Practical Data Wrangling
In this workshop, we will cover practical data wrangling techniques for numerical, categorical, time series, text, event/log, and image data. Participants will learn how to detect and handle missing values, outliers, inconsistencies, duplicates, and formatting problems. We will demonstrate methods for transforming and encoding categorical variables, parsing and aggregating temporal data, processing unstructured text, analyzing event logs, and preparing image datasets for machine learning and analytics. Each session combines concepts, demonstrations, and hands-on exercises using realistic datasets to help participants develop practical skills that can be applied immediately in downstream modeling tasks.
Practical information:
Georganiseerd door:
Key takeaways
In this workshop, participants will learn how to systematically clean and structure different types of real-world data, including tabular, time series, text, event/log, and image data.
By the end of this workshop, participants will:
- gain practical experience in building reproducible data wrangling pipelines that improve data quality and usability for downstream data analysis and machine learning.
- understand common pitfalls in messy datasets and how to address them effectively using standard techniques and tools.
- be able to confidently transform raw datasets into well-structured inputs suitable for downstream modeling and analysis tasks.
Tentative Schedule
Day 1
- Introduction
- Data Types and Data Storage Formats
- Numerical Data Wrangling
- Categorical Data Wrangling
- Time Series Data Wrangling
Day 2
- Text Data Wrangling
- Event and Log Data, and Image Data Wrangling
- Wrangling Other Data Types
- Summary and Key Takeaways