Data Exploration 101
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Transcript#
This transcript was generated automatically and may contain errors.
This is how to start exploring and cleaning your data the right way. When you're starting a new project with a new dataset, it can be tempting to jump straight into building a model, but the real magic happens in EDA, or Exploratory Data Analysis.
If you're using Positron, the Data Explorer widget makes this super easy. Data Explorer gives you instant column summaries to see counts, unique values, and missing data without writing a single line of code, data type detection, interactive filtering, and quick sorting and searching. You can literally open a dataset, click through the columns, and get a feel for the shape of your data in seconds.
the real magic happens in EDA, or Exploratory Data Analysis.
Non-negotiables before analysis
Whether you're using Positron or not, these are your non-negotiables before you start analysis. First up is to check for missing values. Decide if and how you're filling them, or if you're dropping them. Confirm what data types you have. Make sure numbers are numbers, dates are dates, and text is actually text.
Look for duplicates. These can quietly skew your results. Scan for outliers or weird values. Sometimes it's a data entry error. Sometimes it's a real insight. And lastly, standardize your formats for dates, currency, units. This consistency matters.
Make sure you're following along for more data science content. Bye!
