Data Visualization

Data visualization is one of the key components in understanding data.

8 Dashboards

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Category: { Data Visualization }
Summary: Building interactive dashboards is not easy task. However, with the right tool, we can build a prototype fast. Theories Dashboard building seems to be a task to build whatever charts the business would like to add. However, theories are required to build quality dashboards1. AmNeumarkt/253 I wrote a comment about this: AmNeumarkt/253. Creating visualizations seems to be a creative task. At least for entry-level visualization tasks, we follow our hearts and build whatever is needed. However, visualizations are made for different purposes. Some visualizations are simply explorations and for us to get some feelings on the data. Some others are built for the validation of hypotheses.
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6 Boxplot

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Category: { Data Visualization }
Summary: Example The Whiskers in Boxplot They are the outlier data points. Outliers are determined using the interquatile range (IQR, i.e., 25 percentile to 75 percentile.). We usually the lowest data point within 1.5 IQR range below the 25 percentile or the data point within 1.5 IQR range above the 75 percentile.
Pages: 7

5 Histogram

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Category: { Data Visualization }
Summary: Suppose we check out the burger prices at the stores of Han im Glück, we get a list of numbers. We can arrange the numbers into bins of prices. For example, we can count the number stores that have a price between 10 to 11 euros.
Pages: 7

4 Bin Size of Histogram

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Category: { Data Visualization }
Summary: [[Histograms]] Histogram Suppose we check out the burger prices at the stores of Han im Glück, we get a list of numbers. We can arrange the numbers into bins of prices. For example, we can count the number stores that have a price between 10 to 11 euros. are good for understanding the distribution of your data. The Bin Size Problem As an example, we will use the following series as an example. [1.45,2.20,0.75,1.23,1.25,1.25,3.09,1.99,2.00,0.78,1.32,2.25,3.15,3.85,0.52,0.99,1.38,1.75,1.21,1.75] If we use bin size 1, we get this spiky chart and it is not so informing. We could also set bin size to 2.
Pages: 7

3 Anscombe's quartet

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Category: { Data Visualization }
Summary: Anscombe’s Quartet Anscombe’s quartet is a brilliant idea that shows the importance and convenience of visual representation of data. Anscombe’s quartet has four datasets. The values of each dataset are shown below. x1 = [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5] y1 = [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68] x2 = [10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0] y2 = [9.14, 8.14, 8.74, 8.77, 9.26, 8.1, 6.13, 3.1, 9.13, 7.26, 4.74] x3 = [10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0], y3 = [7.46, 6.
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2 Principles of Colors

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Category: { Data Visualization }
Summary: ColorTeller I wrote a python package called colorteller to help us manage and benchmark colors. Basic Concepts of Colors Color Wheel and Color Sphere There are two dimensions in the color wheel: Hue Saturation When we add another dimension, lightness, to the wheel, we have a color sphere (1, 2). Many color systems have been invented. Color wheel and color sphere are two examples of them.
Pages: 7

1 Principles of Design

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Category: { Data Visualization }
Summary: There are many principles of designing a visual representation of data. However, before we understand how data is represented visually, it would benefit us a lot if we understand the basic principles of designing on 2D surface. Robin’s CRAP Robin Williams proposed the four elements of design: Contrast Repetition Alignment Proximity Contrast Use some contrast to distinguish the elements of different contents. Repetition Repeat the design of similar elements on the same page and across pages to make sure the readers learn the meaning of the design quickly. Alignment Find a strong line and stick to it.
Pages: 7