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 analyzing a table using aggregates, it is sometimes necessary to apply some filtering to the data. For example, say we want to count the number of managers and non-managers in a single query:

SELECT
  (SELECT COUNT(*) FROM emp WHERE role = 'Manager') AS managers,
  (SELECT COUNT(*) FROM emp WHERE role != 'Manager') AS non_managers
; 

Learn how to apply conditions on aggregate expressions to produce advanced reports and pivot tables.

Introduction

Basic SQL for Data Analysis

Descriptive Statistics

Grouping and Subtotals

Running and Cumulative Aggregation

Interpolation

Binning

Conclusion

Conditional Aggregates

Overview

Conditional aggregates with `CASE`

Conditional Aggregates

Overview

Conditional aggregates with CASE

Conditional aggregates with `CASE`