  Flight delays hurt airlines, airports, and passengers. Their prediction is
crucial during the decision-making process for all players of commercial
aviation. Moreover, the development of accurate prediction models for flight
delays became cumbersome due to the complexity of air transportation system,
the number of methods for prediction, and the deluge of flight data. In this
context, this paper presents a thorough literature review of approaches used to
build flight delay prediction models from the Data Science perspective. We
propose a taxonomy and summarize the initiatives used to address the flight
delay prediction problem, according to scope, data, and computational methods,
giving particular attention to an increased usage of machine learning methods.
Besides, we also present a timeline of significant works that depicts
relationships between flight delay prediction problems and research trends to
address them.
  The published version of this paper is made available at
\url{https://doi.org/10.1080/01441647.2020.1861123}.
  Please cite as:
  L. Carvalho, A. Sternberg, L. Maia Gon\c{c}alves, A. Beatriz Cruz, J.A.
Soares, D. Brand\~ao, D. Carvalho, e E. Ogasawara, 2020, On the relevance of
data science for flight delay research: a systematic review, Transport Reviews
