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Course

Cleaning Data in Java

IntermediateSkill Level

4.8+

Updated 12/2025

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

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JavaImporting & Cleaning Data4 hr13 videos39 Exercises3,250 XPStatement of Accomplishment

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Course Description

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.

Prerequisites

Data Types and Exceptions in Java

1

Assessing Data Quality

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

2

Transforming Data

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

3

Validating 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

4

Cleaning Tabular 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

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Cleaning Data in Java

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FAQs

Why take this course?

Data quality directly impacts business decisions and model performance. This course provides practical Java skills to ensure reliable analytics and prevent costly errors from poor data quality in your applications.

What knowledge should I have prior to taking this course?

You need strong Java fundamentals including syntax, control flow, and object-oriented concepts. Familiarity with Java's Collections Framework and basic experience with data handling will help you succeed in this intermediate-level course.

What specific tools and techniques will I learn?

You'll master DescriptiveStatistics for numerical analysis, Optional for null handling, DateTimeFormatter for date validation, regular expressions for pattern matching, and Tablesaw library for tabular data cleaning and transformation.

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