From the course: SQL for Exploratory Data Analysis Essential Training
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Next steps - PostgreSQL Tutorial
From the course: SQL for Exploratory Data Analysis Essential Training
Next steps
- [Instructor] Let's discuss some tips for exploring your data. Data exploration is an important step in the data science process. It's used to help understand the quality of data. This is important, before you start to make hypotheses, and draw conclusions from your data. It's used to identify missing values, which can happen if there are data quality control programs, with the source systems that supply your data. It also helps us find unusual, or unexpected values, such as outliers, that don't make sense from a business logic perspective. Data exploration helps highlight inconsistent data, especially with regards to business rules. It also allows us to understand the distribution of the data, that is, the shape of the data, and understand subgroups, using histograms. And correlations allow us to see relationships between variables, when we use the Pearson's Correlation Coefficient. Exploratory data analysis, is a starting point, that helps you understand your data, and avoid…
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