References#

Books#

  • Peng, R. D. (2016). R programming for data science. Available at https://bookdown.org/rdpeng/rprogdatascience/

  • Wickham, H., & Grolemund, G. (2017). R for data science: import, tidy, transform, visualize, and model data. Available at https://r4ds.had.co.nz/

  • 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/

  • Shrestha, S. (2020). Data Science Workflow Management: From Basics to Deployment. Available at https://www.springer.com/gp/book/9783030495362

  • Grollman, D., & Spencer, B. (2018). Data science project management: from conception to deployment. Apress.

  • Kelleher, J. D., Tierney, B., & Tierney, B. (2018). Data science in R: a case studies approach to computational reasoning and problem solving. CRC Press.

  • VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. O'Reilly Media, Inc.

  • 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.

  • Pérez, F., & Granger, B. E. (2007). IPython: a system for interactive scientific computing. Computing in Science & Engineering, 9(3), 21-29.

  • 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.

  • Shen, H. (2014). Interactive notebooks: Sharing the code. Nature, 515(7525), 151-152.