Why is it important to learn about probability?

It's important to learn about probability if you are interested in gambling, statistics, advanced mathematics, or data science. Probability is the understanding of the likelihood of something happening, so it is part of many careers that use data analysis or planning. It is a key part of financial analysis, statistical analysis, social sciences, and medical research. Many topics that cover probability are in computer science, but not all are. Understanding probability can help you solve the data problems faced in your organization. It can also help you understand why things happen in the world.

What are typical careers that use probability?

Typical careers that use probability are those that involve numbers. Probability can help a manager build a strategic plan, a scientist understand research findings, or a politician understand polling data. Some aspects of the study of probability are of use to almost anyone who has a basic understanding of math, while others are deeply theoretical. Those working in machine learning, data science, or statistical analysis will need a deep understanding of probability. When you learn probability, you will be able to understand what sets of numbers show. This is useful in analysis, exploratory data analysis, or for building probabilistic models. It is also a part of many games, so it can be useful outside of your career.

How can online courses on Coursera help me learn probability?

Online courses on Coursera can help you learn probability no matter your current level of math skills. Some cover an intuitive approach for beginners, while others look at statistical theories. There are classes that use specific computer languages or that apply to specific industries. For example, someone working in the social sciences may want to take different courses than someone working in machine learning. Courses include lectures, readings, and projects so that you can apply what you learn. You will have the opportunity to use data sets and to apply your learning to what you see at work or at school. Some courses stand alone, and others are part of Specializations and Professional Certificates.

Обзор
Probability

Фильтр

Результатов для запроса ''probability': 1131

Бесплатно
University of Zurich
An Intuitive Introduction to Probability
Получаемые навыки: Data Analysis, General Statistics, Studentized Residual, Randomness, Bayesian Statistics, Bayesian Network, Probability Distribution, Basic Descriptive Statistics, Bayesian, Chi-Squared Distribution, Probability & Statistics, Probability, Machine Learning
4.8
(рецензий: 1.4k)
Beginner · Course · 1-3 Months
Imperial College London
Mathematics for Machine Learning
Получаемые навыки: Algebra, Algorithms, Analysis, Artificial Neural Networks, Basic Descriptive Statistics, Calculus, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Analysis, Deep Learning, Differential Equations, General Statistics, Lambda Calculus, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Matrices, Probability & Statistics, Probability Distribution, Python Programming, Regression, Statistical Programming, Theoretical Computer Science
4.6
(рецензий: 12.8k)
Beginner · Specialization · 3-6 Months
Бесплатно
Stanford University
Introduction to Statistics
Получаемые навыки: Statistical Tests, Statistical Analysis, Correlation And Dependence, Plot (Graphics), Econometrics, Data Analysis, Probability Distribution, Data Visualization, General Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Machine Learning, Probability & Statistics, Statistical Inference, Markov Model, Regression, Experiment, Basic Descriptive Statistics, Probability
4.5
(рецензий: 1.3k)
Beginner · Course · 1-3 Months
University of California, Santa Cruz
Bayesian Statistics
Получаемые навыки: Analysis, Bayesian, Bayesian Statistics, Business Analysis, Data Analysis, Econometrics, Estimation, Forecasting, General Statistics, Graph Theory, Inference, Machine Learning, Markov Model, Mathematics, Probability & Statistics, Probability Distribution, R Programming, Regression, Statistical Analysis, Statistical Machine Learning, Statistical Programming
4.6
(рецензий: 3.2k)
Intermediate · Specialization · 3-6 Months
Бесплатно
University of London
Probability and Statistics: To p or not to p?
Получаемые навыки: Data Structures, Entrepreneurship, Studentized Residual, General Statistics, Decision Making, Markov Model, Probability Distribution, Hypothesis, Machine Learning, Probability, Data Management, Statistical Hypothesis Testing, Probability & Statistics, Theoretical Computer Science, Hypothesis Testing, Leadership and Management, Experiment
4.6
(рецензий: 1.3k)
Beginner · Course · 1-3 Months
University of Pennsylvania
The Materiality of ESG Factors
Получаемые навыки: Accounting, Audit, Budget Management, Business Analysis, Business Psychology, Business Research, Change Management, Collaboration, Communication, Corporate Bond, Critical Thinking, Design and Product, Entrepreneurship, Finance, Human Resources, Investment Management, Leadership Development, Leadership and Management, Marketing, Organizational Development, Packaging and Labeling, Probability & Statistics, Product Lifecycle, Project Management, Regulations and Compliance, Research and Design, Risk, Risk Management, Sales, Social Media, Strategy, Strategy and Operations
4.7
(рецензий: 276)
Beginner · Specialization · 3-6 Months
University of Pennsylvania
Business and Financial Modeling
Получаемые навыки: Accounting, Accounts Payable and Receivable, Analysis, Basic Descriptive Statistics, Business Analysis, Cash Management, Corporate Accouting, Correlation And Dependence, Data Analysis, Data Analysis Software, Data Management, Data Model, Decision Making, Design and Product, Entrepreneurship, Finance, Financial Accounting, Financial Analysis, Forecasting, General Statistics, Investment Management, Leadership and Management, Linearity, Market Research, Marketing, Mathematics, Microsoft Excel, Modeling, Performance Management, Probability & Statistics, Probability Distribution, Problem Solving, Product Management, Product Marketing, Project, Regression, Research and Design, Risk Management, Sales, Spreadsheet Software, Statistical Analysis, Statistical Tests, Strategy, Strategy and Operations
4.5
(рецензий: 9.8k)
Beginner · Specialization · 3-6 Months
Stanford University
Probabilistic Graphical Models
Получаемые навыки: Bayesian, Bayesian Network, Bayesian Statistics, Behavioral Economics, Business Psychology, Computer Architecture, Computer Programming, Data Analysis, Decision Making, Distributed Computing Architecture, Entrepreneurship, Feature Engineering, General Statistics, Graph Theory, Leadership and Management, Machine Learning, Markov Model, Mathematics, Modeling, Other Programming Languages, Probability & Statistics, Probability Distribution
4.6
(рецензий: 1.5k)
Advanced · Specialization · 3-6 Months
Johns Hopkins University
Advanced Statistics for Data Science
Получаемые навыки: Algebra, Artificial Neural Networks, Bayesian Statistics, Biostatistics, Calculus, Communication, Dimensionality Reduction, Econometrics, Experiment, General Statistics, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematics, Probability & Statistics, Probability Distribution, Regression, Statistical Machine Learning, Statistical Tests
4.4
(рецензий: 660)
Advanced · Specialization · 3-6 Months
Бесплатно

