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Master data cleaning in Java using statistical methods, transformations, and validation for reliable apps.
Cleaning data is crucial for business problems. When data quality suffers, analytics become unreliable, machine learning models make poor predictions, and business decisions go awry.
This course equips you with Java tools to tackle data quality head-on. You'll learn statistical methods to spot outliers and handle missing values, master data transformations from standardizing text to managing dates across time zones, and implement range checks using regular expressions and validation annotations.
Working with Tablesaw, you'll clean real-world tabular data and perform transformations that prepare data for analysis. You'll finish ready to ensure data quality at every step of your applications.
Cleaning data is crucial for business problems. When data quality suffers, analytics become unreliable, machine learning models make poor predictions, and business decisions go awry.
This course equips you with Java tools to tackle data quality head-on. You'll learn statistical methods to spot outliers and handle missing values, master data transformations from standardizing text to managing dates across time zones, and implement range checks using regular expressions and validation annotations.
Working with Tablesaw, you'll clean real-world tabular data and perform transformations that prepare data for analysis. You'll finish ready to ensure data quality at every step of your applications.
