In the previous article, we learned how to use the GROUP BY clause and aggregate functions to group and summarize data. So far, we have completed the learning of the basics.
In this article, we use the global GDP and population data released by the World Bank as a case to introduce the common methods of using SQL for data analysis. At the same time, it is also used as a review and review of the content learned in the basic chapter.
GDP (Gross Domestic Product) refers to the market price of all final products and services produced by all resident units of a country/region in a certain period of time calculated at market prices. GDP is generally recognized as the best indicator of a country's economic conditions.
The source of the data in this article is GDP (current US dollars) and total population in public data of the World Bank . For ease of use, we converted the data into SQL scripts, and extracted the 2010-2018 GDP data (converted in current U.S. dollars) and the corresponding population data of 217 countries and regions around the world. The case initialization script gdp_data.sql is on GitHub, you can click the link to download it.
Preliminary exploration data
All GDP and population data are stored in the gdp_data table. Let’s get familiar with the data in this table:
SELECT *
FROM gdp_data;
The result of the query is as follows (part of the content is displayed):