Quản trị dữ liệu và trực quan hóa

An unprecedented amount of data is being generated at increasingly rapid rates in many disciplines. Every day retail companies collect data on sales transactions, organizations log mouse clicks made on their websites, and biologists generate millions of pieces of information related to genes.

If you are a scientist, an analyst, a consultant, or anybody else who has to prepare technical documents or reports, one of the most important skills you need to have is the ability to make compelling data visualizations, generally in the form of figures. Figures will typically carry the weight of your arguments. They need to be clear, attractive, and convincing

Visualization of one or multiple sets, possibly sharing sev-eral elements, is a recurrent theme both inside and outside
the visualization community. Sometimes, depiction of the
sets is the main concern, excluding any other information
of contained elements besides some means of identification,
i.e., a label.

This is a book about programming data visualizations for
nonprogrammers. If you’re an artist or graphic designer with visual skills but no prior experience working with data or code, this book is for you. If you’re a journalist or researcher with lots of data but no prior experience working with visuals or code, this book is for you, too.

IN A WORLD governed by data, in knowledge economies where ideas are currency, visu-alization has emerged as our shared language. Charts, graphs, maps, diagrams—even
animated GIFs and emojis—all transcend text, spoken languages, and cultures to help people understand one another and connect. This visual language is used everywhere in
the world, every day.

This chapter contains our justification for spending our time, energy, and resources on fiddling with our graphs. We address the very foundation of data visualization and the choices we need to make about the best chart type to use, and when to use it. This is the backbone of our work to visualize data, the rea-soning we need to deliver to our boss when she or he asks why we are still dabbling in Excel when the report is due.

I could not have written this book without the unwavering support of my wonderful wife, Ellie, and my family. The book is dedicated to my inspirational Dad who sadly passed away before its publication. I want to acknowledge the contributions of the thousands of data visualisation
practitioners who have created such a wealth of exceptional design work and smart writing. 

You should look at your data. Graphs and charts let you explore and learn about the structure of the information you collect. Good data visualizations also make it easier to communicate your ideas and findings to other people. Beyond that, producing effective plots from your own data is the best way to develop a good eye for reading and understanding graphs—good and bad—made by others, whether presented in research articles, business slide decks, public policy advocacy, or media reports. This book teaches you how to do it.

Summary

What is exploratory data analysis? How did it begin? How and where did it originate? How is it differentiated from other data analysis approaches, such as classical and Bayesian? Is EDA the same as statistical graphics? What role does statistical graphics play in EDA? Is statistical graphics identical to EDA?

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