Understand the importance of data preparation in the big data analytics process, including its impact on the quality and reliability of analytical results.
Explore techniques for data acquisition and ingestion from various sources, including databases, file systems, APIs, and streaming platforms.
Analyze methods for data cleaning and preprocessing to address issues such as missing values, outliers, duplicates, and inconsistencies.
Understand the principles of data transformation and normalization to ensure data consistency and compatibility across different formats and structures.
Explore the concepts of feature engineering and extraction for creating new features from raw data to enhance model performance and interpretability.
Analyze strategies for data integration and consolidation to combine disparate datasets into a unified schema for comprehensive analysis.
Understand the principles of data sampling and stratification for creating representative subsets of large datasets to facilitate exploratory analysis and model training.
Explore techniques for handling imbalanced datasets to address challenges associated with unequal class distributions in classification tasks.
Analyze methods for data partitioning and splitting to separate datasets into training, validation, and test sets for model development and evaluation.
Understand the concepts of data anonymization and pseudonymization to protect sensitive information and ensure compliance with privacy regulations.
Explore techniques for handling temporal and spatial data to capture temporal dependencies and spatial relationships in analytical models.
Analyze the principles of data quality assessment and validation to measure the accuracy, completeness, and consistency of datasets.
Understand the concepts of metadata management and documentation to catalog and annotate datasets for traceability and reproducibility.
Explore techniques for data enrichment and augmentation to supplement existing datasets with additional information from external sources.
Analyze the role of data profiling and exploratory data analysis (EDA) in gaining insights into dataset characteristics and identifying patterns and trends.
Understand the principles of data versioning and lineage tracking to manage changes and lineage information throughout the data lifecycle.
Explore techniques for data compression and storage optimization to reduce storage costs and improve data accessibility and retrieval performance.
Analyze the challenges and best practices associated with scaling data preparation workflows for handling increasingly large and complex datasets.