50 years of Data Science – by David Donoho

David Donoho published a fascinating paper based on a presentation at the Tukey Centennial workshop, Princeton NJ Sept 18 2015. You can download the full paper from here. 

The paper got quite the attention on Hacker News, Data Science Central, Simply Stats, Xi’an’s blog, srown ion medium, and probably others. Share your thoughts in the comments.

Here is the abstract and table of content.


More than 50 years ago, John Tukey called for a reformation of academic statistics. In ‘The Future of Data Analysis’, he pointed to the existence of an as-yet unrecognized science, whose subject of interest was learning from data, or ‘data analysis’. Ten to twenty years ago, John Chambers, Bill Cleveland and Leo Breiman independently once again urged academic statistics to expand its boundaries beyond the classical domain of theoretical statistics; Chambers called for more emphasis on data preparation and presentation rather than statistical modeling; and Breiman called for emphasis on prediction rather than inference. Cleveland even suggested the catchy name “Data Science” for his envisioned field.

A recent and growing phenomenon is the emergence of “Data Science” programs at major universities, including UC Berkeley, NYU, MIT, and most recently the Univ. of Michigan, which on September 8, 2015 announced a $100M “Data Science Initiative” that will hire 35 new faculty. Teaching in these new programs has significant overlap in curricular subject matter with traditional statistics courses; in general, though, the new initiatives steer away from close involvement with academic statistics departments.

This paper reviews some ingredients of the current “Data Science moment”, including recent commentary about data science in the popular media, and about how/whether Data Science is really different from Statistics.

The now-contemplated field of Data Science amounts to a superset of the fields of statistics and machine learning which adds some technology for ‘scaling up’ to ‘big data’. This chosen superset is motivated by commercial rather than intellectual developments. Choosing in this way is likely to miss out on the really important intellectual event of the next fifty years.

Because all of science itself will soon become data that can be mined, the imminent revolution in Data Science is not about mere ‘scaling up’, but instead the emergence of scientific studies of data analysis science-wide. In the future, we will be able to predict how a proposal to change data analysis workflows would impact the validity of data analysis across all of science, even predicting the impacts field-by-field. Drawing on work by Tukey, Cleveland, Chambers and Breiman, I present a vision of data science based on the activities of people who are ‘learning from data’, and I describe an academic field dedicated to improving that activity in an evidence-based manner. This new field is a better academic enlargement of statistics and machine learning than today’s Data Science Initiatives, while being able to accommodate the same short-term goals.


1 Today’s Data Science Moment

2 Data Science ‘versus’ Statistics

2.1 The ‘Big Data’ Meme

2.2 The ‘Skills’ Meme

2.3 The ‘Jobs’ Meme

2.4 What here is real?

2.5 A Better Framework

3 The Future of Data Analysis, 1962

4 The 50 years since FoDA

4.1 Exhortations

4.2 Reification

5 Breiman’s ‘Two Cultures’, 2001

6 The Predictive Culture’s Secret Sauce

6.1 The Common Task Framework

6.2 Experience with CTF

6.3 The Secret Sauce

6.4 Required Skills

7 Teaching of today’s consensus Data Science

8 The Full Scope of Data Science

8.1 The Six Divisions

8.2 Discussion

8.3 Teaching of GDS

8.4 Research in GDS

8.4.1 Quantitative Programming Environments: R

8.4.2 Data Wrangling: Tidy Data

8.4.3 Research Presentation: Knitr

8.5 Discussion

9 Science about Data Science

9.1 Science-Wide Meta Analysis

9.2 Cross-Study Analysis

9.3 Cross-Workflow Analysis

9.4 Summary

10 The Next 50 Years of Data Science

10.1 Open Science takes over

10.2 Science as data

10.3 Scientific Data Analysis, tested Empirically

10.3.1 DJ Hand (2006)

10.3.2 Donoho and Jin (2008)

10.3.3 Zhao, Parmigiani, Huttenhower and Waldron (2014)

10.4 Data Science in 2065

11 Conclusion

You can download the full paper from here. 

Multidimensional Scaling with R (from “Mastering Data Analysis with R”)

Guest post by Gergely Daróczi. If you like this content, you can buy the full 396 paged e-book for 5 USD until January 8, 2016 as part of Packt’s “$5 Skill Up Campaign” at https://bit.ly/mastering-R

Feature extraction tends to be one of the most important steps in machine learning and data science projects, so I decided to republish a related short section from my intermediate book on how to analyze data with R. The 9th chapter is dedicated to traditional dimension reduction methods, such as Principal Component Analysis, Factor Analysis and Multidimensional Scaling — from which the below introductory examples will focus on that latter.

Multidimensional Scaling (MDS) is a multivariate statistical technique first used in geography. The main goal of MDS it is to plot multivariate data points in two dimensions, thus revealing the structure of the dataset by visualizing the relative distance of the observations. Multidimensional scaling is used in diverse fields such as attitude study in psychology, sociology or market research.

Although the MASS package provides non-metric methods via the isoMDS function, we will now concentrate on the classical, metric MDS, which is available by calling the cmdscale function bundled with the stats package. Both types of MDS take a distance matrix as the main argument, which can be created from any numeric tabular data by the dist function.

But before such more complex examples, let’s see what MDS can offer for us while working with an already existing distance matrix, like the built-in eurodist dataset:

> as.matrix(eurodist)[1:5, 1:5]
          Athens Barcelona Brussels Calais Cherbourg
Athens         0      3313     2963   3175      3339
Barcelona   3313         0     1318   1326      1294
Brussels    2963      1318        0    204       583
Calais      3175      1326      204      0       460
Cherbourg   3339      1294      583    460         0

The above subset (first 5-5 values) of the distance matrix represents the travel distance between 21 European cities in kilometers. Running classical MDS on this example returns:

> (mds <- cmdscale(eurodist))
                      [,1]      [,2]
Athens           2290.2747  1798.803
Barcelona        -825.3828   546.811
Brussels           59.1833  -367.081
Calais            -82.8460  -429.915
Cherbourg        -352.4994  -290.908
Cologne           293.6896  -405.312
Copenhagen        681.9315 -1108.645
Geneva             -9.4234   240.406
Gibraltar       -2048.4491   642.459
Hamburg           561.1090  -773.369
Hook of Holland   164.9218  -549.367
Lisbon          -1935.0408    49.125
Lyons            -226.4232   187.088
Madrid          -1423.3537   305.875
Marseilles       -299.4987   388.807
Milan             260.8780   416.674
Munich            587.6757    81.182
Paris            -156.8363  -211.139
Rome              709.4133  1109.367
Stockholm         839.4459 -1836.791
Vienna            911.2305   205.930

These scores are very similar to two principal components (discussed in the previous, Principal Component Analysis section), such as running prcomp(eurodist)$x[, 1:2]. As a matter of fact, PCA can be considered as the most basic MDS solution.

Anyway, we have just transformed (reduced) the 21-dimensional space into 2 dimensions, which can be plotted very easily — unlike the original distance matrix with 21 rows and 21 columns:

> plot(mds)


Does it ring a bell? If not yet, the below image might be more helpful, where the following two lines of code also renders the city names instead of showing anonymous points:

> plot(mds, type = 'n')
> text(mds[, 1], mds[, 2], labels(eurodist))

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