Mertz Cleaning Data for Effective Data Science

Doing the other 80% of the work with Python, R, and command-line tools
1. Auflage 2021
ISBN: 978-1-80107-440-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 498 Seiten

No detailed description available for "Cleaning Data for Effective Data Science".

Autoren/Hrsg.

Mertz, David

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie

Inhaltsverzeichnis

Table of Contents - Data Ingestion – Tabular Formats
- Data Ingestion - Hierarchical Formats
- Data Ingestion - Repurposing Data Sources
- The Vicissitudes of Error - Anomaly Detection
- The Vicissitudes of Error - Data Quality
- Rectification and Creation - Value Imputation
- Rectification and Creation - Feature Engineering
- Ancillary Matters - Closure/Glossary

Über Autor(innen)

Mertz David:
David Mertz, Ph.D. is the founder of KDM Training, a partnership dedicated to educating developers and data scientists in machine learning and scientific computing. He created a data science training program for Anaconda Inc. and was a senior trainer for them. With the advent of deep neural networks, he has turned to training our robot overlords as well. He previously worked for 8 years with D. E. Shaw Research and was also a Director of the Python Software Foundation for 6 years. David remains co-chair of its Trademarks Committee and Scientific Python Working Group. His columns, Charming Python and XML Matters, were once the most widely read articles in the Python world.