**What is Data Visualization? How to Get a Perfect Visualization of Your Data?**

**Introduction**

In Wikipedia, the definition of Data visualization is given like this: Data visualization is the interdisciplinary field, which works with data’s graphic representation. This is a predominantly efficient way to communicate when data is frequent, for example, the Time Series. If you consider an academic viewpoint, this representation could be measured as the mapping between original data as well as graphic elements. The mapping defines how different attributes of the elements differ as per the data.

**Data Visualization and Visual Experience**

Humans can differentiate differences in shape, line length, distances, orientation, and color eagerly without substantial processing efforts; these are known as the “pre-attentive attributes”. If you take an example, it may need important time and efforts to recognize the total number of times a digit “5” comes in the numbers series; however in case, the digit is dissimilar in orientation, size, or color, examples of the digit could be noted rapidly with pre-attentive processing.

Efficient graphics take benefits of the pre-attentive attributes and processing as well as the qualified strength of the attributes. For instance, as humans can process the differences more easily for line length than the surface area, this might be more efficient to utilize the bar chart that takes benefit of the line lengths to display comparisons rather than the pie charts that utilize surface areas to display comparison.

Let’s take detailed examples of the diagrams utilized in the Data Visualization scenario.

**1. Bar Chart**

**Measurements:**

· Category

· Color

· Length/Count

**Description & Examples of Use**

Provides categorical data using rectangular bars having lengths or heights proportional for the values represented. These bars could be plotted horizontally or vertically.

Any bar graph provides comparisons amongst different categories. One axis in the chart indicates particular categories getting compared as well as another axis denotes a precise value.

Certain bar graphs represent bars clustered with the groups of above one, showing values of over one measured variable. These bunched groups could be differentiated through color.

For instance; a comparison of different values like sales performances for numerous businesses or persons in a single period.

**2. Histogram**

**Measurements:**

· Bin Limits

· Color

· Count/Length

**Description & Examples of Use**

A rough representation of the delivery of mathematical data. Divide the whole variety of values into the series of breaks and then sum how different values fall under every interval, which is named binning. These bins are generally specified as successive, non-overlapping intervals about the variables. The intervals need to be nearby and are generally of the equal size.

For instance, determining frequency about annual percentage returns of the stock market in particular ranges like 0–10% or 11–20%, etc. The bar height represents a total number of years in the returns % within the range characterized by respective bins.

**3. Scatter Plot**

**Measurements:**

· X-Position

· Y-Position

· Color

· Size

· Symbol/Glyph

**Description & Examples of Use**

Utilizes Cartesian coordinates for showing values for generally two variables about the data set.

Points could be coded through shape, color, and size to show extra variables.

Every point on a plot has the related X and Y terms, which determine its locations on the Cartesian plane.

Often scatter plots are used for highlighting correlations between the variables (X & Y).

**4. Scatter Plot (3D)**

**Measurements:**

· Position X

· Position Y

· Position Z

· Color

· Size

· Symbol

**Description & Examples of Use**

Similar to a 2-dimensional scatter plot given above, a 3-dimensional scatter plot envisages the relationship among usually 3 variables from the data set.

Again point could be coded through shape color, and size to show extra variables

**5. Network**

**Measurements:**

· Node’s Color

· Node’s Size

· Spatialization

· Ties Color

· Ties Thickness

**Description & Examples of Use**

Getting clusters in a network (aligning Facebook friends in various clusters).

Determining bridges (boundary spanners or data brokers) between the clusters in a network

Defining the most important nodes in a network (For example, a company needs to target the smaller group of people on Twitter to do some marketing campaigns).

Getting outlier actors that are not suitable for any clusters or are within the boundary of the network.

**6. Pie Chart**

**Measurements:**

· Color

**Description & Examples of Use**

Signifies one definite variable that is divided into slices to show numerical proportions. In the pie chart, the arc’s length of every slice (as well as subsequently its central area and angle), is relational to the quantities it represents.

For instance, as given in this graph to right, the amount of worldwide English native speakers

**7. Line Chart**

**Measurements:**

· X-Position

· Y-Position

· Color

· Size

· Symbol/Glyph

**Description & Examples of Use**

Provides information as the series for data points named ‘markers’ associated with straight-line segments.

Close to the scatter plot excluding that measurement points get ordered (usually by the X-axis value) as well as joined with the straight-line segments.

Generally used for visualizing the data trends over the time intervals — a timely series — therefore the line is usually strained chronologically.

**8. Streamgraph**

**Measurements:**

· Color

· Time (flow)

· Width

**Description & Examples of Use**

One kind of arranged area graph that is displaced with the central axis and resulted in the flowing shape.

