Smith, Gary, and Jay Cordes. The Phantom Pattern Problem: The Mirage of Big Data. First edition. Oxford ; New York, NY: Oxford University Press, 2020. Cite

Koenzen, Andreas, Neil Ernst, and Margaret-Anne Storey. “Code Duplication and Reuse in Jupyter Notebooks.” ArXiv:2005.13709 [Cs], 2020. http://arxiv.org/abs/2005.13709. Cite

Chattopadhyay, Souti, Ishita Prasad, Austin Z. Henley, Anita Sarma, and Titus Barik. “What’s Wrong with Computational Notebooks? Pain Points, Needs, and Design Opportunities.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–12. CHI ’20. Honolulu, HI, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3313831.3376729. Cite

DePratti, Roland. “Jupyter Notebooks versus a Textbook in a Big Data Course.” Journal of Computing Sciences in Colleges 35, no. 8 (2020): 208–20. https://dl.acm.org/doi/abs/10.5555/3417639.3417658. Cite

Willis, Alistair, Patricia Charlton, and Tony Hirst. “Developing Students’ Written Communication Skills with Jupyter Notebooks.” In Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 1089–95. SIGCSE ’20. Portland, OR, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3328778.3366927. Cite

Thylstrup, Nanna Bonde, ed. Uncertain Archives: Critical Keywords for Big Data. Cambridge, Massachusetts: The MIT Press, 2020. Cite

Kwak, Haewoon, Jisun An, and Yong-Yeol Ahn. “A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017.” ArXiv:2005.01803 [Cs], 2020. http://arxiv.org/abs/2005.01803. Cite

Munro, Robert. Human-in-the-Loop Machine Learning. Shelter Island, New York: Manning, 2020. https://www.manning.com/books/human-in-the-loop-machine-learning. Cite

Wang, April Yi, Anant Mittal, Christopher Brooks, and Steve Oney. “How Data Scientists Use Computational Notebooks for Real-Time Collaboration.” Association for Computing Machinery, 2019. https://doi.org/10.1145/3359141. Cite

Rule, Adam, Amanda Birmingham, Cristal Zuniga, Ilkay Altintas, Shih-Cheng Huang, Rob Knight, Niema Moshiri, et al. “Ten Simple Rules for Writing and Sharing Computational Analyses in Jupyter Notebooks.” PLOS Computational Biology 15, no. 7 (2019): e1007007. https://doi.org/10.1371/journal.pcbi.1007007. Cite

Pandey, Parul. Interpretable Machine Learning, 2019. https://towardsdatascience.com/interpretable-machine-learning-1dec0f2f3e6b. Cite

Wikipedia. Confusion Matrix, 2019. https://en.wikipedia.org/w/index.php?title=Confusion_matrix&oldid=881721342. Cite

“Big Data Technologies: A Survey.” Journal of King Saud University - Computer and Information Sciences 30, no. 4 (2018): 431–48. https://doi.org/10.1016/j.jksuci.2017.06.001. Cite

Kery, Mary Beth, Marissa Radensky, Mahima Arya, Bonnie E. John, and Brad A. Myers. “The Story in the Notebook: Exploratory Data Science Using a Literate Programming Tool.” In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–11. CHI ’18. Montreal QC, Canada: Association for Computing Machinery, 2018. https://doi.org/10.1145/3173574.3173748. Cite

Narkhede, Sarang. Understanding Confusion Matrix, 2018. https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62. Cite

Kleinman, Scott, Mark D. LeBlanc, and Michael Drout. Hierarchical Clustering, 2018. http://scalar.usc.edu/works/lexos/hierarchical-clustering?path=manual. Cite

Randles, Bernadette M., Irene V. Pasquetto, Milena S. Golshan, and Christine L. Borgman. “Using the Jupyter Notebook as a Tool for Open Science: An Empirical Study.” In 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 1–2, 2017. https://doi.org/10.1109/JCDL.2017.7991618. Cite

Jupyter, Project. “Project Jupyter: Computational Narratives as the Engine of Collaborative Data Science.” Medium, 2017. https://blog.jupyter.org/project-jupyter-computational-narratives-as-the-engine-of-collaborative-data-science-2b5fb94c3c58. Cite

Ruchansky, Natali, Sungyong Seo, and Yan Liu. “CSI: A Hybrid Deep Model for Fake News Detection.” In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 797–806. CIKM ’17. Singapore, Singapore: Association for Computing Machinery, 2017. https://doi.org/10.1145/3132847.3132877. Cite

Wang, William Yang. “‘Liar, Liar Pants on Fire’: A New Benchmark Dataset for Fake News Detection.” ArXiv:1705.00648 [Cs], 2017. http://arxiv.org/abs/1705.00648. Cite

Mützel, Sophie. “Facing Big Data: Making Sociology Relevant , Facing Big Data: Making Sociology Relevant.” Big Data & Society 2, no. 2 (2015): 2053951715599179. https://doi.org/10.1177/2053951715599179. Cite

