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by Idan Gazit
The ultimate goal of data visualization is to tell a story and supply meaning. There are tools and science that can inform your choice of data to present and how best to present it. We reflexively evaluate data and fit it into a narrative which aids decisionmaking; learn how to take advantage of this tendency in order to deliver meaning, not just numbers and charts.
Data visualization is a hot field right now—and for good reason. In our age of info-saturation, true value is found in distilling large amounts of data into a form that is easy to comprehend and act upon. This talk provides an overview of tools and techniques which you can use to level up your data presentation, regardless of application.
As humans, we are adept at evaluating visual information. From an early age, we learn to make inferences about things based on their visual properties—large and small, near and far, motion, direction, and other attributes. Taking advantage of the visual process we’ve been practicing since birth is an easy way to optimize delivery of your data into the brains of your audience.
Unfortunately, it isn’t enough to appeal to the part of our brains responsible for figuring out whether we can successfully hit an animal with a rock. A great visualization must appeal to our sense of beauty. Structure, layout, typography, and color are all tools which can be used (and abused) to delight your audience and direct their attention where you want it to go.
Whether you’re building an information dashboard for a webapp or presenting scientific data, an understanding of these techniques will make your data more accessible to your audience, and more of a delight to read and learn from.
by Jonathan Rocher
Analyzing, storing and visualizing time-series efficiently are recurring though difficult tasks in various aspects of scientific data analysis such as meteorological forecasting, financial modeling, ... In this talk we will explore the current Python ecosystem for doing this effectively, comparing options, using only open source packages that are mature yet still under active development.
Exploring and analyzing data can be daunting and time-consuming, even for data lovers. Python can make the process fun and exciting. We will present techniques of data analysis, along with python tools that help you explore and map data. Our talk includes examples that show how python libraries such as csvkit, matplotlib, scipy, networkx and pysal can help you dig into and make sense of your data.
Learn about powerful python libraries for analyzing all types of data, including spatial data, through the following illustrated examples.
Example 1: Explore data
Problem: I have a large voter data file in CSV format. I want to examine it, check the column headings and data types, and do some basic stats, but I don’t want to pull it into Excel or Access. What are my options?
Solution: csvkit - I can explore my data, chop it up, sort it, summarize it, and prepare it for import to postgis.
Bonus: Developers and journalists have been working hard to add functionality to csvkit. You can contribute!
Example 2: Analyze data
Problem: I have a bunch of data points from Twitter. How do I make sense of what I have in front of me, and where do I start?
Solutions: matplotlib, networkx
Bonus: Learn about how python libraries are plug and play with each other.
Example 3: Map data
Problem: I have a year’s worth of crime incidents for a large city. I want to explore global and local patterns in the data and identify clusters.
Solutions: PySal (Numpy, Scipy)
Bonus: We’ll look at the full ESDA (Exploratory Spatial Data Analysis) module in PySal, and we’ll briefly touch on a selection of the rest of PySal’s functionality.
To wrap up the talk, we'll give some tips on using postgis and geodjango to go from data analysis and mapping to building a web application.
7th–15th March 2012