pass the theory portion of the Qualifying Exam with at least a MS Pass.

1.  Core Statistical Theory Course; 3 units as follows:

            STAT 566 – Theory of Statistics (available online) - A minimum grade of B is required.

NOTE:  The prerequisite for STAT 566 is STAT 564 (Theory of Probability).  STAT 564 is only offered in the fall semester and STAT 566 is only offered in the spring semester. 

2.  Additional Elective Courses; minimum 9 units from any of the following.  This is not a complete list.  For a comlete list see the coursework spreadsheet:

  • ANS 513/GENE 513 – Statistical Genetics for Quantitative Measures
  • AREC 517/ECON 517 – Introductory Mathematical Statistics for Economists
  • BIOS 576B - Biostatistics for Research (available online)                       
  • BIOS 576C – Applied Biostatistics Analysis
  • BOS 576D – Data Management and the SAS Programming Language
  •  BIOS 647 – Analysis of Categorical Data, or
  •       STAT 574C/SOC 574C – Categorical Data Analysis
  •  BIOS 648 – Analysis of High Dimensional Data
  •  BIOS 675 – Clinical Trials and Intervention Studies
  •  BIOS 684 – General Linear and Mixed Effects Models, or
  •        FSHD 617C – Advanced Data Analysis: Multilevel Modeling  
  •  BIOS 686 – Survival Analysis
  •  BIOS 696S – Biostatistics Seminar*
  •  CSC 580 Principles of Machine Learning
  •  ECE 523 Engineering Applications of Machine Learning and Data Analytics
  •  ECE 639 – Detection and Estimation in Engineering Systems (available online)         
  •  ECOL 518 – Spatio-Temporal Ecology
  •  ECON 518 – Introduction to Econometrics
  •   ECON 520 – Theory of Quantitative Methods in Economics
  •   ECON 522A – Econometrics, or
  •         AREC 559 – Advanced Applied Econometrics
  •   ECON 522B – Econometrics
  •   ECON 549 – Applied Econometric Analysis
  •   EDP 558 – Educational Tests and Measurements
  •   EDP 646A – Multivariate Methods in Educational Research
  •   EDP 658A – Theory of Measurement           
  •   EDP 658B – Theory of Measurement
  •   FSHD 617A – Advanced Data Analysis: Structural Equation Modeling
  •   FSHD 617B – Advanced Data Analysis: Dyadic Data Analysis
  •   FSHD 617C – Advanced Data Analysis: Multilevel Modeling
  •    GEOG 585A – Applied Time Series Analysis
  •            or STAT 574T – Time Series Analysis
  •    HWRS 655 – Stochastic Methods in Surface Hydrology
  •    INFO 521 - Introduction to Machine Learning
  •    LING 539 – Statistical Natural Language Processing
  •    LING 582 – Advanced Statistical Natural Language Processing
  •    MATH 529 – Multivariate Analysis
  •    MATH 543 – Theory of Graphs and Networks
  •    MATH 565A – Stochastic Processes
  •    MATH 565B – Stochastic Processes
  •    MATH 565C – Stochastic Differential Equations
  •    MATH 574M – Statistical Machine Learning
  •    MATH 575A – Numerical Analysis
  •    MATH 577 – Monte Carlo Methods
  •    MCB 516A – Statistical Bioinformatics and Genomic Analysis
  •    MGMT 582D – Multivariate Analysis in Management
  •    MIS 545 – Data Mining for Business Intelligence (available online)       
  •    NURS 646 – Healthcare Informatics: Theory and Practice (available online)
  •    OPTI 637 – Principles of Image Science
  •    PHYS 528 – Statistical Mechanics
  •    PLS 565 – Practical Skills for Next Generation Sequencing Data Analysis
  •    PSY 507B – Statistical Methods in Psychological Research
  •    PSY 507C – Research Design & Analysis of Variance
  •    PSY 597G – Graphical Exploratory Data Analysis
  •    RNR 520 – Advanced Geographic Information Systems          
  •    SIE 520 – Stochastic Modeling I (available online)
  •    SIE 522 – Engineering Decision Making Under Uncertainty (available online)
  •    SIE 525 – Queuing Theory (available online)
  •    SIE 531 – Simulation Modeling and Analysis (available online)
  •    SIE 536 – Experiment Design and Regression (available online)**
  •         or STAT 571B – Design of Experiments (available online)
  •    SIE 545 – Fundamentals of Optimization (available online)
  •    SIE 606 – Advanced Quality Engineering (available online)
  •    SOC 570B – Social Statistics
  •    STAT 563 – Probability Math            
  •    STAT 564 – Theory of Probability (available online)
  •    STAT 567A – Theoretical Statistics I
  •    STAT 567B – Theoretical Statistics II
  •    STAT 571A – Advanced Statistical Regression Analysis (available online)
  •    STAT 571B – Design of Experiments (available online)
  •           or SIE 536 – Experiment Design and Regression (available online)**
  •    STAT 574B – Bayesian Statistical Theory and Applications
  •    STAT 574C – Categorical Data Analysis
  •    STAT 574E – Environmental Statistics
  •    STAT 574G – Introduction to Geostatistics
  •    STAT 574S – Survey Sampling
  •    STAT 574T – Time Series Analysis
  •         or GEOS 585A – Applied Time Series Analysis
  •    STAT 579 – Spatial Statistics and Spatial Econometrics
  •    STAT 675 – Statistical Computing
  •    STAT 687 – Theory of Linear Models
  •    STAT 688 – Statistical Consulting***
  •    STAT 696E – Econometric Modeling I

 

*A maximum of 3 units of Biostatistics Seminar (BIOS 696S) may be applied towards the Elective Certificate/PhD Minor course requirements.

**If you plan to continue on to the Statistics MS or PhD programs at the University of Arizona, you must take STAT 571B, not SIE 536.

***A maximum of 3 units of Statistical Consulting (STAT 688) may be applied towards the Elective Certificate/PhD Minor course requirements.

Changes to the PhD Minor in Statistics

Where needed to suit a particular or specialized need in an individual student’s curriculum plan, petition may be made to the GIDP Executive Committee through the GIDP Chair for approval of a course not listed above for use as an Elective Course.  The decision of the committee will be final.  In no case, however, will a prerequisite course for any Elective Course be considered for such special approval if it is not already listed as an approved course, nor may a course be used to satisfy both a major degree requirement and a requirement for the PhD Minor in Statistics.