Case Study (SQL, Python): Data analysis of the Olympic Games

In the world of Data Science, SQL is critical for data extraction, manipulation, and analysis. In this article, I will present a case study in which I used SQL to analyze a historical dataset from the Olympic Games.

This case study highlights how using SQL (and Python) for data analysis allows for discovering trends and insights of particular statistical interest.

The case study is based on historical data from the Olympic Games to uncover trends and insights that could shed light on the evolution of the games and the athletes participating in them. Initial hypotheses were related to the correlation between athletes’ height and weight, the trend of female participation, and the distribution of Body Mass Index (BMI).

The approach to data analysis was a combination of SQL and Python, leveraging both strengths to extract, clean, and analyze data. Several technical challenges were faced during the analysis, including missing data, inconsistent data, a large dataset, and complex SQL queries.