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.
An intermediate-level course exploring fundamental statistical principles and their practical applications
This intermediate-level course offers a comprehensive exploration of fundamental statistical principles and their practical applications. Designed for learners with a basic understanding of set theory and calculus, this course delves into the fascinating world of probability distributions, descriptive statistics, and estimation methods.
Lessons 1-14 covering various probability distributions, normal distribution, Central Limit Theorem, and computer applications
Lessons 1-11 covering descriptive statistics, estimation methods, and sampling distributions
This course offers a balance of theoretical knowledge and practical applications, preparing learners for advanced statistical concepts and real-world problem-solving. By mastering these fundamental statistical concepts, students will be well-equipped to tackle complex data analysis challenges and make data-driven decisions in their future academic and professional endeavors.
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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.
'Statistical and Probabilistic Foundations of AI' provides an accessible overview of the mathematics and statistics behind fundamental concepts of data science, machine learning, and artificial intelligence. It covers descriptive and exploratory data analysis and a brief introduction to inferential statistics. It provides the principles of probability necessary to understand the methods used in inferential statistics and machine learning at an introductory level.
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.
