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Starts 8 June 2025 02:08
Ends 8 June 2025
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Data Munging to Wrangling - 7 Steps to Mastering Data Preparation for Data Science
Discover essential techniques for data preparation in AI/ML projects. Learn to source, clean, and transform data effectively, enhancing the quality and predictive power of machine learning models.
PASS Data Community Summit
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PASS Data Community Summit
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1 hour 16 minutes
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Overview
Discover essential techniques for data preparation in AI/ML projects. Learn to source, clean, and transform data effectively, enhancing the quality and predictive power of machine learning models.
Syllabus
- Introduction to Data Preparation
- Step 1: Data Sourcing
- Step 2: Data Understanding
- Step 3: Data Cleaning
- Step 4: Data Transformation
- Step 5: Data Enrichment
- Step 6: Data Reduction
- Step 7: Data Validation and Testing
- Conclusion and Best Practices
- Practical Project
Importance of data preparation in AI/ML
Overview of the 7-step process
Identifying data needs
Exploring various data sources
Data collection techniques
Exploring data structure and content
Statistical data exploration
Identifying data outliers and anomalies
Handling missing data
Techniques for dealing with noise and errors
Data deduplication methods
Data normalization and standardization
Feature scaling and selection
Encoding categorical variables
Data integration from multiple sources
Augmentation techniques
Use of external datasets for enrichment
Dimensionality reduction techniques
Feature extraction and selection
Data summarization
Ensuring data quality and integrity
Data validation techniques
Creating and using validation datasets
Recap of key techniques and tools
Tips for efficient data preparation
Common pitfalls and how to avoid them
Apply the 7-step process on a real-world dataset
Present findings and insights
Subjects
Conference Talks
