Probability distribution is the statistical function that explains the possible values that a random variable can take. This course on data analytics and probability distribution explains the different ways to assign probability, including marginal and conditional probabilities. We describe how to find various solutions to various problems using the laws of probability, including those of addition, multiplication and ‘conditional probability’.
The course explains how to use Bayes’ rule of probability (or ‘Bayers’ Theorem’) to calculate the likelihood of an event occurring, based on prior knowledge of the conditions that might be related to it. We explore the properties of empirical distributions and take you through binomial distributions and their applications. We then examine the mean and variance of a discrete random variable.
The course establishes the importance of simple random sampling. We compare descriptive and inferential statistics and introduce you to the differentiating factors between populations and samples. This statistics and data science course is useful for anyone entering the world of science as it covers key aspects of research methodology. Learning how to calculate probability using sampling distributions is a crucial skill in any research context.
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Alison is in Tier 4: commercial training companies and platform-made courses of our institution ranking (50/100). Free to study, paid certificates, quality varies.
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
Harvard University
Learn the concepts and techniques that make up the foundation of data science and machine learning.
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
Harvard University
Build a foundation in R and learn how to wrangle, analyze, and visualize data.
Harvard University
Learn to use machine learning in Python in this introductory course on artificial intelligence.
