This video explains covariance and correlation.<br<br/>/>

This clip is from the chapter "Exploratory Data Analysis" of the series "R Programming for Statistics and Data Science".This section explains exploratory data analysis.

Julia is an easy, fast, open source language that if written well performs nearly as well as low-level languages such as C and FORTRAN. Its design is a dance between specialization and abstraction, providing high machine...

This video explains Eigenvalues and Eigenvectors.<br<br/>/>

This clip is from the chapter "Dimensionality Reduction Using PCA" of the series "Practical Data Science Using Python".This section explains dimensionality reduction using PCA.

This video explains Principal Component Analysis – computations.
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This clip is from the chapter "Dimensionality Reduction Using PCA" of the series "Practical Data Science Using Python".This section explains dimensionality...

In this video, we will discuss some widely used statistical concepts required in predictive analytics, followed by some basic understanding of predictive modeling, such as random sampling, central limit theorem, hypothesis testing...

This video explains OLS assumptions in detail.
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This clip is from the chapter "Assumptions for Linear Regression Analysis" of the series "Statistics for Data Science and Business Analysis".This section explains OLS...

This video continues with the most common measure of interconnection between variables: covariance.
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This clip is from the chapter "Descriptive Statistics Fundamentals" of the series "Statistics for Data Science and Business...