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- Probability

- An Intuitive Introduction to Probability: University of Zurich
- Introduction to Statistics: Stanford University
- Mathematics for Machine Learning: Imperial College London
- Bayesian Statistics: University of California, Santa Cruz
- IBM Data Science: IBM Skills Network
- Probability Theory: Foundation for Data Science: University of Colorado Boulder
- 頑想學概率：機率一 (Probability (1)): National Taiwan University
- Probability and Statistics: To p or not to p?: University of London
- Combinatorics and Probability: University of California San Diego
- Data Science Fundamentals with Python and SQL: IBM Skills Network

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.

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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