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Data Visualization and Mining

       After reading chapters 6 and 7, visualization is a graphic that corresponds with the data being associated with it. It allows for people to interpret large amounts of data, without being lost in so many numbers or facts. In digital humanities projects, many home pages are extremely long with so much text. That makes it hard for the general public to sit and read it fully through and analyze it. Adding a visualization can make the article intriguing and desirable to keep reading.      Data mining and text analysis "is used for analysis of literary and aesthetic objects has promoted many immediate and strong responses, such as the claim that 'literature is not data"(Lamarche 2012). I always thought that literature and humanities could not be data as well. I realized after reading this chapter, that data mining is analysis that looks at patterns and extracts information in digital files. That mean it filters information not in a number form.      In the digital projec

Data Visualization & Mining

In Chapter 6 of The Digital Humanities Coursebook , the concept of information (data) visualization was introduced and subsequently expanded upon. In the most simple of terms, data visualization is the way that project creators are able to portray their findings in some form of visual form, such a chart, graph or image. Johanna Drucker describes data visualization as the following: "The visualizations are often more easily consumed than the complex research data on which they depend... Anything that can be quantified (given a numerical value) can be turned into a graph, chart, diagram, or other visualization" (Drucker 86). Graphs and the plotting of data has been engrained into educational systems for decades, and so the concept itself is not new. However, the usage of visualized data allows a larger group of people to view chunks of data, as many people may be unable to understand or see the meaning behind the data at its initial value. Yesterday, Today, and Tomorrow does a

Data Visualization and Mining

  I love the idea of being able to interpret data easier by visualizations. As we all have learned so far data is an endless wonder of information and that can get confusing. Chapter six discussed different forms of visualisation data like graphs, charts and diagrams. It also discusses the different important variables that graphics need to be interpreted correctly/easier like ize generally indicates quantity, but can signal importance, particularly with typography. Color, like shape, is very legible and makes distinctions highly visible, as does texture"(Drucker 98).  Speaking of interpreting data more easily, another thing aI learned about was data mining. In chapter seven, it talks about how data mining can be helpful when recognising large amount of data. It did however specify that " Data mining only takes place on the information literally in the file, so clarification about the process is essential" (Drucker 110) which makes sense with how big and complex we know

Data Visualization & Mining

 While reading chapters six and seven in the  Digital Humanities Coursebook  I was able to learn what data visualization is along with data mining. First off, I found out what the two terms mean. Data visualization is information that is portrayed through various charts, graphs, and images (Drucker). Chapter seven focused on data mining. I understood data mining to be a way to comb through an abundant amount of data in a relatively fast way. Data mining is relevant in humanities because “It has become a part of research methods in text, music, sound recording, images, and multimodal communications studies with tools customized for this purpose” this allows data mining to become a positive tool for digital humanities research (Drucker 110). These two new terms broadened my definition of digital humanities yet again.        When I was looking at Yesterday, Today, and Tomorrow I was able to see data mining in action. I saw data mining in this project due to the way that the tweets were or

Data Visualization & Mining

 In reading chapters 6 & 7 of The Digital Humanities Coursebook , I was able to extract new information to further expand my knowledge (thus simultaneously losing myself further in the abyss of all digital humanities can be), particularly concerning the topics of data visualization and mining. In terms of data visualization, I found the name itself to be fairly self-explanatory, however the details in its construction and representation provided a lot of information I didn't know. For example, I learned that there are two components to visualization: metrics and graphics. Through these components, information and data can be represented in numerous ways, and a choice between which visual representation to use can be made to best aid readability and consumption of the data. It is also noted in the text that one of the best ways to decide what kind of visual to use is to decipher whether the data is discrete or continuous. Before reading chapter 6, I had a rough idea of the benef

Data Visualization and Mining

 Data visualization is taking different points of information and placing them into an organized chart or graph. What that format looks like depends on the designer's intention. Some graphs, for instance, utilize a continuous graph to show a change in variable over time. Other graphs might use pie chart to display data with percentages. How the data is presented makes a difference in the way it is interpreted. In some cases, certain graphs can exaggerate information, such as Florence Nightingale's hospital chart, that dramatized the radius of operations. In other cases, data visualization can be ineffective or uncomprehendable due to poor graph choices or point display.  In Six Degrees of Francis Bacon, the map demonstrates a spiderweb of first and second degree connections. Meanwhile, Yesterday, Today, and Tomorrow, used similar format of Tweet concentrated bubbles. These provide great examples different ways to map data. They also show how data mining can be used. Data mining

Data and Visualization and Mining

Chapters 6 and 7 taught me a lot of new things in terms of data mining and visualization. To begin, data visualization is putting a given set of data into a graph. They help to see patterns that are happening through different types of graphs such as; bar, line, bubble, and pie charts. The important part about data visualization is how you choose to express it, as said in the text "The challenge is to understand how the information visualization creates an argument and then make use of the graphical format whose features serve your purpose" (90). The key thing to note when it comes to rhetorical graphs is questioning the creator of the content as sometimes it can be deceptive. I also learned about networks and the complexion and how it "is that the development of the system cannot be predicted-- because the processes are nonlinear and/or non-deterministic from a statistical standpoint.  Data Mining as defined in the book "is an automated analysis that looks for patt