Gain insights into advanced SQL data analysis techniques. Explore producing descriptive statistics, handling missing data, and analyzing time series. Discover aggregating results and preparing data for analysis.

SQL databases have been around since the 1970s. Some of the smartest people in the world worked on making it easy to slice, dice, fetch and manipulate data quickly and efficiently. SQL databases have come such a long way that many developers and data scientists have lost track of what they can do with plain SQL.

In this course, you will learn advanced concepts and techniques for analyzing data using SQL. You will obtain hands-on experience in producing descriptive statistics, breaking a large query into multiple steps and samples, and cleaning and preparing data for analysis. You will learn how to produce aggregate results and subtotals, analyze a time series using cumulative and window frames, how to handle and fill missing data, and how to produce buckets and histograms.

After completing this course, you will be able to gain actionable insight from your data using nothing but SQL!

Practical Data Analysis with SQL

# Overview 

Categorical values are taken from a limited set of known values. Common examples are user, product, category, and so on.

To demonstrate, we are going to use the following list of values:


SELECT * FROM (VALUES ('orange'), ('apple'), ('banana'), ('apple')) AS t(fruit);

# Counting distinct values

To count the number of rows, we can use the `COUNT` function as we did before:

SELECT 
    COUNT(*) 
FROM (
    VALUES ('orange'), ('apple'), ('banana'), ('apple')
) AS t(fruit);

Using `COUNT(*)`, we found that the series has 4 rows. However, our series can have duplicate values. If we want to know how many unique values we have in the series, we can use the `DISTINCT` keyword:

Learn how to describe a categorical series using SQL.

Introduction

Basic SQL for Data Analysis

Descriptive Statistics

Grouping and Subtotals

Running and Cumulative Aggregation

Interpolation

Binning

Conclusion

Describing a Categorical Series

Overview

Counting distinct values