Data Science Professional profiles definitions

Data Science Professional profiles definition (DSP)

This document presents the Data Science Professional profiles definition (DSP), a key component of the EDISON Data Science Competences Framework which is in turn based on the study of the demand and supply sides for Data Science competences and skills in research and industry, as well as existing standards, taxonomies and studies.

The Data Science Professional profiles definition (DSP) is another cornerstone component of the whole EDISON framework.

Read this page here - it contains the links to download the document.

O'Reilly Ideas blog: Doing good data science

The first in a new series of posts on data ethics at O'Reilly Ideas is an easy-to-read taste of the issues, including how data science relates to corporate culture:

https://www.oreilly.com/ideas/doing-good-data-science

“Data scientists, data engineers, AI and ML developers, and other data professionals need to live ethical values, not just talk about them.”

The research librarian of the future: data scientist and co-investigator

There remains something of a disconnect between how research librarians themselves see their role and its responsibilities and how these are viewed by their faculty colleagues. Jeannette Ekstrøm, Mikael Elbaek, Chris Erdmann and Ivo Grigorov imagine how the research librarian of the future might work, utilising new data science and digital skills to drive more collaborative and open scholarship. Arguably this future is already upon us but institutions must implement a structured approach to developing librarians’ skills and services to fully realise the benefits.

See blog post: http://blogs.lse.ac.uk/impactofsocialsciences/2016/12/14/the-research-librarian-of-the-future-data-scientist-and-co-investigator/ 

Nature article: Legal confusion threatens to slow data science

http://www.nature.com/news/legal-confusion-threatens-to-slow-data-science-1.20359

Researcher who spent months chasing permission to republish online data sets urges others to read up on the law.

03 August 2016

"... some of the data sets that he wanted to use had clear licences ­— and some of these prevented unrestricted redistribution, but others did not. The most frustrating part of his project, he says, was the feeling that good data were going to waste because their creators could not clarify whether he could republish them."

Article tools

Data Science courses you can take online... free...

Data Science courses you can take online... free... at Coursera...

https://www.coursera.org/browse/data-science

In Data Science, Coursera includes: Data Analysis, Machine Learning and Statistics.

Not sure that I would agree with that as an exhaustive definition of DS.

Data Analysis at John Hopkins, UPenn, UC San Diego, Georgia Tech... and more...

Machine Learning at Stanford...

Statistics everywhere...

Comprehensive list of data science resources

On the Data Science Central Blog:

We blended together the best of the best resources posted recently on DSC. It would be great to organize them by category, but for now they are organized by date. This is very useful too, since you are likely to have seen old entries already, and can focus on more recent stuff. Starred entries have interesting charts. 

http://www.datasciencecentral.com/group/resources/forum/topics/comprehensive-list-of-data-science-resources