References#
Books#
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Peng, R. D. (2016). R programming for data science. Available at https://bookdown.org/rdpeng/rprogdatascience/
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Wickham, H., & Grolemund, G. (2017). R for data science: import, tidy, transform, visualize, and model data. Available at https://r4ds.had.co.nz/
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Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. Available at https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
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Shrestha, S. (2020). Data Science Workflow Management: From Basics to Deployment. Available at https://www.springer.com/gp/book/9783030495362
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Grollman, D., & Spencer, B. (2018). Data science project management: from conception to deployment. Apress.
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Kelleher, J. D., Tierney, B., & Tierney, B. (2018). Data science in R: a case studies approach to computational reasoning and problem solving. CRC Press.
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VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. O'Reilly Media, Inc.
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Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., ... & Ivanov, P. (2016). Jupyter Notebooks-a publishing format for reproducible computational workflows. Positioning and Power in Academic Publishing: Players, Agents and Agendas, 87.
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Pérez, F., & Granger, B. E. (2007). IPython: a system for interactive scientific computing. Computing in Science & Engineering, 9(3), 21-29.
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Rule, A., Tabard-Cossa, V., & Burke, D. T. (2018). Open science goes microscopic: an approach to knowledge sharing in neuroscience. Scientific Data, 5(1), 180268.
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Shen, H. (2014). Interactive notebooks: Sharing the code. Nature, 515(7525), 151-152.