Requirements: .ePUB reader, 32.4 MB
Overview: Radically improve the quality of your data visualizations by employing core principles of color, typography, chart types, data storytelling, and more. Everyday Data Visualization is a field guide for design techniques that will improve the charts, reports, and data dashboards you build every day. Everything you learn is tool-agnostic, with universal principles you can apply to any data stack. This book gives you the tools you need to bring your data to life with clarity, precision, and flair. You’ll learn how human brains perceive and process information, wield modern accessibility standards, get the basics of color theory and typography, and more. One of the most popular and powerful data visualization libraries for JavaScript is the free and open-source d3.js, also known as D3 or d3, which stands for “Data-Driven Documents.” If JavaScript and its asynchrony aren’t your jam, perhaps you’d prefer Python or R. While Python is an entire programming language, and R is a software environment specifically made for statistical analysis, both are free and open source, have a cult-like following among coders, and are great for data viz practitioners. Both R and Python have very friendly and approachable syntax, hence their considerable popularity. If you’re a statistician or data scientist, you probably already know R, and if you’re not, I’d wager you’d prefer Python. As for libraries to use in each, R users adore their ggplot/ggplot2 and Shiny, while Python users have many other options, including Seaborn, Plotly, and Matplotlib.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
https://ouo.io/QGRqao
https://ouo.io/72d1AL.
