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

Generating random data is useful for various purposes, but more often than not, we want to generate random data for testing, development, and benchmarking. For example, you might want to populate data in your local development environment, or you might want to test some query optimization techniques and you need a big table. 

# The `RANDOM` function

To generate random numbers PostgreSQL provides a `random` function. The function then returns a value between 0 and 1:


> **Note:** Try executing this function multiple times, and each time you'll get a distinct random number between 0 and 1.

# Generating random integers
 
As we've seen in the previous example, the `random` function produces a random number greater than or equal to 0 and less than 1. It does not accept any arguments that allow us to control the range. What should we do if we want a random integer?

To generate values at different ranges, we can use `random` in an expression. For example, to produce a random float between 0 and 99, we can multiply the result of `random()` by `100`:

> **Note:** If we execute this query multiple times, we’ll get a different random number greater than or equal to 0 and less than 100, every time.

If we want to produce a random integer between 0 and 99, we can round the number down using the `floor` function:

Learn to generate random data in PostgreSQL.

Introduction

Basic SQL for Data Analysis

Descriptive Statistics

Grouping and Subtotals

Running and Cumulative Aggregation

Interpolation

Binning

Conclusion

Random Data

The `RANDOM` function

Generating random integers

Random Data

The RANDOM function

Generating random integers

The `RANDOM` function