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 

When working with big data, it is useful to aggregate results at different levels. For example, counting the number of students per class, finding the maximum price for each category, and so on. In this lesson, we'll use SQL to group results and compute aggregates.

# Simple grouping

Let’s suppose we have a table of employees. For each employee, we keep their name, role, department, and their salary. The table looks like this:


CREATE TABLE emp AS 
    SELECT * 
    FROM (VALUES
        ('Haki',    'R&D',      'Manager',   10000),
        ('Dan',     'R&D',      'Developer', 7000),
        ('Jax',     'R&D',      'Developer', 7500),
        ('George',  'Sales',    'Manager',   8500),
        ('Bill',    'Sales',    'Developer', 8000),
        ('David',   'Sales',    'Developer', 8000)
    ) AS t(
        name,       department,  role,       salary
    );

SELECT * FROM emp;

To count the number of employees in each department, we can use the `GROUP BY` clause:

SELECT 
    department, 
    COUNT(*) AS number_of_employees
FROM
    emp
GROUP BY 
    department;

Learn how to group results and compute aggregates using SQL.

Introduction

Basic SQL for Data Analysis

Descriptive Statistics

Grouping and Subtotals

Running and Cumulative Aggregation

Interpolation

Binning

Conclusion

Grouping

Overview

Simple grouping