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1. Introduction to Statistics
This lesson will define statistics, including the difference between descriptive and inferential statistics. It will also outline the contents of this course.

What is Statistics? (00:53)
Statistics is the process of collecting, analyzing and interpreting data. It underpins most work in data analytics. We can divide statistics into two broad areas: descriptive statistics and inferential statistics.
Descriptive statistics focuses on summarizing a dataset of interest, so we can draw conclusions about the data. Inferential statistics involves making a prediction about a population of data based on a sample of that data.

Course Overview (01:48)
This course focuses on explaining the principles behind the statistical techniques that are applied in other Kubicle courses. The course is divided into three main sections:

Describing a Data Set

Statistical Concepts

Distributions

In this course, we're going to learn about the principles of statistics. Statistics underpins almost all work in data analytics so having some knowledge of statistics is essential for any aspiring data analyst. Statistics is a large field of study that can get complex and offputting, but we're only going to focus on the relatively simple concepts featured in other Kubicle courses. Many other Kubicle courses teach you how to apply various statistical methods and techniques.
For many users, this is perfectly adequate and enables them to work with statistics in whatever software they're using. However, in this course, we'll focus on teaching you the principles behind those techniques.
Knowing the principles behind the methods will help improve your understanding of these statistical techniques. First, let's answer the question, what is statistics? Statistics is simply the process of collecting, analyzing and interpreting data.
Statistics can be divided into two broad areas, descriptive statistics and inferential statistics. Descriptive statistics is the more straightforward branch. It focuses on summarizing and analyzing a dataset of interest so we can derive various conclusions about that data. Common examples of descriptive statistics include concepts such as averages, variance and percentiles.
Inferential statistics is more advanced and involves making predictions about a population of data based on a sample of that data. Some examples of inferential statistics include hypothesis testing and confidence intervals.
Our focus over the coming lessons will be primarily on descriptive statistics but we'll introduce a few basics concepts that are used in inferential statistics. This course has three main sections. First, we'll focus on describing a dataset. We'll learn about averages, measures of variability, percentiles and data events. Second, we'll look at a few important statistical concepts specifically the difference between continuous and discrete data and the idea of correlation.
Finally, we'll learn how data can be distributed, zscores and the difference between populations and samples.
Let's stop here. In the next lesson, we'll start describing a dataset by looking at different types of averages.