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Course Outline
Introduction
- Recognizing the significance of data preparation in analytics and machine learning for government operations
- The role of the data preparation pipeline in the lifecycle of data management for government
- Identifying common challenges in raw data and their impact on analysis within public sector workflows
Data Collection and Acquisition
- Sources of data: databases, APIs, spreadsheets, text files, and more for government use
- Techniques for collecting data and ensuring data quality during the collection process in government environments
- Gathering data from various sources to support government initiatives
Data Cleaning Techniques
- Identifying and managing missing values, outliers, and inconsistencies in datasets for government applications
- Addressing duplicates and errors within datasets for enhanced accuracy in government reports
- Cleaning real-world datasets to meet the standards required for government analysis
Data Transformation and Standardization
- Data normalization and standardization techniques for government data sets
- Categorical data handling: encoding, binning, and feature engineering for government analytics
- Transforming raw data into formats suitable for government reporting and decision-making
Data Integration and Aggregation
- Merging and combining datasets from different sources to support comprehensive government analysis
- Resolving data conflicts and aligning data types for consistent government reports
- Techniques for data aggregation and consolidation in government contexts
Data Quality Assurance
- Methods for ensuring data quality and integrity throughout the process in government operations
- Implementing quality checks and validation procedures to maintain high standards of data accuracy for government use
- Case studies and practical applications of data quality assurance in government settings
Dimensionality Reduction and Feature Selection
- Understanding the need for dimensionality reduction in government datasets
- Techniques such as PCA, feature selection, and reduction strategies for government analytics
- Implementing dimensionality reduction techniques to enhance the efficiency of government data analysis
Summary and Next Steps
Requirements
- Foundational knowledge of data principles
Audience for government
- Data Analysts
- Database Administrators
- IT Professionals
14 Hours
Testimonials (2)
It's a hands-on session.
Vorraluck Sarechuer - Total Access Communication Public Company Limited (dtac)
Course - Talend Open Studio for ESB
I generally enjoyed the knowledge of the trainer.
