The number pad

Many people say we are in the information era, but it seems that we are passed this. Nowadays, information is within everyone's reach, about everything and as much as we want. Data is not the issue anymore, at least most of the time. The real issue is  how to analyze the data.  It seems that having information is not the problem now, but actually having too much  data. One of the places in which we can find too much data are social networks. The richness of social networks is that they are a continuous flow of interesting data, what I like to call social data .  Social data is so rich as you can extract information from it in so many ways. One is to analyze what people express over a specific topic on social media. To this end, I developed  a way to identify the most important ideas found on a stream of user comments. Basically an algorithmic summary tool.  With a data set of a few tens of user comments, it is easy to grasp the general feelings and thoughts that

Given the importance of the recent Zika outbreak, I decided to perform some analysis on how the virus is spreading. I found very good data about Puerto Rico, so decided to focus there. With data from each municipality in Puerto Rico, it is possible to find a connectivity map of the island according to how the virus spreads there. By analyzing the growth of cases over time in each municipality treat them as independent series and performing correlation analysis, we have that the most relevant connectivity groups are given by the following graph. There are 5 clear connectivity groups based on their response to the virus, Group 1 Group 2 Group 3 Group 4 Group 5 And the strongest connectivity is given by the following graph, This connectivity is given by an adjacency matrix obtained by calculating the r² coefficients among the municipalities. In this graphs are displayed only the strongest r² coefficients. 

A portfolio analysis can be performed in order to analyze how effective the NBA teams were during the 2016 season. It is possible to think of both Eastern and Western Conferences to be a benchmark  that we can reference teams to. When we analyze each team's performance against the overall performance of the entire benchmark, it is possible to discover how effective and consistent teams are. This can tell us how risky or stable a team is, and how much of a pay off teams offer for being risky.  With this we can find that the most stable teams in the Eastern Conference are Chicago and Atlanta, and in the Western Conference were New Orleans and Utah. On the other hand, the most volatile teams were Philadelphia and New York in the Eastern Conference, and Houston and LA Lakers in the Western Conference.  Likewise, it can be found that the most representative teams for the performance of the Eastern Conference were Philadelphia and Miami, and for the Western Confere

Analyzing statistics in modern day sports is one of the prime sport-fan pastimes. However it is possible to capture more information than just a plain statistical description of the data. For example, it is possible to use financial models and techniques to understand performance in sports in a more insightful way. In  particular, in the case of the recent NBA finals, we can analyze the performance of each player as if it were a financial asset.  A financial asset or security is anything that has the potential of giving a financial return overtime, such as stocks, bonds, treasury bills, and others. In this way, our goal is to analyze and asses a player's performance, and identify how reliable and efficient he is. In the case of the 2016 NBA Champions, the Cleveland Cavaliers, we can perform this analysis by studying the player's individual performance compared to that of their conference during the 2016 season. Here we can determine how sensitive a player is wi