What is probability theory?

Probability theory is a division of the mathematics field that's focused on analyzing random or uncertain distributions and phenomena. It doesn't predict a specific outcome from the data that's offered, but it tells analysts several different potential outcomes. It does this by applying mathematical equations to predict the things that may happen as a result of the information. Probability theory offers a scientific process that can be used to make an educated guess as to the most likely outcome, or event.

Why is it important to learn about probability theory?

It's important to learn about probability theory because it can help improve your critical thinking skills. It can also help you make better decisions and help you with taking control of potential outcomes and risks. This can be beneficial in both your professional career and in your everyday life. If you're interested in a career in science, probability theory is a vital part of scientific reasoning that you'll use frequently in your career. It's also an interesting topic to learn about and to discuss with peers.

How can taking online courses on Coursera help me learn probability theory?

Taking online courses on Coursera can help you learn probability theory by presenting each step of the learning process in a logical order that you can pass through from beginner-level courses through intermediate and advanced courses. If you already have an understanding of data science and advanced math skills, you move right to the courses you need to upgrade or refresh your skills. You also can take the courses at your own pace, so you're never rushed, and you can make sure you have a solid understanding of each concept before progressing to the next.

How do I know if learning probability theory is right for me?

If you're a highly analytical person who enjoys mathematics, probability theory may be right for you. It's also right for you if your goal is to work in the field of data science, statistical analysis, or data manipulation. Additionally, it's a useful study area if you want to work in finance or at a high-level accounting job.

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834 resultados para "probability theory"

Gratis
Imperial College London
Mathematics for Machine Learning
Habilidades que obtendrás: Mathematics, Algebra, Linear Algebra, Machine Learning, Python Programming, Probability & Statistics, General Statistics, Calculus, Computer Programming, Applied Mathematics, Mathematical Theory & Analysis, Statistical Programming, Algorithms, Dimensionality Reduction, Regression, Theoretical Computer Science, Basic Descriptive Statistics, Data Analysis, Probability Distribution, Artificial Neural Networks, Computer Graphic Techniques, Computer Graphics, Computer Networking, Deep Learning, Differential Equations, Experiment, Machine Learning Algorithms, Network Model
4.6
(13.4k reseñas)
Beginner · Specialization · 3-6 Months
University of California San Diego
Combinatorics and Probability
Habilidades que obtendrás: Data Science, Computer Science, Probability & Statistics, Mathematics, Combinatorics, Data Analysis, Statistical Analysis, General Statistics, Bayesian Statistics, Computer Programming, Python Programming, Correlation And Dependence, Estimation, Probability Distribution, Computational Thinking, Theoretical Computer Science, Business Analysis, Computational Logic, Critical Thinking, Machine Learning, Markov Model, Mathematical Theory & Analysis, Research and Design, Statistical Programming, Strategy and Operations
4.6
(806 reseñas)
Beginner · Course · 1-3 Months
Gratis
Duke University
Introduction to Probability and Data with R
Habilidades que obtendrás: General Statistics, Probability & Statistics, Probability Distribution, Statistical Tests, Data Analysis, Statistical Analysis, Correlation And Dependence, Experiment, R Programming, Basic Descriptive Statistics, Bayesian Statistics, Data Mining, Plot (Graphics), Statistical Visualization, Data Analysis Software, Data Visualization, Exploratory Data Analysis, Statistical Programming
4.7
(5.4k reseñas)
Beginner · Course · 1-3 Months
Stanford University
Probabilistic Graphical Models
Habilidades que obtendrás: Probability & Statistics, Machine Learning, Bayesian Network, General Statistics, Markov Model, Bayesian Statistics, Probability Distribution, Computer Architecture, Distributed Computing Architecture, Leadership and Management, Other Programming Languages, Computer Programming, Machine Learning Algorithms, Statistical Machine Learning, Applied Machine Learning, Correlation And Dependence, Behavioral Economics, Business Psychology, Data Analysis, Graph Theory, Mathematics, Algebra, Geovisualization
4.6
(1.5k reseñas)
Advanced · Specialization · 3-6 Months
Stanford University
Probabilistic Graphical Models 1: Representation
Habilidades que obtendrás: Probability & Statistics, General Statistics, Machine Learning, Bayesian Network, Markov Model, Leadership and Management, Probability Distribution, Bayesian Statistics, Computer Programming, Behavioral Economics, Business Psychology, Data Analysis, Graph Theory, Machine Learning Algorithms, Mathematics, Other Programming Languages, Statistical Machine Learning
4.6
(1.4k reseñas)
Advanced · Course · 1-3 Months
New York University
Guided Tour of Machine Learning in Finance
Habilidades que obtendrás: Machine Learning, Finance, Machine Learning Algorithms, Algorithms, Theoretical Computer Science, Artificial Neural Networks, Business Analysis, Data Analysis, Deep Learning, Financial Analysis, Machine Learning Software, Tensorflow, Applied Machine Learning, Statistical Machine Learning
3.8
(644 reseñas)
Intermediate · Course · 1-4 Weeks
Stanford University
Probabilistic Graphical Models 2: Inference
Habilidades que obtendrás: Probability & Statistics, Bayesian Network, Machine Learning, Markov Model, Computer Architecture, Distributed Computing Architecture, Applied Machine Learning, Bayesian Statistics, Correlation And Dependence, General Statistics, Other Programming Languages, Probability Distribution, Geovisualization, Machine Learning Algorithms, Statistical Machine Learning
4.6
(478 reseñas)
Advanced · Course · 1-3 Months

Búsquedas relacionadas con probability theory

probability theory: foundation for data science

En síntesis, estos son los 10 cursos más populares probability theory

An Intuitive Introduction to Probability: University of Zurich
Probability Theory: Foundation for Data Science: University of Colorado Boulder
Data Science Foundations: Statistical Inference: University of Colorado Boulder
Mathematics for Machine Learning: Imperial College London
Probabilistic Graphical Models 3: Learning: Stanford University
Combinatorics and Probability: University of California San Diego
Data Science Math Skills: Duke University
Introduction to Probability and Data with R: Duke University
Probabilistic Graphical Models: Stanford University
Probabilistic Graphical Models 1: Representation: Stanford University

Preguntas frecuentes sobre Teoría de la probabilidad

Probability theory is a division of the mathematics field that's focused on analyzing random or uncertain distributions and phenomena. It doesn't predict a specific outcome from the data that's offered, but it tells analysts several different potential outcomes. It does this by applying mathematical equations to predict the things that may happen as a result of the information. Probability theory offers a scientific process that can be used to make an educated guess as to the most likely outcome, or event.‎
It's important to learn about probability theory because it can help improve your critical thinking skills. It can also help you make better decisions and help you with taking control of potential outcomes and risks. This can be beneficial in both your professional career and in your everyday life. If you're interested in a career in science, probability theory is a vital part of scientific reasoning that you'll use frequently in your career. It's also an interesting topic to learn about and to discuss with peers.‎
Taking online courses on Coursera can help you learn probability theory by presenting each step of the learning process in a logical order that you can pass through from beginner-level courses through intermediate and advanced courses. If you already have an understanding of data science and advanced math skills, you move right to the courses you need to upgrade or refresh your skills. You also can take the courses at your own pace, so you're never rushed, and you can make sure you have a solid understanding of each concept before progressing to the next.‎

Se puso a disposición este contenido de preguntas frecuentes solo con fines informativos. Se les pide a los estudiantes que realicen investigaciones adicionales para asegurarse de que los cursos y otras credenciales cumplan con sus objetivos personales, profesionales y financieros.