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.
Ce cours fait partie de la Spécialisation Data Science Foundations: Statistical Inference
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Spécialisation Data Science Foundations: Statistical InferenceUniversité du Colorado à BoulderÀ propos de ce cours
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Cours 1 sur 3 dans le
Niveau intermédiaire
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Approx. 48 heures pour terminer
Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
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.
Compétences que vous acquerrez
- Probability
- central limit theorem
- continuous random variables
- Bayes' Theorem
- discrete random variables
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 3 dans le
Niveau intermédiaire
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Approx. 48 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Université du Colorado à 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.
Commencez à travailler pour obtenir votre master
Ce cours fait partie du diplôme intégralement en ligne Master of Science in Data Science de Université du Colorado à Boulder.
Si vous êtes admis(e) au programme complet, vos cours sont pris en compte dans votre apprentissage diplômant.
Programme de cours : ce que vous apprendrez dans ce cours
9 heures pour terminer
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 heures pour terminer
3 vidéos (Total 51 min), 5 lectures, 3 quiz
7 heures pour terminer
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 heures pour terminer
2 vidéos (Total 52 min), 1 lecture, 3 quiz
8 heures pour terminer
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 heures pour terminer
4 vidéos (Total 78 min), 1 lecture, 3 quiz
9 heures pour terminer
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 heures pour terminer
4 vidéos (Total 97 min), 2 lectures, 3 quiz
Avis
- 5 stars67,14 %
- 4 stars17,14 %
- 3 stars2,85 %
- 2 stars2,85 %
- 1 star10 %
Meilleurs avis pour PROBABILITY THEORY: FOUNDATION FOR DATA SCIENCE
par PP17 avr. 2022
Need to brush up integral calculus for thios course. Something I haven't looked at for 40 years.
par MB15 juin 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.
par ES10 oct. 2021
The instructor is very good, more examples need to be added, there are mistakes in the evaluation
À propos du Spécialisation 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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