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Perspectives on Data Science for Software Engineering. Book • Edited by: Tim Menzies, Laurie Williams and Thomas Zimmermann. Browse book content.
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Is specialisation inevitable or are generalist data scientists here to stay? Please let me know privately , via Twitter , or in the comments section. I think exactly the same, but for the moment the title is somewhat needed. Like Liked by 1 person. I think this will change over time as data scientists or whatever they will be called roles get further defined. Data is data, it is just raw numbers.

Sorry, I guess that was a bit of a rant ….

Perspectives on Data Science for Software Engineering

Yeah, people like the word data more than the word database these days. There are also the various places where you can put data. You can drown it in a lake, for example…. Like Like. Reblogged this on codefying and commented: Especially like the antiparallel structure of scientific inquiry and engineering design.

I find it interest that, as you said, data analysis is very useful if done by effective hires. Otherwise, you could come to conclusions that miss the mark.

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It is important to have those who are properly qualified analyze data accurately. Great article!

I also feel that anybody with an experience of 5 years and more in Data Science, can be considered for a Data Scientist role. Professionals with lesser experience can always have their roles as Data Analysts or Data Engineers.

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    Perspectives on Data Science for Software Engineering. Date: pages: ISBN: The idea for this book was created during the conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss. Presents the wisdom of community experts, derived from a summit on software analytics Provides contributed chapters that share discrete ideas and technique from the trenches Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data Presented in clear chapters designed to be applicable across many domains.

    Continuously experiment to assess values early on — University of Helsinki

    In Summary… Structure your unstructured data first! What are the Key Entities?

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    What are the Key Tasks? Validate and Calibrate Your Data Step 4. So What Should Practitioners Do? Data science revolution in process improvement and assessment? What to Do on a Tight Budget? Software analytics under the lamp post or what star trek teaches us about the importance of asking the right questions Abstract Prologue Learning from Data Which Bin is Mine? Epilogue What can go wrong in software engineering experiments? His research includes artificial intelligence, data mining and search-based software engineering. He is best known for his work on systematic mining of version archives and bug databases to conduct empirical studies and to build tools to support developers and managers.

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