This data dive emphasizes the importance of documentation when it comes to data and explores some of the attributes that can be confusing at first glance. It also looks for both explicit and implicit missing values as well as empty groups within the dataset. Outliers are also discussed towards the end.

This data dive explores the effects of different sample size within NBA unexpected player performance data.

This data dive explores NBA player data of unexpected performance, using grouping and probabilities to spot common patterns and rare cases, with visualizations and short interpretations along the way.

Explores summary statistics and visual patterns in unexpected NBA player performance data with a focus on scoring and overall impact between both regular season and playoff games.