ADMISSION REQUIREMENTS
1. PREREQUISITE KNOWLEDGE (pick one)
Mathematics:
408C Calculus I
408L Integral Calculus
408Q Dierential and Integral Calculus for Business
408R Calculus for Biologists
408S Integral Calculus
2. MATHEMATICAL FOUNDATION OF
STATISTICS (pick one)
Biomedical Engineering
335 Engineering Probability & Sttistics
Electrical Engineering
351K Probability and Random Processes
Mathematics
362K Probability I
Statistics and Data Sciences
321 Intro to Probability & Statistics
3. APPLIED STATISTICS COURSE 1 (pick one)
Economics
329 Economic Statistics
Educational Psychology
371 Intro to Statistics
Government
350K Statistical Analysis in Political Science
Mathematics
358K Applied Statistics
Psychology
418 Statistics & Research Design
420M Psychological Methods and Statistics
Sociology
317L Intro to Social Statistics
Statistics
309 Elementary Business Statistics
Statistics and Data Sciences
302 Data Analysis for the Health Sciences
302F Foundations of Statistics
304 Statistics in Health Care
306 Statistics in Market Analysis
320E Elements of Statistics
320H Elements of Statistics Honors
328M Biostatistics
4. APPLIED STATISTICS COURSE 2 (pick one)
Economics
441K Intro to Econometrics
Mathematics
349R Applied Regression
Statistics (majors only)
371G/H Statistics & Modeling/Honors
375/H Statistics and Modeling for Finance/Honors
Statistics and Data Sciences
324E Elements of Regression Analysis
325H Honors Statistics
332 Statistical Models for the Health & Behavioral
Sciences
352 Statistical Modeling
358.1 Applied Regression
5. ELECTIVES (pick three)
Students are encouraged to select courses
within their own majors or colleges as
appropriate. The Statistics and Data Sciences
courses are available to students in all majors.
Advertising
344K Advertising Research
Communication Studies
348 Communication Research Methods
Computer Science
342 Neural Networks
343 Articial Intelligence
363D Introduction to Data Mining
Economics
348K.1 Advanced Econometrics
354K Intro to Game Theory
Electrical Engineering
461P Data Science Principles
Geological Sciences
325K Computational Methods
365N Seismic Data Processing
Health Education
343 Foundations of Epidemiology
373 Evaluation & Research Design
Kinesiology
376 Measurement in Kinesiology
Linguistics
350.15 Computational Semantics
Mathematics
339J Probability Models with Actuarial Applications
349P Actuarial Statistical Estimate
362M Introduction to Stochastic Processes
378K Introduction to Mathematical Statistics
378P or SDS 378P Decision Analytics
Management Information Systems
373.11 Advanced Analytics Programming
373.17 Data Mining for Business
Petroleum and Geosystems Engineering
378 Applied Reservoir Characterization
Psychology
325K Advanced Statistics
Public Health
354 Epidemiology
Statistics
372.5: Financial and Econometric Time Series
Modeling
Statistics and Data Sciences
322E Elements of Data Science
323 Statistical Learning and Inference
348 Computational Biology & Bioinformatics
353 Advanced Multivariate Methods
374E Visualization & Data Analysis
375 Data Viz in R
378 Intro to Mathematical Statistics
378P or M 378P Decision Analytics
379R Undergraduate Research
Certicate in Applied Statistical Modeling Course
Progression Worksheet 2022–2024 Catalog
Course(s)
Fullled
Course(s)
Fullled
Certicate in Applied Statistical Modeling Course
Progression Worksheet 2022–2024 Catalog
POLICIES & PROCEDURES
• Total of 18 hours required (six courses in sections II.-V below) must be completed with a grade of C or higher with a cumulative
grade point average of at least 3.0 across all courses used to fulll the certicate (excluding prerequisites).
• No transfer credit or credit-b-exam may be used to fulll certicate course requirements (excluding prerequisite).
• Not all courses listed in this document are oered every semester. See UT course schedule for available class oerings.
• Please visit the certicate website for how to enroll:
stat.utexas.edu/undergraduate/certicate-in-applied-statistical-modeling