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Duration 14 hours
Course Outline
Introduction
- Recognizing the critical role of data preparation in analytics and machine learning
- The data preparation pipeline and its function within the data lifecycle
- Investigating typical raw data challenges and their effect on analytical outcomes
Data Acquisition and Sourcing
- Identifying data sources: databases, APIs, spreadsheets, text files, and beyond
- Strategies for gathering data while maintaining quality control during acquisition
- Methods for retrieving data from multiple origins
Data Cleansing Methodologies
- Detecting and managing missing values, outliers, and inconsistencies
- Addressing duplicates and errors within datasets
- Practical exercises in cleaning real-world datasets
Data Transformation and Standardization
- Approaches to data normalization and standardization
- Managing categorical data through encoding, binning, and feature engineering
- Converting raw data into actionable formats
Data Integration and Aggregation
- Merging and combining datasets from disparate sources
- Resolving data conflicts and aligning data types
- Strategies for aggregating and consolidating data
Data Quality Assurance
- Ensuring data quality and integrity throughout the entire process
- Establishing quality checks and validation protocols
- Reviewing case studies and practical applications of quality assurance
Dimensionality Reduction and Feature Selection
- Comprehending the necessity of dimensionality reduction
- Applying techniques such as PCA, feature selection, and reduction strategies
- Executing dimensionality reduction methods
Conclusion and Future Directions
Requirements
- Fundamental comprehension of data concepts
Target Audience
- Data analysts
- Database administrators
- IT professionals
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.
