Generating correlated random variables with Python - Probability Theory, Statistics and Exploratory

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Generating correlated random variables with Python - Probability Theory, Statistics and Exploratory Data Analysis
Mathematics for Data Science Specialization
Exploration of Data Science requires certain background in probability and statistics. This course introduces you to the necessary sections of probability theory and statistics, guiding you from the very basics all way up to the level required for jump starting your ascent in Data Science.
The core concept of the course is random variable - i.e. variable whose values are determined by random experiment. Random variables are used as a model for data generation processes we want to study. Properties of the data are deeply linked to the corresponding properties of random variables, such as expected value, variance and correlations. Dependencies between random variables are crucial factor that allows us to predict unknown quantities based on known values, which forms the basis of supervised machine learning. We begin with the notion of independent events and conditional probability, then introduce two main classes of random variables: discrete and continuous and study their properties. Finally, we learn different types of data and their connection with random variables.
While introducing you to the theory, we'll pay special attention to practical aspects for working with probabilities, sampling, data analysis, and data visualization in Python.
This course requires basic knowledge in Discrete mathematics (combinatorics) and calculus (derivatives, integrals).
Excellent course. To the point with no fluff. The professor explained everything in just the right amount of detail and the inclusion of python is great too.,One of the best courses for Probability and statistics, the course structure and syllabus was really very organized. I enjoyed completing this course.
This week we'll study continuous random variables that constitute important data type in statistics and data analysis. For continuous random variables we'll define probability density function (PDF) and cumulative distribution function (CDF), see how they are linked and how sampling from random variable may be used to approximate its PDF. We'll introduce expected value, variance, covariance and correlation for continuous random variables and discuss their properties. Finally, we'll use Python to generate independent and correlated continuous random variables.
Generating correlated random variables with Python - Probability Theory, Statistics and Exploratory Data Analysis
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SO INTERSTING, Thank you so much

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Thank you

[@rohanm.2707]

How can I generate more than 2 correlated random variables?