Hashem, Ibrahim Abaker Targio, Ibrar Yaqoob, Nor Badrul Anuar, Salimah Mokhtar, Abdullah Gani, and Samee Ullah Khan. “The Rise of ‘Big Data’ on Cloud Computing: Review and Open Research Issues.” Information Systems 47 (2015): 98–115. https://doi.org/10.1016/j.is.2014.07.006. Cite

“Beyond the Hype: Big Data Concepts, Methods, and Analytics.” International Journal of Information Management 35, no. 2 (2015): 137–44. https://doi.org/10.1016/j.ijinfomgt.2014.10.007. Cite

Ahonen, Pertti. “Institutionalizing Big Data Methods in Social and Political Research , Institutionalizing Big Data Methods in Social and Political Research.” Big Data & Society 2, no. 2 (2015): 2053951715591224. https://doi.org/10.1177/2053951715591224. Cite

Conroy, Niall J., Victoria L. Rubin, and Yimin Chen. “Automatic Deception Detection: Methods for Finding Fake News: Automatic Deception Detection: Methods for Finding Fake News.” Proceedings of the Association for Information Science and Technology 52, no. 1 (2015): 1–4. https://doi.org/10.1002/pra2.2015.145052010082. Cite

Lazer, David, Ryan Kennedy, Gary King, and Alessandro Vespignani. “The Parable of Google Flu: Traps in Big Data Analysis.” Science 343, no. 6176 (2014): 1203–5. https://doi.org/10.1126/science.1248506. Cite

Kitchin, Rob. “Big Data, New Epistemologies and Paradigm Shifts.” Big Data & Society 1, no. 1 (2014): 2053951714528481. https://doi.org/10.1177/2053951714528481. Cite

Kitchin, Rob. The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences. Los Angeles, California: SAGE Publications, 2014. Cite

Philip Chen, C. L., and Chun-Yang Zhang. “Data-Intensive Applications, Challenges, Techniques and Technologies: A Survey on Big Data.” Information Sciences 275 (2014): 314–47. https://doi.org/10.1016/j.ins.2014.01.015. Cite

Bail, Christopher. The Cultural Environment: Measuring Culture With Big Data, 2014. https://www.researchgate.net/publication/260705893_The_Cultural_Environment_Measuring_Culture_With_Big_Data. Cite

Chen, Min, Shiwen Mao, and Yunhao Liu. “Big Data: A Survey.” Mobile Networks and Applications 19, no. 2 (2014): 171–209. https://doi.org/10.1007/s11036-013-0489-0. Cite

Wu, Xindong, Xingquan Zhu, Gong-Qing Wu, and Wei Ding. “Data Mining with Big Data.” IEEE Transactions on Knowledge and Data Engineering 26, no. 1 (2014): 97–107. https://doi.org/10.1109/TKDE.2013.109. Cite

Burscher, Björn, Daan Odijk, Rens Vliegenthart, Maarten de Rijke, and Claes H. de Vreese. “Teaching the Computer to Code Frames in News: Comparing Two Supervised Machine Learning Approaches to Frame Analysis.” Communication Methods and Measures 8, no. 3 (2014): 190–206. https://doi.org/10.1080/19312458.2014.937527. Cite

Richards, Neil M., and Jonathan H. King. “Three Paradoxes of Big Data.” Stanford Law Review 66 (2013). https://www.stanfordlawreview.org/online/privacy-and-big-data-three-paradoxes-of-big-data/. Cite

Ward, Jonathan Stuart, and Adam Barker. “Undefined By Data: A Survey of Big Data Definitions.” ArXiv:1309.5821 [Cs], 2013. http://arxiv.org/abs/1309.5821. Cite

Sagiroglu, Seref, and Duygu Sinanc. “Big Data: A Review.” In 2013 International Conference on Collaboration Technologies and Systems (CTS), 42–47, 2013. https://doi.org/10.1109/CTS.2013.6567202. Cite

Labrinidis, Alexandros, and H. V. Jagadish. “Challenges and Opportunities with Big Data.” VLDB Endowment, 2012. https://doi.org/10.14778/2367502.2367572. Cite

Drout, Michael, and Leah Smith. How to Read a Dendogram, 2012. https://wheatoncollege.edu/wp-content/uploads/2012/08/How-to-Read-a-Dendrogram-Web-Ready.pdf. Cite

Boyd, Danah, and Kate Crawford. “Six Provocations for Big Data.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, 2011. https://papers.ssrn.com/abstract=1926431. Cite

Peng, Roger D. “Reproducible Research in Computational Science.” Science 334, no. 6060 (2011): 1226–27. https://doi.org/10.1126/science.1213847. Cite

Sebastiani, Fabrizio. “Machine Learning in Automated Text Categorization.” ACM Computing Surveys (CSUR) 34, no. 1 (2002): 1–47. https://doi.org/10.1145/505282.505283. Cite

Miller, M. Mark. “Frame Mapping and Analysis of News Coverage of Contentious Issues.” Social Science Computer Review 15, no. 4 (1997): 367–78. https://doi.org/10.1177/089443939701500403. Cite