Data science is the business of learning from data, which is traditionally the business of statistics. Data science, however, is often understood as a broader, task-driven and computationally-oriented version of statistics. Both the term data science and the broader idea it conveys have origins in statistics and are a reaction to a narrower view of data analysis. Expanding upon the views of a number of statisticians, this paper encourages a big-tent view of data analysis. We examine how evolving approaches to modern data analysis relate to the existing discipline of statistics (e.g. exploratory analysis, machine learning, reproducibility, computation, communication and the role of theory). Finally, we discuss what these trends mean for the future of statistics by highlighting promising directions for communication, education and research.
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This idea is usually communicated through a venn diagram, e.g. http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram.
This includes both databases and mathematical representations of data.
For example, reproducible research would fall under this category and point 5.
We take the position that data science is the practice of broader statistics.
“A data scientist is a statistician who lives in San Francisco” (Bhardwaj 2017).
We do not claim this list is exhaustive.
Peter Naur uses the term “data science” but in a narrower sense, focusing more on computation.
e.g. see https://priceonomics.com/hadley-wickham-the-man-who-revolutionized-r/ and the quote about “The fact that data science exists as a field is a colossal failure of statistics”.
The literature is not consistent about the definitions of reproducibility and replicability. In this paper we use the definitions given here.
Writing code that continues to work overtime is non-trivial; it involves maintaining the same computing environment and managing dependencies correctly, e.g. the software packages the code uses change over time, version 1.1.1 might work the same as version 2.1.1.
Understanding the nitty-gritty details of how statistical software works is not trivial: how does the optimization routine determine convergence? Are the data mean centered by default? There is a lack in uniformity in how statistical software is written; we believe this is exacerbated by the lack of of statisticians writing statistical software.
Even if the code for a study is available, someone may still want to rewrite the code say in another language. In this case have the original code available to base the new code on is helpful.
Publishing messy code is still beneficial and certainly better than not publishing code (Barnes 2010).
The use of shallow means we can view a generalized linear model as a neural network with 0 layers. The more layers a network has, the more complex of a pattern it can find (Goodfellow et al. 2016).
i.e. the output of a predictive model may be interesting insofar as it helps us do something.
In other words, in many cases understanding is primarily a means to and ends for predictive problems and visa versa.
From Talking Machines season 3, episode 5. https://www.thetalkingmachines.com/.
This statement probably applies to non-quantitative fields. For example, some academics in comparative literature are more “empirical” in the sense they examine a particular body of work, draw conclusions and possibly generalize/relate their conclusions to other bodies of work. Other people in comparative literature apply “theoretical methods”.
e.g. L2 regularized (ridge) linear regression has a closed form while L1 regularization (LASSO) does not.
For example, if google’s algorithms mistakenly think someone is dead, then likely the rest of the world will too https://www.nytimes.com/2017/12/16/business/google-thinks-im-dead.html.
Our argument is that computation can help communication. Others have taken this idea further and use computation, specifically information theory, as a metaphor for communication, e.g. Doumont (2009).
For example, it is often suggested that code comments should describe why the code was written the way it was, not what the code is doing. For data analysis, where the target audience is probably less experienced with programming, describing the what may also be useful.
For more information and examples see http://rmarkdown.rstudio.com/.
e.g. see the list of people discussed in https://simplystatistics.org/2015/12/11/instead-of-research-on-reproducibility-just-do-reproducible-research/.
The complexity and time costs to making research reproducible is, in part, technical issue.
e.g. see each of the notes from https://idc9.github.io/stor390/.
e.g. including a lecture on communication in an undergraduate data science course: https://idc9.github.io/stor390/notes/communication/communication.html.
e.g. in an upper level undergraduate course such as UNC’s STOR 455: Statistical Methods I.
See for example https://www.inferentialthinking.com/chapters/13/prediction.html.
e.g. see the order of the chapters in the textbook: https://www.inferentialthinking.com/.
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This research was supported in part by the National Science Foundation under Grant No. 1633074. We would like to thank Deborah Carmichael for editorial comments.
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Carmichael, I., Marron, J.S. Data science vs. statistics: two cultures?. Jpn J Stat Data Sci 1, 117–138 (2018). https://doi.org/10.1007/s42081-018-0009-3
- Literate programming
- Machine learning