Different from an old-fashioned arranged area graph where the layers get stacked on the top of any axis, in the streamgraph, different layers get positioned for minimizing the “wiggle”.

Streamgraphs show data with positive values as well as are not capable of representing both positive and negative values.

For instance, the correct visual shows music listened to by the user at the beginning of the year 2012.

**9. Treemap**

**Measurements:**

· Color

· Size

**Description & Examples of Use**

Is the method to display hierarchical data through nested figures, normally rectangles?

For instance, disk space through a location or file type

**10. Gantt Chart**

**Measurements:**

· Color

· Time (Flow)

**Description & Examples of Use**

Kind of bar charts, which illustrates the project schedule

Contemporary Gantt charts show the reliance relationships between the activities as well as present schedule position.

For instance, used in the project planning

**11. Heat Map**

**Measurements:**

· Categorical Variables

· Color

**Description & Examples of Use**

Characterizes the magnitude of the phenomenon as colors with two Measurements.

You can have two categories for heat maps:

**Spatial Heat Maps:** were no medium of fixed cells size, for instance, a heat map presenting population densities showed on the geographical map

**Cluster Heat Map:** where different magnitudes are placed into the medium of fixed cell size whose columns and rows are unconditional data. For instance, the graph given on the right.

**12. Stripe Graphic**

**Measurements:**

· X Position

· Color

**Description & Examples of Use**

Utilizes the series of chronologically ordered colored stripes to visually represent long-term temperature styles.

Portrays the single variable with prototypically temperature to represent global warming

Knowingly minimalist — without any technical indicia for communicating instinctively with the non-scientists

Could be “stacked” for representing the plural series

**13. Animated Spiral Graphics**

**Measurements:**

· Color (All Passing Years)

· Radial Distance (Reliant Variable)

· Rotating Angle (Cycling with Months)

**Description & Examples of Use**

Portrays one dependent variable having prototypically temperature to describe global warming

The reliant variable is gradually plotted along a constant “spiral” resolute as the function of continuously rotating angles twelve months for every revolution as well as evolving color as color changes with passing years.

**14. Box & Whisker Plots**

**Measurements:**

· X-axis

· Y-axis

**Description & Examples of Use**

A method for graphically depicting groups of precise data using quartiles.

Various box plots could also have different lines scattering from the boxes representing inconsistency outside upper as well as lower quartiles.

Outliers could be prearranged as distinct points.

A couple of boxes graphed on the top characterize the mid 50% of data having the lines separating two boxes recognizing the middle data value as well as the bottom and top edges of boxes provide the 25th and 75th percentile of data points respectively.

Different box plots are non-parametric as they show variations in samples about the statistical populations without doing any assumptions about fundamental statistical distribution, therefore are suitable for having an early understanding of data sets. For instance, comparing distributions of the ages between groups of people.

**15. Flowchart**

**Measurements:**

· Process or Workflow

**Description & Examples of Use**

Characterizes a workflow, procedure, or a step-by-step method to solve a task.

Here, a flowchart displays the steps like boxes of different types as well as their order through connecting boxes using arrows.

For instance, outlying actions for undertaking if the lamp isn’t working, as given in the drawing to the right.

**16. Radar Chart**

**Measurements:**

· Attributes

· Value Given to the Attributes

**Description & Examples of Use**

Shows multivariate data with a two-dimensional chart having three or more measurable variables given on the axes beginning from similar points.

The comparative position, as well as angle of axes, is normally uninformative however, different heuristics like algorithms, which plot data like the best total area, could be applied for sorting the variables into comparative positions, which reveal separate trade-offs, correlations, as well as different other proportional measures.

For instance, comparing attributes or skills (like analytical, communication, and IT skills) cultured across various university degrees (like economics, psychology, mathematics)

**17. Venn Diagram**

**Measurements:**

All the possible logical associations between determinate collections of various sets.

**Description & Examples of Use**

Displays all possible rational relations between the predetermined collections of various sets.

All these diagrams represent elements as the points in-plane as well as sets as districts within closed curves.

The Venn diagram includes different overlying closed curves, generally circles, every circle representing the set.

The given points within the curve considered S represent the elements of set S, whereas points outside a boundary represent the elements, not within a set S. It results in itself in instinctive visualizations; for instance, a set of different elements, which are the members of sets S as well as T, represented S ∩ T as well as read as “connection of S & T”, is characterized visually by an area of overlapping the areas S as well as T. In the Venn diagrams, different curves get overlapped in all possible ways, showing different possible relationships between data sets.

Resource: https://en.wikipedia.org/wiki/Data_visualization

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