Data Wrangling: Transforming Data for Analysis

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This lecture covers the process of data wrangling, focusing on transforming and preparing data for analysis. It explains the differences between ETL and data wrangling, the stages of data wrangling, and common issues encountered during the process such as data structure, accuracy, and temporality.

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Instructor

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Ontological neighbourhood

Business

Business administration: Data management

Related lectures (32)

Data Wrangling with Hive: Managing Big Data Efficiently

Covers data wrangling techniques using Apache Hive for efficient big data management.

Data Wrangling: Structuring and Wrangling Issues

Covers data wrangling stages, structuring techniques, and common issues in data preparation.

Data Modeling: Concepts and Applications

Explores data modeling concepts, SQL implementations, and practical applications in handling missing data.

Handling Data: Data Models and Wrangling

Explores data handling fundamentals, including models, sources, and wrangling, emphasizing the importance of understanding and addressing data problems.

Data Wrangling with Hadoop: Advanced Techniques

Covers advanced data wrangling techniques using Hadoop, focusing on Hive and HBase integration.