Результаты поиска, связанного с probability

probability and statistics

probability theory

probability and statistics: to p or not to p?

probability theory: foundation for data science

using probability distributions for real world problems in r

introduction to probability and data with r

combinatorics and probability

an intuitive introduction to probability

10 самых популярных курсов по запросу probability

An Intuitive Introduction to Probability: University of Zurich
Mathematics for Machine Learning: Imperial College London
Introduction to Statistics: Stanford University
Probability Theory: Foundation for Data Science: University of Colorado Boulder
Bayesian Statistics: University of California, Santa Cruz
Probability and Statistics: To p or not to p?: University of London
The Materiality of ESG Factors: University of Pennsylvania
Data Science Foundations: Statistical Inference: University of Colorado Boulder
Business and Financial Modeling: University of Pennsylvania
Probabilistic Graphical Models: Stanford University

Часто задаваемые вопросы на тему Вероятность

It's important to learn about probability if you are interested in gambling, statistics, advanced mathematics, or data science. Probability is the understanding of the likelihood of something happening, so it is part of many careers that use data analysis or planning. It is a key part of financial analysis, statistical analysis, social sciences, and medical research. Many topics that cover probability are in computer science, but not all are. Understanding probability can help you solve the data problems faced in your organization. It can also help you understand why things happen in the world.‎
Typical careers that use probability are those that involve numbers. Probability can help a manager build a strategic plan, a scientist understand research findings, or a politician understand polling data. Some aspects of the study of probability are of use to almost anyone who has a basic understanding of math, while others are deeply theoretical. Those working in machine learning, data science, or statistical analysis will need a deep understanding of probability. When you learn probability, you will be able to understand what sets of numbers show. This is useful in analysis, exploratory data analysis, or for building probabilistic models. It is also a part of many games, so it can be useful outside of your career.‎
Online courses on Coursera can help you learn probability no matter your current level of math skills. Some cover an intuitive approach for beginners, while others look at statistical theories. There are classes that use specific computer languages or that apply to specific industries. For example, someone working in the social sciences may want to take different courses than someone working in machine learning. Courses include lectures, readings, and projects so that you can apply what you learn. You will have the opportunity to use data sets and to apply your learning to what you see at work or at school. Some courses stand alone, and others are part of Specializations and Professional Certificates.‎

Часто задаваемые вопросы предоставляются в ознакомительных целях. Учащимся рекомендуется дополнительно убедиться в том, что интересующие их курсы и другие материалы соответствуют их личным, профессиональным и финансовым потребностям.