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Course Outline
Introduction
- The critical role of data preparation in analytics and machine learning
- The data preparation pipeline and its place within the broader data lifecycle
- Common challenges found in raw data and their effects on analytical outcomes
Data Collection and Sourcing
- Identifying data origins: databases, APIs, spreadsheets, text files, and others
- Strategies for gathering data while maintaining quality standards at the source
- Methods for aggregating data from multiple platforms
Data Cleansing Methods
- Recognizing and managing missing values, outliers, and irregularities
- Addressing duplicates and correcting errors within datasets
- Practical cleaning of real-world data sets
Data Transformation and Standardization
- Applying normalization and standardization methods
- Managing categorical data through encoding, binning, and feature engineering
- Converting raw data into actionable formats
Data Integration and Aggregation
- Combining datasets from disparate sources
- Resolving conflicts and harmonizing data types
- Strategies for consolidating and aggregating data
Data Quality Assurance
- Approaches to safeguard data integrity and quality throughout the workflow
- Establishing validation procedures and quality checks
- Examining case studies and practical implementations of quality assurance
Dimensionality Reduction and Feature Selection
- Why reducing dimensionality is often necessary
- Applying techniques such as PCA, feature selection, and reduction strategies
- Putting dimensionality reduction methods into practice
Wrap-up and Future Steps
Requirements
- A foundational grasp of data principles
Who Should Attend
- Data analysts
- Database administrators
- IT specialists
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.
