FeaturedUdemy
Cleaning Data in Java
4.9(66)552 studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
Data quality is crucial for reliable software and analytics. This intermediate course introduces Java developers to data cleaning techniques using Tablesaw and standard Java libraries, covering outlier detection, date/time formatting across time zones, and regular expression validation.
Who It's For
Java developers, backend engineers, and data engineers who need to enforce data quality and sanitize tabular datasets directly within Java applications.
Key Takeaways
- Tablesaw for Java: Manipulate and clean tabular datasets using the Tablesaw Java library.
- Missing Values & Outliers: Apply statistical methods to handle missing values and spot data anomalies.
- Validation & Transformations: Standardize text, manage time zones, and enforce range constraints using regular expressions and annotations.
About this course
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.
Price
Free
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