Filter by
The language used throughout the course, in both instruction and assessments.
Results for "discrete+random+variables"
Status: FreeÉcole normale supérieure
Status: FreeEIT Digital
Skills you'll gain: Leadership and Management
University of Colorado Boulder
Skills you'll gain: General Statistics, Probability Distribution
Coursera Project Network
Skills you'll gain: BlockChain
Arizona State University
Skills you'll gain: Probability & Statistics, Experiment, Statistical Analysis, General Statistics, Statistical Tests
Johns Hopkins University
Skills you'll gain: Data Analysis, General Statistics, Basic Descriptive Statistics, Data Visualization, Exploratory Data Analysis, Probability & Statistics, Research and Design, Statistical Analysis, Statistical Tests
Status: FreeUniversity of Minnesota
Skills you'll gain: Algebra, Linear Algebra, Mathematics, Problem Solving
Coursera Project Network
Skills you'll gain: Probability Distribution, R Programming
Status: FreeThe Chinese University of Hong Kong
Skills you'll gain: Leadership and Management
Databricks
Skills you'll gain: General Statistics, Probability & Statistics
University of Minnesota
Skills you'll gain: Decision Making, Probability & Statistics, Probability Distribution, Statistical Analysis, Data Analysis, General Statistics
Status: FreeThe University of Melbourne
In summary, here are 10 of our most popular discrete+random+variables courses
- Statistical Mechanics: Algorithms and Computations: École normale supérieure
- Quantitative Model Checking:Â EIT Digital
- Managing, Describing, and Analyzing Data:Â University of Colorado Boulder
- TypeScript Variables and Data Types:Â Coursera Project Network
- Random Models, Nested and Split-plot Designs:Â Arizona State University
- Data – What It Is, What We Can Do With It: Johns Hopkins University
- Matrix Methods:Â University of Minnesota
- Using probability distributions for real world problems in R:Â Coursera Project Network
- 离散优化建模基础篇 Basic Modeling for Discrete Optimization: The Chinese University of Hong Kong
- Bayesian Inference with MCMC:Â Databricks
