top of page

In The Mix with Intermix

Public·19 members
Adrian Nelson
Adrian Nelson

Data Points: Visualization That Means Something



Nathan Yau has a PhD in statistics and is a statistical consultant who helps clients make use of their data through visualization. He created the popular site FlowingData.com, and is the author of Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, also published by Wiley.




Data Points: Visualization That Means Something



Whether it's statistical charts, geographic maps, or the snappy graphical statistics you see on your favorite news sites, the art of data graphics or visualization is fast becoming a movement of its own. In Data Points: Visualization That Means Something, author Nathan Yau presents an intriguing complement to his bestseller Visualize This, this time focusing on the graphics side of data analysis. Using examples from art, design, business, statistics, cartography, and online media, he explores both standard-and not so standard-concepts and ideas about illustrating data.


Scientists, probably more than most, are aware of the ever-increasing presence of data visualization in newspapers, television, online shopping, social media and even policy-making sales pitches in the US Congress. I'll bet there's a brand new office building in Washington DC devoted to creating chart-laden poster boards for congressional members. Statistician and visualization expert Nathan Yau's Data Points: Visualization That Means Something is a clear and passionate exploration of this burgeoning phenomenon.


Whether it's statistical charts, geographic maps, or the snappy graphical statistics you see on your favorite news sites, the art of data graphics or visualization is fast becoming a movement of its own. In Data Points: Visualization That Means Something, author Nathan Yau presents an intriguing complement to his bestseller Visualize This, this time focusing on the graphics side of data analysis. Using examples from art, design, business, statistics, cartography, and online media, he explores both standard-and not so standard-concepts and ideas about illustrating data.


Nathan Yau has a PhD in statistics and is a statistical consultant who helps clients make use of their data through visualization. He created the popular site FlowingData.com, and is the author of Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, also published by Wiley.


Yau is known for his blog FlowingData in which he publishes writing and tutorials on information design and analytics, as well as visualizations and data science-related projects created by other professionals.[3][8][9]


This is what context can do. It can completely change your perspective on a dataset, and it can help you decide what the numbers represent and how to interpret them. After you do know what the data is about, your understanding helps you find the fascinating bits, which leads to worthwhile visualization.


Without context, data is useless, and any visualization you create with it will also be useless. Using data without knowing anything about it, other than the values themselves, is like hearing an abridged quote secondhand and then citing it as a main discussion point in an essay. It might be okay, but you risk finding out later that the speaker meant the opposite of what you thought.


Who: A quote in a major newspaper carries more weight than one from a celebrity gossip site that has a reputation for stretching the truth. Similarly, data from a reputable source typically implies better accuracy than a random online poll.


What: Ultimately, you want to know what your data is about, but before you can do that, you should know what surrounds the numbers. Talk to subject experts, read papers, and study accompanying documentation.


Why: Finally, you must know the reason data was collected, mostly as a sanity check for bias. Sometimes data is collected, or even fabricated, to serve an agenda, and you should be wary of these cases. Government and elections might be the first thing that come to mind, but so-called information graphics around the web, filled with keywords and published by sites trying to grab Google juice, have also grown up to be a common culprit. (I fell for these a couple of times in my early days of blogging for FlowingData, but I learned my lesson.)


In 2010, Gawker Media, which runs large blogs like Lifehacker and Gizmodo, was hacked, and 1.3 million usernames and passwords were leaked. They were downloadable via BitTorrent. The passwords were encrypted, but the hackers cracked about 188,000 of them, which exposed more than 91,000 unique passwords. What would you do with that kind of data?


Author BioNathan Yau, author of Data Points: Visualization That Means Something, has a PhD in statistics and is a statistical consultant who helps clients make use of their data through visualization. He created the popular site FlowingData.com, and is the author of Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, also published by Wiley.


Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Additionally, it provides an excellent way for employees or business owners to present data to non-technical audiences without confusion.


The importance of data visualization is simple: it helps people see, interact with, and better understand data. Whether simple or complex, the right visualization can bring everyone on the same page, regardless of their level of expertise.


While traditional education typically draws a distinct line between creative storytelling and technical analysis, the modern professional world also values those who can cross between the two: data visualization sits right in the middle of analysis and visual storytelling.


See our list of great data visualization blogs full of examples, inspiration, and educational resources. The experts who write books and teach classes about the theory behind data visualization also tend to keep blogs where they analyze the latest trends in the field and discuss new vizzes. Many will offer critiques on modern graphics or write tutorials to create effective visualizations. Others will collect many different data visualizations from around the web in order to highlight the most intriguing ones. Blogs are a great way to learn more about specific subsets of data visualization or to look for relatable inspiration from well-done projects.


Read our list of great books about data visualization theory and practice. While blogs can keep up with the changing field of data visualization, books focus on where the theory stays constant. Humans have been trying to present data in a visual form throughout our entire existence. One of the earlier books about data visualization, originally published in 1983, set the stage for data visualization to come and still remains relevant to this day. More current books still deal with theory and techniques, offering up timeless examples and practical tips. Some even take completed projects and present the visual graphics in book form as an archival display.


He recommends a very logical way of processing this data. First, choose the right data and only then find the right visualization method for it (like a bar chart, pie chart, treemap, line plot, or scatter plot). Only this way will you be able to find a match and make the answers to your questions clear.


So, when creating your data project, organize your data points according to their importance. You can do that simply by assigning each element a number (say, 1-10). Then, try to reflect that order in your designs. Below, I listed some elements that can help you do just that:


All of the points above essentially lead to this one. Because, just as text needs to be readable, so does data and visual information. Here are some solutions Nathan Yau suggests you can employ to achieve that:


Author BioNathan Yau, author of Data Points: Visualization That Means Something,has a PhD in statistics and is a statistical consultant who helps clients make use of their data through visualization. He created the popular site FlowingData.com, and is the author of Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, also published by Wiley.


Data visualisation / data visualization (dataviz) is an umbrella term for converting data sources into a visual representation (can include charts, spreadsheets graphs, maps, tables, animations, data art). In short, it is the process used to create data graphics.


Good data visualisation illuminates patterns and tells a research story. According to Yau*, the best data visualisation, "evokes that moment of bliss when seeing something for the first time, knowing that what you see has been right in front of you, just slightly hidden...and enables you to see trends, patterns, and outliers that tell you about yourself and what surrounds you".


In general terms, this means that you should feel free to use any of the materials on this site for your own classes or personal use (with attribution) but you may not use it for profit. You don't have to ask permission, but I always enjoy knowing when an exercise or idea has come in handy for someone else!


We use cookies in order to improve the quality and usability of the HSE website. More information about the use of cookies is available here, and the regulations on processing personal data can be found here. By continuing to use the site, you hereby confirm that you have been informed of the use of cookies by the HSE website and agree with our rules for processing personal data. You may disable cookies in your browser settings.


I am trying to construct a model that predicts stock price volatility on a given day based on data points represented as strings that may or may not be present on that day. My hypothesis is that certain combinations of these data points correlate to different levels of volatility in stock price, but I don't know what those combinations are. Out of about 2100 unique potential data points, there will only be 10-20 on a given day. Therefore, I'm looking for a method that can visualize/display the rate of cooccurence out of a grab-bag of these data points bucketed by volatility. 041b061a72


About

Welcome to the group! You can connect with other members, ge...
bottom of page