Data Munging to Wrangling - 7 Steps to Mastering Data Preparation for Data Science

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
Importance of data preparation in AI/ML
Overview of the 7-step process
• Step 1: Data Sourcing
Identifying data needs
Exploring various data sources
Data collection techniques
• Step 2: Data Understanding
Exploring data structure and content
Statistical data exploration
Identifying data outliers and anomalies
• Step 3: Data Cleaning
Handling missing data
Techniques for dealing with noise and errors
Data deduplication methods
• Step 4: Data Transformation
Data normalization and standardization
Feature scaling and selection
Encoding categorical variables
• Step 5: Data Enrichment
Data integration from multiple sources
Augmentation techniques
Use of external datasets for enrichment
• Step 6: Data Reduction
Dimensionality reduction techniques
Feature extraction and selection
Data summarization
• Step 7: Data Validation and Testing
Ensuring data quality and integrity
Data validation techniques
Creating and using validation datasets
• Conclusion and Best Practices
Recap of key techniques and tools
Tips for efficient data preparation
Common pitfalls and how to avoid them
• Practical Project
Apply the 7-step process on a real-world dataset
Present findings and insights

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Subjects

Conference Talks