FeaturedUdemy
DataCampCleaning Data in Java
4.9(66)INTERMEDIATEEnglish
Korshub's take
Master Data Integrity with Java
Inaccurate data is the silent killer of effective machine learning and business intelligence. This intermediate-level course focuses on the practical application of Java to solve the critical problem of poor data quality.
Key Learning Objectives
- Statistical Cleaning: Learn to identify outliers and manage missing values using statistical methods.
- Data Transformation: Master complex tasks like standardizing text and handling time zone variations in dates.
- Validation Techniques: Implement robust range checks using regular expressions and validation annotations.
- Hands-on Implementation: Use the Tablesaw library to clean and transform real-world tabular datasets.
This course is ideal for developers looking to build reliable applications where data accuracy is paramount for downstream analytics and decision-making.
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.
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