En el ejercicio de esta semana nos proponían la lectura de unos cuantos artículos (1 2 3 4) relacionados con el mundo de la universidad. Concretamente con el concepto de Tenure. Además se nos proporcionaba un dataset con porcentajes y números que debería valernos para validar o refutar las hipótesis formuladas en dichos artículos.

My idea for this visualization is to make an interactive graph that lets the user to navigate through the different levels of information that are provided by the data. The first level shows the aggregated data of the tenured faculties in percentage and numbers. These graphics shows an overview of our story. As we can see it looks like there isn’t a serious problem. At the end the % decrease of tenured faculties is small although the total numbers of tenured is growing during this period of time. We’ve to remember that the tenure is something for the whole life.

The second level of information that I want to show is related to the rank of the faculties. We’ve data for six types of faculties (professor, associate, assistant, instructor, lecturer and other). My idea is to let the user the freedom to navigate through this information. In the visualization you could have a select menu with these types that will change the graphics. I’ve attached the graphics for the professor rank

If in the previous level the user can filter by rank in this level my idea is to user the type of track (tenured, on track, non tenured) to configure the graph. I’ve included two graphics one for tenured and other for tenured track

Most of the articles put the emphasis in the point that the university is changing the tenured with the non-tenured. If we look at these graphics we can see very clear that this hypothesis is incorrect. We can see that the % of non-tenured faculties is mostly the same during the period analyzed.

Esta semana he comenzado con mi primer MOOC. La materia en la cual me he matriculado es “Introduction to Infographics and Data Visualization”. La razón principal por la que he elegido este curso es porque durante las charlas de Hacks & Hackers ya han aparecido varios proyectos de visualización que me han resultado muy interesantes y segundo como buen programador el gusto que tengo en el diseño es nulo y tengo la esperanza de aprender algo de diseño que pueda aplicar a mi vida de técnico.

Para la primera semana de trabajo hemos tenido que analizar la siguiente infografía que muestra cuando es el mejor momento para publicar en twitter / facebook. Nuestro trabajo ha consistido en criticarla y proponer un diseño alternativo. A continuación incluyo mis comentarios y propuestas.

The objective of the infographic is to show when we should have more impact publishing on two social networks (twitter and facebook). The 3D graphic is difficult for the interpretation and analysis. If you don’t read the text that surrounds the graphics it is difficult to know what is exactly the optimum time for publishing. The infographic presents different analysis for the networks too. The data from twitter is more useful and includes the best time period and the avoid time period. With facebook the data presented is different: best time of the week and best time period.

For both networks, the infographic includes when the networks gets the most traffic. Because this information isn’t put together with the click-through (CTR) graphics it’s difficult to analyze if exists correlation between both. Although the text included don’t facilitate the interpretation: “more traffic more competition”. I don’t know if it’s good or bad to publish on high traffic periods.

The headline I’ve got for the infographic is related more with the use of the networks than for the impact of the publishing of the networks. Twitter is a network mostly used during workdays, the use goes down during the weekend (a lot of companies stops working Fridays at 3:00 P;). Facebook has a more anarchic use during the week. What we can see is that effectiveness of publishing increase around the lunch time: 1-3PM for twitter and 1-4PM for Facebook.

There are two variables that will improve the analysis  One is the number of users connected at a time to the network and the other is the reach of the publications. The CTR takes the numbers of users that see your post and divides by the numbers of users that click or read the post. A 50% CTR looks good but maybe only one person read the post. Although the active users on Facebook (800M) and twitter (200M) are very different. Sometimes the CTR isn’t a very good indicator.

I’ve tried to rewrite the infographic with two different charts. I couldn’t find the exact data so I’ve tried to guess the correct values.

Grafica que compara el % de CTR por horas del día entre facebook y twitter.

Gráfica de barras que compara el % CTR medio entre las redes sociales twitter y facebookk