ANA330

Rattenbury, T., Hellerstein, J., Heer, J., Kandel, S., & Carreras, C. (2017). Principles of data wrangling: practical techniques for data preparation (First edition.). O’Reilly.
Read Chapters 3 and 7.
Chapter 3: The Dynamics of Data Wrangling
In this chapter, the authors go into more depth about data wrangling, the terminology used, and the introduction to transformation and profiling.
Chapter 7: Using Transformation to Clean Data
In this chapter, the authors discuss transformation of data to account for missing or invalid values.

Moses, Gavish, L., & Vorwerck, M. (2022). Data quality fundamentals: A practitioner’s guide to building trustworthy data pipelines. O’Reilly Media.
Read Chapter 6: Fixing Data Quality Issues at Scale.
In Chapter 6, the authors describe discuss how data teams can fix data incidents at scale by applying standard practices and principles of software incident management.

Chapple, M. (2018, August 22). Types of missing data - Cleaning bad data in R. LinkedIn. https://www.linkedin.com/learning/cleaning-bad-data-in-r/types-of-missing-data?autoplay=true&u=2168252

Tebeka, M. (2019, December 12). Filling in missing values; from the course: Data Ingestion with Python. https://www.linkedin.com/learning/search?entityType=ALL&keywords=analytic%20missing%20data%20complete%20case%20analysis&language=en_US&spellcheck=true&u=2168252