In our statistics intermediate for R users course, you will learn how to summarize distributions using the mean, median, and the mode. And we’ll cover when to use them, and which statistic gives you the most information about a distribution, so that you don’t just know how to apply them, you know why.

You will also learn to measure variability using variance or standard deviation, and how to locate and compare values using z-scores. We’ll then dig into concepts such as range, mean absolute deviation, variance, and standard deviation.

After learning about the different ways to measure variability, you'll learn about Z-Scores and how to use them to compare values across any distribution. Z-Scores are important because they allow us to calculate the probability of a statistic occurring within our distribution. Z-Scores are also important when you study about a domain of statistics called inferential statistics.

At the end of the course, you'll complete a portfolio project in which you'll find the best markets for advertising an e-learning platform that combines your data science programming skills and the statistical skills you’ve learned in this course. It also serves as a portfolio project you can use to demonstrate the statistical skills that you'll bring to the job for potential future employers.

By the end of this course, you'll be able to:

## Learn Intermediate Statistics in R

### The Mean

Learn to use the mean to summarize the distribution of a variable.

### The Weighted Mode And The Median

Learn about the weighted mean and the median as alternatives to the mean.

### The Mode

Learn about the mode and the location of the mean, median, and mode in skewed and symmetrical distributions.

### Measures Of Variability

Learn how to measure variability using range, mean absolute deviation, variance, and standard deviation.

### Z-Scores

Learn to locate and compare values using z-scores.

### Finding The Best Markets To Advertise In

Learn to combine the skills you learned in this course to perform practical data analysis.