Interactive Course

# Intro to Statistics with R: Introduction

A friendly introduction to fundamental concepts in statistics in R.

• 4 hours
• 16 Videos
• 45 Exercises
• 54,652 Participants
• 2,850 XP

### Course Description

Not sure if this is the type of online statistics course you’re looking for? Or perhaps not yet familiar with DataCamp’s interactive learning interface? By taking this free course, you can discover it for yourself! Via a combination of videos and interactive coding challenges, this introductory course will teach you about variables, plotting, and summary statistics like the mean and standard deviation. Enjoy learning by doing!

1. 1

#### Variables

In this chapter professor Conway will cover types of variables. It is very important to understand what type of variable you are dealing with when conducting a particular type of statistical analysis. You will cover variables such as nominal, ordinal, interval and ratio, and you will experiment with these via interactive exercises in R.

2. 3

#### Scales of Measurement

When working with data it is very important to keep in mind what type of scale you are dealing with, hence this chapter on scales of measurement. This chapter will introduce you to the different types of scales with a specific focus on the standard scale, the z-scale.

3. 5

#### Measures of Variability

Measures of central tendency try to capture the center point of a distribution. Measures of variability want to capture how much spread there is, or how wide the distribution is. The two measures you will look at in this final chapter will be standard deviation and variance.

4. 2

#### Histograms and Distributions

You will look here at distributions in graphs called histograms. A histogram is one of the simplest graphs used in statistics, but they are very useful and very informative. Studying histograms will help you to overcome the tendency to put too much of a focus on summary statistics.

5. 4

#### Measures of Central Tendency

In the previous chapters you looked at distributions and the importance of these. In this chapter the focus is more on summarizing all available information and drafting summary statistics. To make it a little bit more fun, the examples will be based on a wine tasting experiment :-).

1. 1

#### Variables

In this chapter professor Conway will cover types of variables. It is very important to understand what type of variable you are dealing with when conducting a particular type of statistical analysis. You will cover variables such as nominal, ordinal, interval and ratio, and you will experiment with these via interactive exercises in R.

2. 2

#### Histograms and Distributions

You will look here at distributions in graphs called histograms. A histogram is one of the simplest graphs used in statistics, but they are very useful and very informative. Studying histograms will help you to overcome the tendency to put too much of a focus on summary statistics.

3. 3

#### Scales of Measurement

When working with data it is very important to keep in mind what type of scale you are dealing with, hence this chapter on scales of measurement. This chapter will introduce you to the different types of scales with a specific focus on the standard scale, the z-scale.

4. 4

#### Measures of Central Tendency

In the previous chapters you looked at distributions and the importance of these. In this chapter the focus is more on summarizing all available information and drafting summary statistics. To make it a little bit more fun, the examples will be based on a wine tasting experiment :-).

5. 5

#### Measures of Variability

Measures of central tendency try to capture the center point of a distribution. Measures of variability want to capture how much spread there is, or how wide the distribution is. The two measures you will look at in this final chapter will be standard deviation and variance.

### What do other learners have to say?

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden