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Duration 14 hours
Course Outline
Introduction
- Examining the critical role of rigorous data preparation in supporting analytics and machine learning initiatives for government operations
- Mapping the data preparation pipeline and its function within the broader data governance lifecycle
- Identifying prevalent challenges in raw data sources and their potential impact on analytical accuracy and reporting
Data Collection and Acquisition
- Identifying authoritative data sources, including databases, application programming interfaces, spreadsheets, and text files, relevant to public sector needs
- Implementing established techniques for data acquisition that prioritize quality controls and compliance during the collection phase
- Aggregating data from multiple disparate sources to support unified reporting and analysis for government use
Data Cleaning Techniques
- Systematically identifying and remediating missing values, statistical outliers, and data inconsistencies to ensure integrity
- Addressing duplicate records and systemic errors within datasets to maintain reliability
- Applying cleaning protocols to real-world datasets to enhance data trustworthiness for government stakeholders
Data Transformation and Standardization
- Applying normalization and standardization methods to ensure uniformity and compatibility across datasets
- Managing categorical data through proper encoding, binning, and feature engineering to support accurate modeling
- Converting raw, unprocessed data into standardized formats suitable for operational and analytical use for government
Data Integration and Aggregation
- Effectively merging and combining datasets from varied sources to create comprehensive views of information
- Resolving data conflicts and aligning data types to ensure consistency across integrated systems
- Utilizing aggregation and consolidation techniques to summarize and streamline data for decision-making for government
Data Quality Assurance
- Establishing methods to uphold data quality and integrity throughout the entire processing workflow
- Implementing rigorous quality checks and validation procedures to ensure accountability and accuracy
- Reviewing case studies and practical applications of data quality assurance within public sector contexts
Dimensionality Reduction and Feature Selection
- Evaluating the necessity of dimensionality reduction to optimize computational efficiency and model performance
- Utilizing techniques such as Principal Component Analysis, feature selection, and other reduction strategies to enhance data utility
- Applying dimensionality reduction techniques to refine datasets for more effective analysis and reporting for government
Summary and Next Steps
Requirements
- A foundational understanding of core data concepts and principles.
Audience
- Data analysts responsible for generating insights and reports for government.
- Database administrators managing data infrastructure and integrity.
- IT professionals supporting technical operations and data management for government.
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.
