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Dibs Fall 2026

STATS

Statistics

95 courses on record · 3 with sections in Fall 2026

Live section data currently covers GE courses; the rest is filled in by a nightly pass, so this department is likely running more than is listed here.

95 courses

Lower division 7

Lower division courses in Statistics
Course Title Predicted GPA This term
10 Introduction to Statistical Reasoning GE 3.42 n=8,412 5 sections
12 Introduction to Statistical Methods for Geography and Environmental Studies GE 3.52 n=336 —
13 Introduction to Statistical Methods for Life and Health Sciences GE 3.50 n=5,861 5 sections
15 Introduction to Data Science GE 3.67 n=297 1 section
19 Fiat Lux Freshman Seminars no letter grades —
20 Introduction to Statistical Programming with R 3.60 n=2,644 —
21 Python and Other Technologies for Data Science 3.85 n=582 —

Upper division 31

Upper division courses in Statistics
Course Title Predicted GPA This term
100A Introduction to Probability 3.53 n=2,039 —
100B Introduction to Mathematical Statistics 3.27 n=1,287 —
100C Linear Models 3.49 n=1,137 —
101A Introduction to Data Analysis and Regression 3.66 n=1,530 —
101B Introduction to Design and Analysis of Experiment 3.53 n=1,244 —
101C Introduction to Statistical Models and Data Mining 3.77 n=1,035 —
102A Introduction to Computational Statistics with R 3.30 n=1,441 —
102B Introduction to Computation and Optimization for Statistics 3.55 n=1,363 —
102C Introduction to Monte Carlo Methods 3.30 n=968 —
112 Statistics: Window to Understanding Diversity 3.83 n=160 —
115 Probabilistic Decision Making 3.85 n=106 —
C116 Applied Bayesian Social Statistics 3.93 n=71 —
133 Introduction to Text Mining Using R 3.76 n=69 —
140XP Collaboration in Data Science 3.60 n=1 small sample —
141XP Practice of Statistical Consulting 3.92 n=908 —
143 Introduction to Research in Statistics 3.65 n=33 —
147 Data Technologies for Data Scientists 3.98 n=216 —
M148 Experience of Data Science 3.92 n=76 —
153 Hierarchical Linear Modeling no grade history —
C155 Applied Sampling 3.44 n=43 —
C161 Introduction to Machine Learning and Artificial Intelligence 3.86 n=96 —
C163 Generative Data Science 3.68 n=7 small sample —
167 Introduction to Databases 3.94 n=79 —
M169 Causality X 3.50 n=17 small sample —
170 Introduction to Time-Series Analysis 3.50 n=29 —
C173 Applied Geostatistics 3.61 n=97 —
C180 Introduction to Bayesian Statistics 3.68 n=27 —
C183 Statistical Models in Finance 3.62 n=143 —
184 Societal Impacts of Data 3.96 n=241 —
186 Careers in Statistics no letter grades —
199 Directed Research in Statistics 3.78 n=10 small sample —

Graduate 57

Graduate courses in Statistics
Course Title Predicted GPA This term
200A Applied Probability 3.74 n=133 —
200B Theoretical Statistics 3.73 n=100 —
200C High Dimensional Statistics 3.78 n=54 —
201A Research Design, Sampling, and Analysis 3.71 n=116 —
201B Statistical Modeling and Learning 3.94 n=118 —
201C Advanced Modeling and Inference 3.69 n=82 —
202A Statistics Programming 3.89 n=143 —
202B Matrix Algebra and Optimization 3.79 n=101 —
202C Monte Carlo Methods for Optimization 3.80 n=88 —
203 Large Sample Theory, Including Resampling 3.87 n=48 —
205 Hierarchical Linear Models 3.87 n=38 —
210 Computer Intensive Methods 3.78 n=19 small sample —
211 Topics in Economics and Machine Learning 3.91 n=42 —
212 Graphical Models 3.75 n=36 —
C216 Applied Bayesian Social Statistics 3.85 n=21 —
218 Statistical Analysis of Networks 3.88 n=26 —
219 Topics in Reinforcement Learning 3.79 n=14 small sample —
221 Time-Series Analysis 3.85 n=32 —
M222 Spatial Statistics 3.79 n=14 small sample —
M231A Pattern Recognition and Machine Learning 3.95 n=142 —
231B Methods of Machine Learning 3.90 n=56 —
232C Cognitive Artificial Intelligence 3.89 n=31 —
C236 Introduction to Bayesian Statistics 3.84 n=19 small sample —
240 Multivariate Analysis 3.83 n=16 small sample —
M243 Logic, Causation, and Probability 3.70 n=12 small sample —
M254 Statistical Methods in Computational Biology 3.91 n=95 —
256 Causality 3.93 n=79 —
C261 Introduction to Machine Learning and Artificial Intelligence 3.60 n=12 small sample —
C263 Generative Data Science 3.75 n=17 small sample —
C283 Statistical Models in Finance no grade history —
285 Seminar: Computing for Statistics no letter grades —
290 Current Literature in Statistics no letter grades —
291XP Service Learning for Graduate Statistical Consulting no grade history —
296 Participating Seminar: Statistics no letter grades —
400 Introduction to Probability Modeling 3.93 n=153 —
401 Survey of Methods in Modern Statistics 3.90 n=148 —
402 Applied Regression 3.92 n=154 —
403 Mathematical Statistics 3.78 n=139 —
404 Statistical Computing and Programming 3.96 n=153 —
405 Data Management 3.94 n=166 —
411 Multivariate Statistical Analysis 3.91 n=79 —
412 Advanced Regression and Predictive Modeling 3.64 n=78 —
413 Machine Learning 3.93 n=134 —
414 From Predictive Artificial Intelligence to Generative Artificial Intelligence 3.67 n=29 —
416 Applied Geostatistics 3.67 n=15 small sample —
417 Models in Finance 3.80 n=53 —
419 Experimental Design 3.64 n=58 —
420 Causal Inference 3.87 n=24 —
421 Advanced Statistical Communication 3.87 n=79 —
422 Data Visualization 3.89 n=38 —
425 Large Language Models in Text Mining 3.78 n=56 —
426 Deep Learning no grade history —
495A Teaching College Statistics no letter grades —
496 Statistics Internship no grade history —
497 Directed Individual Study or Research no grade history —
498 MAS Thesis Research no grade history —
596 Directed Individual Study or Research 3.96 n=103 —

“Predicted” is shrunk toward the department average, so a course taught once to a dozen students sits near the department mean rather than at whatever those twelve happened to get. The figure is always shown — small sample marks where it rests mostly on that prior.