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Mapping Data: A guide for making #geodata visualizations | #ddj #methods #tools

Mapping Data: A guide for making #geodata visualizations | #ddj #methods #tools | Public Datasets - Open Data - | Scoop.it
luiy's insight:

As an add-on to our presentation we produced two more things, that some of you out there mind find helpful too:

 

- Mappable Toolset: The number of tools to process data, make maps, interactive visualizations etc. is continuously growing. While we love new tools, this leads to a situation that makes it quite hard to keep an overview of which tools are good for a certain tasks, where to find them and how much they cost. To keep track of the tools we've used so far and as a guide for others we thus collected our toolset. Have a look at it here:English version, German version.


-  Mappable Cheat-Sheet: Making maps and other visualizations with a geospatial component is certainly not a trivial tasks. There are many pitfalls, take alone spatial reference systems as an example, that might completely mess up your visualization if you don't handle them correctly. We thus created a checklist for making geodata visualizations in (data-driven) journalism. You can find it here: English version, German version.

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#DataScience Workflow: Overview and Challenges I #methods #research

#DataScience Workflow: Overview and Challenges I #methods #research | Public Datasets - Open Data - | Scoop.it
I provide an overview of the data science workflow and highlight some challenges that data scientists face in their work.

Via João Greno Brogueira
luiy's insight:

@luiy. Great article about #DataScience: the workflow design, methods and problematics. 

 

What do data scientists do at work, and what challenges do they face?

 

This post provides an overview of the modern data science workflow, adapted from Chapter 2 of my Ph.D. dissertation, Software Tools to Facilitate Research Programming.

The Data Science Workflow

The figure below shows the steps involved in a typical data science workflow.  There are four main phases, shown in the dotted-line boxes: preparation of the data, alternating between running the analysis andreflection to interpret the outputs, and finally dissemination of results in the form of written reports and/or executable code.

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