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Course Outline
Introduction
- Understanding the importance of data preparation in analytics and machine learning
- Data preparation pipeline and its role in the data lifecycle
- Exploring common challenges in raw data and the impact on analysis
Data Collection and Acquisition
- Sources of data: 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
- Dealing with duplicates and errors in the dataset
- Cleaning real-world datasets
Data Transformation and Standardization
- Data normalization and standardization techniques
- Categorical data handling: 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 like PCA, feature selection, and reduction strategies
- Implementing dimensionality reduction techniques
Summary and Next Steps
Requirements
- Basic understanding of data concepts
Audience
- Data analysts
- Database administrators
- IT professionals
14 Hours
Testimonials (1)
I generally enjoyed the knowledge of the trainer.
