Data Wrangling

Messy vs Clean Data

Handling Missing Values

Data Transformation

Joining Datasets

Detecting and Resolving Duplicates

Data Validation and Quality Assurance

Working with Text Data and Regular Expressions

Handling Date and Time Data

Handling Outliers and Anomalies

Advanced Imputation Techniques for Missing Data

Categorical Data Encoding and Management

Data Aggregation and Summarization

Building Reproducible Data Wrangling Workflows

Optimizing Data Wrangling for Performance and Scale

Feature Engineering for Machine Learning

Wrangling Semi-structured and Unstructured Data (JSON, XML, Logs)

Data Lineage, Versioning, and Audit Trails

Real-time Data Wrangling and Streaming Data

Advanced Record Linkage and Data Fusion

Automating Data Quality Checks and Validation Pipelines