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Course Outline
Introduction
- Overview of the critical role data preparation plays in analytics and machine learning initiatives
- Description of the data preparation pipeline and its function within the broader data lifecycle
- Analysis of prevalent challenges associated with raw data and their implications for analytical outcomes
Data Collection and Acquisition
- Identification of primary data sources, including databases, application programming interfaces (APIs), spreadsheets, text files, and others
- Methodologies for data acquisition and protocols to maintain data quality during collection phases
- Strategies for sourcing data across diverse environments
Data Cleaning Techniques
- Procedures for detecting and addressing missing values, outliers, and inconsistent entries
- Management of duplicate records and erroneous data within datasets
- Application of cleaning protocols to real-world datasets
Data Transformation and Standardization
- Techniques for data normalization and standardization
- Management of categorical variables through encoding, binning, and feature engineering
- Conversion of raw data into formats suitable for processing
Data Integration and Aggregation
- Methods for merging and combining datasets originating from disparate sources
- Resolution of data conflicts and alignment of data types
- Approaches to data aggregation and consolidation
Data Quality Assurance
- Strategies for maintaining data quality and integrity throughout the preparation process
- Implementation of validation procedures and quality checks
- Review of case studies and practical applications relevant for government agencies
Dimensionality Reduction and Feature Selection
- Rationale behind dimensionality reduction in complex datasets
- Overview of techniques such as Principal Component Analysis (PCA), feature selection, and reduction strategies
- Execution of dimensionality reduction methodologies
Summary and Next Steps
Requirements
- Fundamental knowledge of data principles is required.
Intended 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.
