STATS 100A · STAT 100AIntroduction to Probability
Statistics · 4 units · Undergraduate upper division (100-199)
Not open to students with credit for Electrical Engineering 131A or Mathematics 170A; open to graduate students. Students may receive credit for only two of following: course 100A, former course 110A, Biostatistics 100A. Probability distributions, random variables, vectors, and expectation.
P/NP or letter grading.
When it runs
Checking the Schedule of Classes…
Requisites
Official UCLA wording
Requisites: Mathematics 32B, 33A.
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
STATS 100A21 more beyond
- STATS C151Experimental Design
- STATS 170Introduction to Time-Series Analysis
- BIOMATH M257Computational Methods for Biostatistical Research
- STATS C173Applied Geostatistics
- STATS M230Statistical Computing
- BIOMATH M280Statistical Computing
- BIOSTAT M257Computational Methods for Biostatistical Research
- STATS 102BIntroduction to Computation and Optimization for Statistics
- ECON 109CAdvanced Sequence: Computational Economics
- ECON 109DAdvanced Sequence: Econometric Theory
- ECON 109DLAdvanced Sequence: Econometric Theory Laboratory
- ECON 109EAdvanced Sequence: Applied Empirical Economics
- STATS 102CIntroduction to Monte Carlo Methods
- HUM GEN M207ATheoretical Genetic Modeling
- STATS 140XPCollaboration in Data Science
- STATS 141XPPractice of Data Science
- STATS 143Introduction to Research in Statistics
- STATS 105Statistics for Engineers
- BIOINFO M223Statistical Methods in Computational Biology
- STATS 115Probabilistic Decision Making
- STATS C116Applied Bayesian Social Statistics
- BIOMATH M271Statistical Methods in Computational Biology
- STATS C160Causal Inference for Health Data
- STATS 201BStatistical Modeling and Learning
- STATS 201CAdvanced Modeling and Inference
- STATS 206Modern Survey Methods
- STATS 211Topics in Economics and Machine Learning
- STATS 213Synthetic Data Generation
- STATS C263Generative Data Science
- STATS 212Graphical Models
- STATS 219Topics in Reinforcement Learning
- STATS 231CTheories of Machine Learning
- STATS C236Introduction to Bayesian Statistics
- BIOSTAT 125The Science of Why: Causal Inference for Public Health
- STATS C216Applied Bayesian Social Statistics
- STATS 238Vision as Bayesian Inference
- C&S BIO M175Stochastic Processes in Biochemical Systems
- STATS M254Statistical Methods in Computational Biology
- CH ENGR M148Introduction to Data Science
- CHEM M186Stochastic Processes in Biochemical Systems
38 courses list this as a requisite. Showing 53 courses over 3 levels; the branches marked with a count carry on past it. Every course here opens its own tree.