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The language used throughout the course, in both instruction and assessments.
Results for "reproducibility"
Microsoft
Johns Hopkins University
Skills you'll gain: Data Analysis, Probability & Statistics, Strategy
University of Amsterdam
Skills you'll gain: Probability & Statistics, Statistical Tests, General Statistics, Correlation And Dependence, R Programming, Regression, Statistical Programming, Experiment
Johns Hopkins University
Skills you'll gain: General Statistics, Probability & Statistics, Mathematics, Statistical Tests, Linear Algebra, Algebra, Regression, Statistical Analysis, Biostatistics, Probability Distribution, Bayesian Statistics, Correlation And Dependence, Estimation, Basic Descriptive Statistics, Mathematical Theory & Analysis
Skills you'll gain: Algorithms, Machine Learning, Machine Learning Algorithms, Dimensionality Reduction, Applied Machine Learning, Human Learning, Statistical Machine Learning
Coursera Project Network
Skills you'll gain: Python Programming
Skills you'll gain: Artificial Neural Networks, Data Analysis, Data Science
Status: FreeColumbia University
Skills you'll gain: Mathematics
Stanford University
Skills you'll gain: Bayesian Network, General Statistics, Probability & Statistics, Graph Theory, Bayesian Statistics, Markov Model
Coursera Project Network
Skills you'll gain: Data Science, Software Engineering
Status: FreeColumbia University
University of Colorado Boulder
Skills you'll gain: Dimensionality Reduction
Searches related to reproducibility
In summary, here are 10 of our most popular reproducibility courses
- Generative AI for Data Science:Â Microsoft
- Fundamentals of Scientific Research Under Uncertainty:Â Johns Hopkins University
- Inferential Statistics:Â University of Amsterdam
- Advanced Statistics for Data Science:Â Johns Hopkins University
- Unsupervised Machine Learning:Â IBM
- Interpretable Machine Learning Applications: Part 1:Â Coursera Project Network
- Generative AI for Data Scientists:Â IBM
- Causal Inference 2:Â Columbia University
- Probabilistic Graphical Models 2: Inference:Â Stanford University
- Interpretable machine learning applications: Part 5:Â Coursera Project Network
