Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference -- Part of the MITx MicroMasters program in Statistics and Data Science.
Embark on an exciting journey into the world of probability and statistical inference with this advanced-level course offered by MITx. "Introduction to Probability - The Science of Uncertainty and Data" is a comprehensive and rigorous exploration of probabilistic modeling and its applications in real-world scenarios. This course, part of the MITx MicroMasters Program in Statistics and Data Science, has been refined over 50 years at MIT and presents complex concepts in an intuitive yet mathematically precise manner.
This course is ideal for:
The skills acquired in this course have wide-ranging applications across various industries and research fields. Graduates can:
Each unit covers specific topics in depth, providing a comprehensive understanding of probability theory and its applications. This course offers a challenging but rewarding experience that will equip you with the tools to become an effective practitioner of data science and statistical analysis.
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This lecture series discusses how the concept of probability can be used to handle, control, and exploit uncertainty in the real-world. It is an undergraduate-level lecture series on probability, but is entirely different from the usual courses on probability theory. The lectures cover the basics of probability theory including the relevant mathematics, but instead of focusing on mathematics, the lectures explain how probability theory can help understand real-world uncertainty using various examples. The examples are used to describe how uncertainty can be exploited to implement modern randomized algorithms such as Markov chain Monte Carlo and deep learning.
This course introduces the basic tools and methods of statistical analysis, which can be applied to a wide variety of situations and data encountered in the areas of business and economics. Topics discussed include descriptive statistics, probability, sampling distributions, estimation, and hypothesis testing. By the end of the semester students should be able to compute and interpret basic descriptive statistical measures, understand the basic concepts of probability, and apply techniques of statistical inference.
This course provides an introduction to basic statistical concepts. We begin by walking through a library of probability distributions – including the normal distribution, which in turn leads to the Central Limit Theorem. We then discuss elementary descriptive statistics and estimation methods.
