Understand the foundations of probability and its relationship to statistics and data science. We’ll learn what it means to calculate a probability, independent and dependent outcomes, and conditional events. We’ll study discrete and continuous random variables and see how this fits with data collection. We’ll end the course with Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance for all of statistics and data science.
Este curso forma parte de Programa especializado: Data Science Foundations: Statistical Inference
Ofrecido Por


Acerca de este Curso
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Qué aprenderás
Explain why probability is important to statistics and data science.
See the relationship between conditional and independent events in a statistical experiment.
Calculate the expectation and variance of several random variables and develop some intuition.
Habilidades que obtendrás
- Probability
- central limit theorem
- continuous random variables
- Bayes' Theorem
- discrete random variables
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Ofrecido por
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Programa - Qué aprenderás en este curso
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Descriptive Statistics and the Axioms of Probability
Conditional Probability
Discrete Random Variables
Continuous Random Variables
Reseñas
- 5 stars75,51 %
- 4 stars12,24 %
- 3 stars2,04 %
- 2 stars2,04 %
- 1 star8,16 %
Principales reseñas sobre PROBABILITY THEORY: FOUNDATION FOR DATA SCIENCE
Need to brush up integral calculus for thios course. Something I haven't looked at for 40 years.
This is an excellent course to review foundational probability concepts. The instructor speaks clearly and goes through examples thoroughly for each concept.
This is a great course on probability. Although I felt like it was too easy and should include more PDFs (such as Beta and Gamma) and random variable transformations.
The instructor is very good, more examples need to be added, there are mistakes in the evaluation
Acerca de Programa especializado: Data Science Foundations: Statistical Inference

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