Congratulations!
1. Congratulations!
Congratulations on completing Cleaning Data in Java!2. Journey through data cleaning
We've covered essential skills for ensuring data quality: assessing data through statistics, transforming text and dates to standard formats, validating values against business rules, and cleaning tabular datasets. Let's review our journey.3. Assessing data quality
Bad data leads to bad decisions. Our first step was assessing data quality to catch issues early. Using `DescriptiveStatistics`, we identified outliers that could skew analysis. With `Optional` and utility methods, we detected missing values that could cause processing errors. Type verification with parsers like `LocalDate.parse()` helped prevent data corruption. These assessments guide our cleaning strategy and ensure reliable results.4. Transforming data consistently
Inconsistent data formats prevent accurate analysis. We need clean strings to find matching text, standardized categories to group related items, and consistent dates to compare events. Using string normalization, category mapping, and date conversion, we transform messy data into consistent formats that enable reliable searching, grouping, and sorting.5. Validating data integrity
Data validation catches problems before they enter our system and propagate through calculations. We used `Range` to ensure values stay within valid bounds, regex `Pattern` to verify text formats, and Jakarta's validation framework to enforce business rules. These checks create a safety net for data integrity.6. Cleaning tabular data
Datasets often contain missing values or inconsistent formats, and they may need derived metrics for meaningful analysis. Using Tablesaw, we first assess quality with `.countMissing()`, then clean columns with `.map()`, and finally combine operations with `.where()` and `.summarize()` to create reliable, analysis-ready data.7. Resources
Check out a few of these DataCamp resources for your next learning journey!8. Ready to clean
You now have the tools to tackle data cleaning challenges. Keep practicing these skills on your own datasets!Create Your Free Account
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