Bibliografia

Principal

• Rachel Schutt and Cathy O'Neil. 2013. Doing Data Science: Straight Talk from the Frontline. O'Reilly Media, Inc. Alberto Cairo. 2012. The Functional Art: An introduction to information graphics and visualization (1st. ed.). New Riders Publishing, USA. Voeneky, S., Kellmeyer, P., Mueller, O., & Burgard, W. (Eds.). 2022. The Cambridge Handbook of Responsible Artificial Intelligence: Interdisciplinary Perspectives. Cambridge: Cambridge University Press.: • • •

Secundária

• P. Mathur. 2028. Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance. Apress. P. Tattar, T. Ojeda, S. P. Murphy B. Bengfort, A. Dasgupta. 2017. Practical Data Science Cookbook, Second Edition. Packt Publishing. I. Foster, R. Ghani, R. S. Jarmin, F. Kreuter, J. Lane. 2016. Big Data and Social Science: A Practical Guide to Methods and Tools, 1st Edition. CRC Press, Chapman & Hall. T. W. Miller. 2015. Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python?. O'Reilly. M. N. Jones, 2016. Big Data in Cognitive Science (Frontiers of Cognitive Psychology), Taylor & Francis. F. Provost. 2013. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly. S. Few. 2004. Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press.: • • •