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Course Outline
Introduction
- Understanding the importance of data preparation in analytics and machine learning.
- The data preparation pipeline and its role in the data lifecycle.
- Exploring common challenges in raw data and their impact on analysis.
Data Collection and Acquisition
- Data sources: databases, APIs, spreadsheets, text files, and more.
- Techniques for collecting data and ensuring data quality during collection.
- Collecting data from various sources.
Data Cleaning Techniques
- Identifying and handling missing values, outliers, and inconsistencies.
- Addressing duplicates and errors in the dataset.
- Cleaning real-world datasets.
Data Transformation and Standardization
- Data normalization and standardization techniques.
- Handling categorical data: encoding, binning, and feature engineering.
- Transforming raw data into usable formats.
Data Integration and Aggregation
- Merging and combining datasets from different sources.
- Resolving data conflicts and aligning data types.
- Techniques for data aggregation and consolidation.
Data Quality Assurance
- Methods for ensuring data quality and integrity throughout the process.
- Implementing quality checks and validation procedures.
- Case studies and practical applications of data quality assurance.
Dimensionality Reduction and Feature Selection
- Understanding the need for dimensionality reduction.
- Techniques such as PCA, feature selection, and reduction strategies.
- Implementing dimensionality reduction techniques.
Summary and Next Steps
Requirements
- Fundamental understanding of data concepts.
Target 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.
