Handling Missing Values in SQL | SQL Tutorial
Missing data can be a real headache in SQL analysis. But fear not, This video equips you with the essential skills to conquer missing values and ensure your results are accurate and meaningful. Dive into different techniques and emerge victorious over incomplete datasets!
Imagine this:
Analyzing customer surveys: Some responses might be missing, but you still need valuable insights. Learn how to handle them effectively.
Building machine learning models: Missing values can skew your predictions. Discover techniques to prepare your data for reliable models.
Generating reports: Incomplete data can lead to misleading conclusions. Master methods to present accurate and representative results.
This video empowers you with:
Understanding the different types of missing values: Identify missing at random (MAR), missing not at random (MNAR), and other categories to choose the right approach.
Utilizing deletion techniques: Learn when and how to safely remove rows or columns with missing values, considering potential biases.
Imputing missing values: Explore various imputation methods like mean/median filling, KNN imputation, and model-based approaches to estimate missing values strategically.
Leveraging window functions: Discover how window functions like LAG, LEAD, and INTERPOLATE can fill in missing values within groups based on available data.
Choosing the best method: Learn how to select the most appropriate technique based on your data characteristics, analysis goals, and potential biases.
Real-world applications: See how different industries handle missing values in SQL, from finance and marketing to healthcare and social sciences.
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