In this video, you will learn about skewness, the symmetry you learned about in the previous lesson.

In this lesson, the author explains more about the mean and median and when to use them for the best results.

In this lecture, we will elaborate more about the third and final measure of central tendency, the mode.

In this video, we will discuss the measures of central tendency. Here, you will learn more about the first measure, the mean.

This brief introduction to the section outlines what you will be learning from this section, including the basics of statistics.

In this lesson, the author explains how to use the course best and the techniques and tools best applicable.

In this video, the author briefly introduces what it would take to become a data scientist and then goes over in detail what one can learn from this course.

In this video, we will briefly understand the concept of regression and how regression analysis predicts data value.

While performing the hypothesis testing and general statistics, you will learn more about the p-z combination and the normal distribution.

In this video, let's learn to calculate the F ratio for the two-way ANOVA, interpret the results, and draw conclusions.

Let's explore more about using two factors to perform the two-way ANOVA, which will let us find the influence of two factors.

In this video, you will learn to use the one-way ANOVA, where there is only one dependent factor.

In this lecture, you will learn how to approach this problem of dealing with missing data and look at some practical examples.

In this lecture, we will look at a decision tree, how it works, its logic, and how to construct a decision tree.

Here, we will understand dependent and independent variables and learn how to predict values using linear regression.

This video provides an elaborate solution to the practice exercise of the central limit theorem concept.

This final course video concludes with a thank-you note from the author.

After learning how to use the one-way ANOVA, we will try to understand how to use the F-distribution to conclude the analysis results.

Let's now learn more about the Random Forest, which is based on a decision tree (not very robust and more complex) that is now broken down into smaller decision trees, which are not as complex.

After learning how decision trees work, we will now look at the application of the decision tree practically that combines a decision tree with regression.

This video will look at logistic regression in detail, mainly used for classification problems with one or multiple independent variables.

In this lesson, you will learn to use polynomial regression when linear regression is not working well to report accuracy, using the Bayes information criterion.

In this video, we will explore overfitting, which is very commonly used in machine learning.

After learning about simple linear regression with two variables, we will now look at multiple linear regression using multiple variables.