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Training in Data Science for Public Officials in Madrid

Introduction to the elements of data science for public servants: open data, visualization and public policy

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We recount relevant points that we touched on in the series of webinars to introduce data science in public management in Madrid. We worked on four main fronts during the training: data structuring and access to open data, data visualization to reach larger audiences, identification of common errors in communicating information, and support for public policies using open data.

Telling data stories to citizens

This session provided an introduction to data story-telling from a public service perspective. We touched on concepts about communication with data and some examples of projects that have used public information to have a more significant impact.

Takeaway: Many times, we do not need large volumes of data to tell a story. In many cases, we could have a single data point, which would be enough to share a message. For example, the increase of people without access to health services could trigger a prompt response from the authorities. In other cases, we could start with big data, such as the public tree census. Still, we make decisions with aggregated information, preferably visually, to help us answer questions such as the neighborhoods with the highest concentration of trees in poor condition.

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Structuring and visualization of data

In this session, we showed how to identify the most common errors in a database and better organize data tables. Data structuring helps as a data interrogation technique to support you in discovering good stories behind the data. We covered multiple examples to select which type of graphic or map works best in different contexts. We concluded with some design tips for making visualizations.

Takeaways: Have you tried to analyze public information without success simply because you could not get the data right? Consolidating data from multiple sources or loading it into excel can be a challenge sometimes. In addition, there are significant difficulties in structuring information. It may be that the information does not exist. If it does exist, it may be in the wrong formats. If it is in a suitable format, it may not necessarily be verified. Don’t forget; there is also data of excellent quality that is in the wrong formats.

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Identify communication errors in communicating data

We take a tour of examples that show how data and visualization can mislead an audience. The use of inappropriate visual cues, color palettes, and context manipulations can be powerful tools for deception.

Takeaways: There are common errors in the visual communication of information. For instance: working with absolute values ​​instead of calculating rates can lead to erroneous perceptions about rankings. Working with maps also represents a challenge; many times, maps can exaggerate or undermine the perception of data values because regions can be more or less populated areas. Finally, we close our talk with some other cases about using visual representations to confuse assembly members and “resolve” geopolitical conflicts.

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Support for public policies with open information

In the last session of the training cycle, we addressed the topic of using public information to support policymaking. Current challenges of governments and public entities show there is space using better tools to take advantage of the multiple sources of public information that can spur government innovation. We offer examples of innovative use cases of data for supporting government administrators around the world.

Takeaways: With such varied sources of information, it is often difficult to find the appropriate data for a particular topic. Often, the right information is not as easy to process as those in text documents. In these cases, different text processing tools allow us to shed light on data proxies to understand things like public investment in the SDGs.

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