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Course Outline
Introduction
- Recognizing the critical role of data preparation in analytics and machine learning workflows
- Examining the data preparation pipeline and its function within the broader data lifecycle
- Investigating common issues in raw data and their potential impact on analytical outcomes
Data Collection and Acquisition
- Identifying diverse data sources, including databases, APIs, spreadsheets, and text files
- Applying effective collection techniques while maintaining data integrity from the source
- Managing data ingestion from multiple, varied sources
Data Cleaning Techniques
- Detecting and resolving missing values, outliers, and inconsistencies within datasets
- Mitigating the effects of duplicates and erroneous entries
- Practicing cleaning workflows on realistic, complex datasets
Data Transformation and Standardization
- Applying normalization and standardization methods to unify data formats
- Processing categorical data through encoding, binning, and feature engineering strategies
- Converting raw inputs into formats suitable for downstream consumption
Data Integration and Aggregation
- Combining and merging datasets originating from disparate sources
- Resolving conflicts between data fields and aligning data types for compatibility
- Utilizing advanced aggregation and consolidation techniques
Data Quality Assurance
- Employing methods to uphold data quality and integrity across the entire process
- Establishing robust quality checks and validation procedures
- Reviewing case studies and practical implementations of quality assurance
Dimensionality Reduction and Feature Selection
- Evaluating the necessity of reducing data dimensions for improved performance
- Implementing strategies such as PCA, feature selection, and other reduction techniques
- Applying dimensionality reduction methods to optimize models
Summary and Next Steps
Requirements
- A foundational understanding of core data concepts
Target Audience
- Data analysts
- Database administrators
- IT professionals
14 Hours
Testimonials (2)
The variety of the information shared and the clarity to explain terms in plain English.
Arisbe Mendoza - Fairtrade International
Course - GDPR Workshop
It's a hands-on session.
