Assignment 9

I had never worked with R before this assignment (though obviously I remembered hearing about it in previous lectures), so it was an interesting experience. I don't personally have much experience with coding so at first imputing what I wanted was challenging, but eventually I got used to it. It ended up fascinating to see the graph build itself before my eyes however, rather than just seeing the final product. I'm interested in learning more about R and bettering my understanding of it (including having a better understanding of how to insert colors-I tried to get each bar to correspond to each color but could not get it to work).

Assignment 8

After generating the Chi-square results, here is what I have found:

-Goals: chi-squared equals 0.000 with a P value of 1.
-Grades: chi-squared equals 0.533 with a P value of 0.7661.
-Popular:chi-squared equals 0.982 with a P value of 0.6119.
-Sports: chi-squared equals  0.003 with a P value of  0.9987.

From the results, I gather that the biggest difference between the actual and expected results was how many students valued popularity the most in each group, and the least (actually no difference) was how many students valued goals.

Assignment 7

Mean: 55,303,632.375 (FB), 36,042,208.5 (T)

Median: 57,963,191 (FB), 37,133,201 (T)

Standard Deviation: 15,979,901.476981508 (FB), 7,783,594.278588524 (T)

Displaying the data in a bar graph really conveys how more users overall are following celebrities social media through Facebook rather than Twitter. It's easy to look at and gain general knowledge about the data overall (Rhianna has the most Facebook likes, Shakira has the least amount of followers on Twitter, etc). However, there are downsides to this model as well. If the data is very close to one another, like Justin Bieber and Katy Perry's Twitter followers, then it's hard to tell which is greater.

Assignment 6

For this assignment I chose to use the Wolfram Alpha program on my Facebook profile to see what kind of data it could produce from it.

Some of the information it gathered was more obvious, for instance that I currently live in Tampa and that I'm 21 years old. However the data that it gathered from my friends list was very interesting. It revealed the average age of my friends list (26), that most of my friends are women, that most of them are in relationships, and that with the exception of four people, most of my friends are mutual friends. If a company were to use my Facebook page to try and market to me, they would likely be able to appeal to most of my friends list as well.

Another insight the program gave me was that it noticed I rarely tag anyone in any of the photos I upload. This can occasionally cause problems since I won't remember who was in the photos later on, or where I was when it was taken. It also noted that most of my posts had been made within the past few years-I hadn't really used Facebook until I started college.

Assignment 5

This map is made up of the data set provided in class, of homicides in Chicago.

Assignment 4

Figure 1 is a Descriptive model. It's a summary of the given data, most often used as a table or graph. The data presented is organized and presented in a way that displays the most obvious features about it. From the charts we could reasonably deduce the mean, median, and mode of the data and see if there is any skewness. 

Figure 2 is a Predictive model. The chart is predicting what the scores of the students and instructor will most likely be based off the given data. It cannot predict the future-however it can determine what might happen that includes risk assessments in its analysis. Predictive statistics typically help business owners understand their customers better, identity new opportunities for growth, or spot a potential problem. For this figure, it predicts the scores will lower based off previous data. 

Figure 3 is a Inferential model. It draws conclusions based off a sample of a bigger data set. This particular figure wants to know if Rick Perry has a chance of winning in the upcoming primary election. Asking every registered Republican in the nation would be impossible, so the chart draws from a smaller sample-a poll. Measuring a sample of a bigger population draws conclusions about the population as a whole. 

Assignment 3

Robin Camarote's "4 Great Resources for Presenting Your Data Creatively" list resources that can help inspire anyone who needs to create a data visualization. Each source contains sample charts/graphs/other visualizations that help fully convey how differently information can be displayed. The article, like the lecture, explains that people need visualization in order to fully understand the full scope of big data. While regular bar graphs are appropriate for some projects, creativity for visualizations can also be rewarding.

Another article on Forbes, "Big Data Needs More 'Creative Types'", explains that the data science field should be populated with creative, arty people. These people, whom the article refers to as "data artists" are able to combine knowledge about statistics and problem solving skills to successfully portray a story out of large amounts of data. In the lecture it was explained that some data visualizations are able to point out certain trends or inconsistencies more than others. Data artists are able to discern patterns from information in unconventional ways, that most people simply are unable to do.

Denise Lu's "7 Data Viz Sites to Inspire Your Creative Eye" features a list of sites that display interesting, out of the box, visual displays of data. Some sites put more of an emphasis on the asethetics they offer while others are optimal for different types of data. For example, "Chart Porn" is frequently used for political and financial graphics. The lecture gave examples of several measurements of data (gender, Twitter users, etc) out of the many possibilities. Knowing about as many visualizations as possible makes it easier to think up new ways to display a given data set.


Referenced articles:

http://www.inc.com/robin-camarote/look-smart-with-inspiration-from-these-top-4-data-visualization-sites.html

http://www.forbes.com/sites/teradata/2015/01/30/big-data-needs-more-creative-types/

http://mashable.com/2013/10/01/data-viz-sites/#gkbjZh.qDuk_