دانلود کتاب بصری سازی داده ها در R و پایتون

The idea of this handbook came to me when I noticed something that made me pause and reflect. What I saw was that when I mentioned data visualization to a person who know just a little about it, perhaps adding that it involves representing data and the results of data analysis with figures, sometimes even interactive one, the reaction was often of curiosity with a shade of perplexity, the name sounded nice, but what is it, exactly? After all, if we have a table with data and we want to produce a graph, isn’t it enough to search in a menu, choose the stylized figure of the graph you want to create and click? Is there so much to say to fill an entire book? When I also add that what I was talking about were completely different graphic tools from those of office automation and that, to tell the truth, it doesn’t even stop at the graphics, even if they are interactive, but there are also dashboards, i.e. the latest evolution of data visualization, when real dynamic web applications are created, then the expression of the interlocutor was generally crossed by a shadow of concern. At that moment, I typically threw the ace up the sleeve by saying that in data visualization there are also maps, geographical maps – why not? – those are data too, they are spatial data, geographical data, and the maps are produced with the zoom, the flags, colored areas, and also cartographicmaps, you may work with maps of New York, Tokyo, Paris, Rome, New Delhi, you name it. At that point the interlocutors were usually looking puzzled, the references they had from the common experience were lost and doesn’t really know what this data visualization is about, only that there actually seems to be a lot to say, enough to fill an entire book. If anyone recognizes themselves in this interlocutor, be assured that you are in good company. Good in a literal not figurative sense, because data visualization is the Cinderella of data science that many admire but always from a certain distance, it arrives last and at the best moment it is forced to step back because there is no longer enough time to teach, study, or practice it. Yet, it frequently happens that those who, given the right opportunity to study and practice it, sense that it could be decidedly interesting, certainly prove useful and applicable in an infinite number of fields. This is due to a property that data visualization has and is instead absent in data analysis or code development: it stimulates visual creativity together with logic. Even statisticians and programmers use creativity, those who deny it have never been neither of them, but that is logical creativity. With data visualization, another dimension of otherwise neglected data science comes into play, the visual language combined with computational logic, meaning that data are represented with an expressive form that is no longer just logical and symbolic, but also perceptive, sensorial, shapes together with colors come into play, the once passive observer starts interacting, or projections of geographical areas suddenly become artifacts to use in a visual communication. Data visualization conveys different knowledge and logic for an expressive form that always has a double nature: computational for the data that feeds it, visual and sometimes interactive for the language it uses to communicate with the observer. There is enough to fill not a single book, in fact, what is contained in this book is a part of the discourse on data visualization, the one more practical and operative, other publications approach data visualization considering complementary aspects, such as the aesthetical composition of graphics, the storytelling behind a visual communication, and the syntax and semantic of a visual language together with the sensorial perception and psychology, and there is a lot to say for each one of these topics. All of them are essential for a complete understanding of the aim and extent of data visualization, but together they just don’t fit in one single handbook, unless presented in a truly superficial fashion, for this reason almost every book on data visualization focuses more explicitly on a few of those aspects. This book is dedicated to the more operational and computational issues, because you have to know the low-level logic behind modern data visualization artifacts and you have to know and practice with tools, they are not all alike, “just pick the easiest to use and you’re all set” is definitely not a good advice and, given the liveliness of the proprietary data visualization tools’ market, it is easy to forget about open-source ones, which instead rival and often surpass what proprietary tools are able to offer; may be with a little more of initial efforts, but not much. To conclude, data visualization probably deserves better consideration in educational programs and a recognition as a coherent and evolving discipline. It could be a lot of fun to study and practice it, it could make also you pause and reflect about tools for communicating data science results with a visual language, and it includes many different aspects from diverse disciplines, both theoretical and practical, all converging and enmeshing in a coherent body of knowledge. These are all good characteristics for curious persons. The Cinderella role of data visualization can be overcome by recognizing its educational and professional value and, no less important, its creative stimulus.