The top ten languages generated unit sales of 10,, for the 8-year period, while the second ten generated 2,, in the same period. With so many languages to choose from, makes me wonder if any language will every see the numbers that Java experienced in the early timeframe. This pretty much indicates that the last quarter of was roughly the same as the last quarter of from the programming languages perspective. Most of the bright green, which indicates rapid growth, is in the bottom right hand corner and small in size.
This means there was some growth with small languages in the fourth quarter of I have grouped these languages by total number of units sold between As you can see in the table below, the Large and Major languages were up collectively, and everything from Mid-Major and below was down. However books where the language was the focus, ended up 74, ahead of even though the Mid-Major and below lost , units compared to last year.
For the sake of grouping and presenting this information in a more readable format, we have classified the categories for the languages in this way with the following headers:. Think about it this way: An efficiency of 1 is the market average: A publisher that achieves its share with fewer titles will have a higher ratio. In three publishers continue to have an efficiency of more than 1: Publishers under the 1. A note of caution though, some publishers have many evergreen titles , which can skew this data.
Typically, older titles sell fewer units each subsequent year. But this is not always true, as some titles continue to sell like they are newly released. Head First Design Patterns is one example, still selling more than the majority of brand-new titles.
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So efficiency could be thought of as a frequency ratio rather than a true efficiency measure, because it is very efficient to publish a title and have it sell for years. A true efficiency metric would take into account all titles published by all publishers and how many make it into the Top Another caution is that many titles have new editions release every few years so those titles will not become evergreen.
And some publishers have titles that never make the Top , so we will not be able to count them for or against an efficiency metric because they are missing from the dataset. The chart below shows imprints that have a percentage of titles aged in ranges from years, years, and less than five years. The range is the evergreen status and the bar indicating that is green. This is purely titles that were published between X date and now. Surprisingly it looks as though Microsoft Press and New Riders are the most balanced, whereas Sams, Prentice Hall and Addison Wesley are more on the evergreen side of things.
Manning, Apress and Wrox are the publishers more heavily weighted in the less than five years area. It is a new science. Many disciplines are seeing the emergence of a new type of data science and management expert, accomplished in the computer, information, and data sciences arenas and in another domain science. These individuals are key to the current and future success of the scientific enterprise. However, these individuals often receive little recognition for their contributions and have limited career paths.
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So the complimentary scarce factor is the ability to understand that data and extract value from it… I do think those skills—of being able to access, understand, and communicate the insights you get from data analysis—are going to be extremely important. Managers need to be able to access and understand the data themselves. March Kirk D. This is true for both specialists scientists and non-specialists everyone else: the public, educators and students, workforce.
Specialists must learn and apply new data science research techniques in order to advance our understanding of the Universe. Non-specialists require information literacy skills as productive members of the 21st century workforce, integrating foundational skills for lifelong learning in a world increasingly dominated by data.
Obviously, I whole-heartedly agree. Heck, I'd go a step further and say they're sexy now— mentally and physically. However, if you went on to read the rest of Varian's interview, you'd know that by statisticians, he actually meant it as a general title for someone who is able to extract information from large datasets and then present something of use to non-data experts… [Ben] Fry… argues for an entirely new field that combines the skills and talents from often disjoint areas of expertise… [computer science; mathematics, statistics, and data mining; graphic design; infovis and human-computer interaction].
And after two years of highlighting visualization on FlowingData, it seems collaborations between the fields are growing more common, but more importantly, computational information design edges closer to reality. We're seeing data scientists —people who can do it all— emerge from the rest of the pack.
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June Troy Sadkowsky creates the data scientists group on LinkedIn as a companion to his website, datasceintists. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. Unfortunately, simply enumerating texts and tutorials does not untangle the knots. Therefore, in an effort to simplify the discussion, and add my own thoughts to what is already a crowded market of ideas, I present the Data Science Venn Diagram… hacking skills, math and stats knowledge, and substantive expertise.
Is it just a faddish rebranding of statistics? KMIS, Ekinge, R. Fontaine, M. Frank, U. In Barnes, S.
A Very Short History Of Data Science
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Summary of findings
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