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
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Programa especializado: Data Science Foundations: Statistical InferenceUniversidad de Colorado en BoulderAcerca de este Curso
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Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Aprox. 48 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
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
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Aprox. 48 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
Universidad de Colorado en 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.
Comienza a trabajar para obtener tu maestría
Este curso es parte del Master of Science in Data Science completamente en línea de Universidad de Colorado en Boulder.
Si te aceptan en el programa completo, tus cursos cuentan para tu título de grado.
Programa - Qué aprenderás en este curso
9 horas para completar
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 completar
3 videos (Total 51 minutos), 5 lecturas, 3 cuestionarios
7 horas para completar
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 completar
2 videos (Total 52 minutos), 1 lectura, 3 cuestionarios
8 horas para completar
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 completar
4 videos (Total 78 minutos), 1 lectura, 3 cuestionarios
9 horas para completar
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 completar
4 videos (Total 97 minutos), 2 lecturas, 3 cuestionarios
Reseñas
- 5 stars66,17 %
- 4 stars17,64 %
- 3 stars2,94 %
- 2 stars2,94 %
- 1 star10,29 %
Principales reseñas sobre 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 oct. de 2021
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
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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