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Updated in [June 30th, 2023]

This course, Probability - The Science of Uncertainty and Data, provides an introduction to the fundamentals of probability and statistical inference. Students will learn how to use probabilistic models to analyze data and make scientifically sound predictions. The course covers multiple discrete or continuous random variables, expectations, and conditional distributions, laws of large numbers, the main tools of Bayesian inference methods, and an introduction to random processes (Poisson processes and Markov chains). The course is based on the MIT class Introduction to Probability, which has been offered and continuously refined over more than 50 years. Upon completion of the course, students will have the skills needed to be an informed and effective practitioner of data science. This course is part of the MITx MicroMasters Program in Statistics and Data Science.

[Applications]
Upon completion of this course, students should be able to apply the concepts of probability theory to real-world applications or to their research. They should be able to use the language of mathematics to model uncertainty and analyze data, and be familiar with the main tools of Bayesian inference methods. Additionally, they should have an understanding of multiple discrete or continuous random variables, expectations, and conditional distributions, as well as laws of large numbers and an introduction to random processes (Poisson processes and Markov chains). Finally, they should be aware of the MITx MicroMasters Program in Statistics and Data Science, and how it can help them demonstrate their proficiency in data science or accelerate their path towards an MIT PhD or a Master's at other universities.

[Career Paths]
Job Position Path: Data Scientist

Data Scientists are responsible for analyzing large amounts of data and using their findings to inform business decisions. They use a variety of methods, including machine learning, statistical analysis, and predictive modeling, to uncover patterns and trends in data. Data Scientists must be able to interpret and communicate their findings to stakeholders in a clear and concise manner.

Data Scientists are in high demand as businesses increasingly rely on data-driven decision making. As such, the demand for Data Scientists is expected to continue to grow in the coming years. Companies are looking for Data Scientists with a strong background in mathematics, statistics, and computer science, as well as experience with programming languages such as Python and R. Additionally, Data Scientists must have strong communication and problem-solving skills.

[Education Paths]
The recommended educational path for learners interested in Probability is to pursue a Bachelor's degree in Statistics or Data Science. This degree will provide students with a comprehensive understanding of the fundamentals of probability, including multiple discrete or continuous random variables, expectations, and conditional distributions, laws of large numbers, and the main tools of Bayesian inference methods. Students will also gain an introduction to random processes such as Poisson processes and Markov chains.

The development trend of this degree is to focus on the application of probability theory to real-world problems. This includes the use of data science and machine learning techniques to analyze and interpret data, as well as the development of predictive models. Additionally, students will learn how to use statistical software to analyze data and create visualizations. Finally, students will gain an understanding of the ethical implications of data science and the importance of data privacy.