Learn essential techniques for assessing data quality in Java applications. Discover how to use descriptive statistics to identify outliers, detect and handle missing values appropriately, and validate data types to prevent errors. Master key tools like `DescriptiveStatistics` for numerical analysis, `Optional` for null handling, and `DateTimeFormatter` for date validation.

Data statistics

Computing mean, min, max

Computing percentiles

Null detection

Detecting missing numbers

Detecting missing strings

Data type verification

Parsing text into dates and numbers

Reading CSV files safely

Assessing Data Quality

Master data transformation techniques for reliable Java applications. Learn to normalize strings using regular expressions for consistent text matching, standardize categories with `EnumMap` and `HashMap` for robust lookup tables, and handle date formats using Java's `time` API with `LocalDate` and `ZoneId` for consistent date handling across time zones.

String normalization

Regular expressions

Cleaning strings

Categorical standardization

Building a category mapping

Grouping by standardized categories

Date standardization

Standardizing dates

Converting time zones

Extracting month and year from dates

Transforming Data

Ensure data quality through validation techniques. Learn to implement range validation for numeric values and dates, master pattern validation using regular expressions to verify data formats, and apply constraint validation to enforce business rules.

Range validation

Validating numeric ranges

Validating date ranges

Pattern validation

Full pattern matching

Pattern finding

Character matching

Constraint enforcement

Annotations with video game sales

Displaying validation messages

Validating Data

Transform messy tabular data into clean, usable datasets with Tablesaw, a powerful Java library. You'll assess data quality, standardize column contents, and apply filtering operations to prepare your data. By the end, you'll confidently turn raw datasets into analysis-ready tables.

Data quality assessment with Tablesaw

Analyzing missing and unique values

Analyzing numeric columns

Column cleaning and standardization

Transforming numeric data

Transforming text data

Column filtering and aggregation

Filtering columns

Aggregating columns

Congratulations!

Cleaning Tabular Data

grocery_inventory.csv

coffee_sales.csv

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.

Data Types and Exceptions in Java

Master data cleaning in Java using statistical methods, transformations, and validation for reliable apps.

Cleaning Data in Java

Software Engineer

Why check data types?

Validating numeric text

Parsing numeric text

Validating date formats

Combining numeric and date validation

Summary: data type verification

Let's practice!

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Assessing Data Quality - Data type verification

Data type verification

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