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 faz parte do Programa de cursos integrados Data Science Foundations: Statistical Inference
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Programa de cursos integrados Data Science Foundations: Statistical InferenceUniversidade do Colorado em BoulderInformações sobre o curso
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Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível intermediário
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Aprox. 48 horas para completar
Inglês
Legendas: Inglês
O que você vai aprender
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 você terá
- Probability
- central limit theorem
- continuous random variables
- Bayes' Theorem
- discrete random variables
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível intermediário
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Aprox. 48 horas para completar
Inglês
Legendas: Inglês
oferecido por
Universidade do Colorado em Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
Comece a trabalhar rumo ao seu mestrado
Este curso é parte da graduação 100% on-line Master of Science in Data Science da Universidade do Colorado em Boulder.
Caso seja aceito para o programa completo, seus cursos contarão para sua graduação.
Programa - O que você aprenderá com este curso
9 horas para concluir
Descriptive Statistics and the Axioms of Probability
Understand the foundation 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 it’s fundamental importance for all of statistics and data science.
9 horas para concluir
3 vídeos (Total 51 mín.), 5 leituras, 3 testes
7 horas para concluir
Conditional Probability
The notion of “conditional probability” is a very useful concept from Probability Theory and in this module we introduce the idea of “conditioning” and Bayes’ Formula. The fundamental concept of “independent event” then naturally arises from the notion of conditioning. Conditional and independent events are fundamental concepts in understanding statistical results.
7 horas para concluir
2 vídeos (Total 52 mín.), 1 leitura, 3 testes
8 horas para concluir
Discrete Random Variables
The concept of a “random variable” (r.v.) is fundamental and often used in statistics. In this module we’ll study various named discrete random variables. We’ll learn some of their properties and why they are important. We’ll also calculate the expectation and variance for these random variables.
8 horas para concluir
4 vídeos (Total 78 mín.), 1 leitura, 3 testes
9 horas para concluir
Continuous Random Variables
In this module, we’ll extend our definition of random variables to include continuous random variables. The concepts in this unit are crucial since a substantial portion of statistics deals with the analysis of continuous random variables. We’ll begin with uniform and exponential random variables and then study Gaussian, or normal, random variables.
9 horas para concluir
4 vídeos (Total 97 mín.), 2 leituras, 3 testes
Avaliações
- 5 stars68,05%
- 4 stars16,66%
- 3 stars2,77%
- 2 stars2,77%
- 1 star9,72%
Principais avaliações do PROBABILITY THEORY: FOUNDATION FOR DATA SCIENCE
por PP17 de abr de 2022
Need to brush up integral calculus for thios course. Something I haven't looked at for 40 years.
por MB15 de jun de 2022
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.
por ES10 de out de 2021
The instructor is very good, more examples need to be added, there are mistakes in the evaluation
Sobre Programa de cursos integrados Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language.
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