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.

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1215 Ergebnisse für „probability“

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University of Zurich
An Intuitive Introduction to Probability
Kompetenzen, die Sie erwerben: Data Analysis, Machine Learning, Studentized Residual, Basic Descriptive Statistics, General Statistics, Probability & Statistics, Probability Distribution, Probability, Bayesian Network, Randomness, Bayesian Statistics, Bayesian, Chi-Squared Distribution
4.8
(1.4k Bewertungen)
Beginner · Course · 1-3 Months
Kostenlos
Stanford University
Introduction to Statistics
Kompetenzen, die Sie erwerben: Data Analysis, Experiment, Basic Descriptive Statistics, Econometrics, Machine Learning, Plot (Graphics), Data Visualization, Statistical Tests, Statistical Analysis, Probability Distribution, Regression, General Statistics, Probability & Statistics, Markov Model, Probability, Bayesian Statistics, Inference, Hypothesis, Correlation And Dependence, Hypothesis Testing
4.5
(1.4k Bewertungen)
Beginner · Course · 1-3 Months
University of California, Santa Cruz
Bayesian Statistics
Kompetenzen, die Sie erwerben: Advertising, Analysis, Bayesian, Bayesian Statistics, Business Analysis, Communication, Data Analysis, Econometrics, Estimation, Forecasting, General Statistics, Graph Theory, Inference, Machine Learning, Marketing, Markov Model, Mathematics, Probability, Probability & Statistics, Probability Distribution, R Programming, Regression, Statistical Analysis, Statistical Machine Learning, Statistical Programming
4.6
(3.2k Bewertungen)
Intermediate · Specialization · 3-6 Months
Imperial College London
Mathematics for Machine Learning
Kompetenzen, die Sie erwerben: Algebra, Algorithms, Analysis, Artificial Neural Networks, Basic Descriptive Statistics, Calculus, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Analysis, Deep Learning, Differential Equations, Dimensionality Reduction, General Statistics, Lambda Calculus, Linear Algebra, Linear Regression, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Probability Distribution, Python Programming, Regression, Statistical Programming, Theoretical Computer Science
4.6
(12.9k Bewertungen)
Beginner · Specialization · 3-6 Months
Kostenlos
University of London
Probability and Statistics: To p or not to p?
Kompetenzen, die Sie erwerben: Experiment, Data Management, Leadership and Management, Data Structures, Entrepreneurship, Machine Learning, Decision Making, Markov Model, Theoretical Computer Science, Probability Distribution, General Statistics, Probability & Statistics, Statistical Hypothesis Testing, Hypothesis, Hypothesis Testing, Probability, Studentized Residual
4.6
(1.3k Bewertungen)
Beginner · Course · 1-3 Months
University of Pennsylvania
Business and Financial Modeling
Kompetenzen, die Sie erwerben: Accounting, Accounts Payable and Receivable, 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, Market Research, Marketing, Mathematics, Microsoft Excel, Performance Management, Probability, Probability & Statistics, Probability Distribution, Problem Solving, Product Management, Product Marketing, Project, Regression, Research and Design, Risk, Risk Management, Sales, Spreadsheet Software, Statistical Analysis, Statistical Tests, Strategy, Strategy and Operations
4.5
(9.9k Bewertungen)
Beginner · Specialization · 3-6 Months
Johns Hopkins University
Data Science
Kompetenzen, die Sie erwerben: Algorithms, Application Development, Applied Machine Learning, Business Analysis, Computer Programming, Data Analysis, Data Clustering Algorithms, Data Management, Data Visualization, Econometrics, Exploratory Data Analysis, Extract, Transform, Load, General Statistics, Interactive Data Visualization, Interactivity, Knitr, Machine Learning, Machine Learning Algorithms, Natural Language Processing, Plot (Graphics), Probability, Probability & Statistics, Probability Distribution, R Programming, Regression, Software Engineering Tools, Statistical Analysis, Statistical Programming, Statistical Tests, Theoretical Computer Science, Web Application
4.5
(49.3k Bewertungen)
Beginner · Specialization · 3-6 Months
Kostenlos
The Hong Kong University of Science and Technology
Python and Statistics for Financial Analysis
Kompetenzen, die Sie erwerben: Statistical Analysis, Computer Programming, Finance, Financial Analysis, Data Analysis, General Statistics, Python Programming, Probability & Statistics, Statistical Tests, Statistical Programming, Probability Distribution, Accounting, Analysis, Business Analysis, Financial Data Analysis
4.4
(3.3k Bewertungen)
Intermediate · Course · 1-4 Weeks
Kostenlos
IBM Skills Network
IBM Data Science
Kompetenzen, die Sie erwerben: Algebra, Algorithms, Business Analysis, Communication, Computational Logic, Computer Programming, Computer Programming Tools, Correlation And Dependence, Data Analysis, Data Management, Data Mining, Data Structures, Data Visualization, Database Administration, Database Application, Databases, Econometrics, Entrepreneurship, Exploratory Data Analysis, Extract, Transform, Load, General Statistics, Geovisualization, Interactive Data Visualization, Machine Learning, Machine Learning Algorithms, Marketing, Mathematical Theory & Analysis, Mathematics, Modeling, Plot (Graphics), Probability & Statistics, Python Programming, R Programming, Recommender Systems, Regression, SPSS, SQL, Spreadsheet Software, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Supply Chain Systems, Supply Chain and Logistics, Theoretical Computer Science
4.6
(98.2k Bewertungen)
Beginner · Professional Certificate · 3-6 Months

Suchanfragen mit Bezug zu probability

probability and statistics

probability theory

probability and statistics: to p or not to p?

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introduction to probability and data with r

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an intuitive introduction to probability

Zusammenfassend sind hier 10 unserer beliebtesten probability Kurse

An Intuitive Introduction to Probability: University of Zurich
Introduction to Statistics: Stanford University
Probability Theory: Foundation for Data Science: University of Colorado Boulder
Data Science Foundations: Statistical Inference: University of Colorado Boulder
Bayesian Statistics: University of California, Santa Cruz
Mathematics for Machine Learning: Imperial College London
Probability and Statistics: To p or not to p?: University of London
Business and Financial Modeling: University of Pennsylvania
Data Science: Johns Hopkins University
Python and Statistics for Financial Analysis: The Hong Kong University of Science and Technology

Häufig gestellte Fragen zum Thema Wahrscheinlichkeit

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.‎

Diese häufig gestellten Fragen dienen nur zu Informationszwecken. Den Lernenden wird empfohlen, eingehender zu recherchieren, ob Kurse und andere angestrebte Qualifikationen wirklich ihren persönlichen, beruflichen und finanziellen Vorstellungen entsprechen.