Data Visualization

Data Visualization is a method to understand the data. It is a more efficient and better way than reading and judging the trends in data of huge volume.

In Big Data, it plays a crucial role while examining the underlying patterns in the data. It is defined as the study of representing data/information in a visual format.

Visuals can be in form of charts, graphs, diagrams, images etc. A more abstract and model based visualization approach is crucial for a better instructional design in data visualization.

Example of Data Visualization using python and seaborn

How to represent Data

Data can be represented visually by using many visualization techniques. Use of interactive and color represented data visualization helps any  user to interpret the data in without ambiguity. Some advantages of are:

  • Easier for people to interpret big data at a glance.
  • Easier to identify outliers and errors in data.
  • Easier to explore data and get actionable insights.
  • Visual representations are a great way to establish relationships between data points to find patterns.

Data is usually represented visually in the following ways:


Graphs are representation of data in X-Y axes to understand the meaning of data.


Diagrams are 2D representation of data


Timeline is a representation of crucial events in a sequence of time.


Checklist visual representation is used for comparison and verification purposes.


Template is used to represent information for some 3rd party to analyse and explain it.


Flowchart is a representation of instructions or a procedure.

Types of Data Visualization

  • 1D- Linear or 1D visualization includes the visual representation of data in form of lists.
  • 2D- 2D or planar is a technique used to present data in form of images, diagrams, maps charts etc (or data with multiple columns and rows). Some tools for 2D representation are Google Fusion tables, Tableau etc.
  • 3D- 3D representation includes 3 dimensions i.e. X, Y and Z to show simulations, renderings etc. Some important tools for 3D data visualization are AutoCad, TrueSpace and so on.
  • Temporal- Temporal data visualizations are time dependent such as time series, Gantt chart etc. Some important tools to create Temporal Diagrams are Tableau, Google Charts etc.
  • Multidimensional-Multidimensional visualization includes numerous dimensions of data that can be visualized with the help of charts such as histograms, pie chart, bar chart etc. Tableau and Google Charts are great tools to perform multidimensional visualization.
  • Network-Network visualization represents relationships among several objects in data. Mostly, the data relations are in the form of hierarchical data which is somewhat complex. Google charts and d3 are great examples of platforms to develop network data visualization.
  • Hierarchical-The data points that exist in hierarchies can be represented by using Hierarchical visualization. Trees are used in this type of visualization.

Techniques used in Data Visualization


These are 2D representations that may include bar graphs, tables and other data diagrams. It is more inclined towards data arrangement rather than data visualization through analysis.


A Map is a visual representation of locations over a certain area depicted by a planar surface.

Venn Diagrams

Venn Diagrams are derived from Relations and Sets in mathematics that represents logical relationships between data points.


Cluster is used to represent a collection of entities with similar properties or values.

Event Timeline

Timeline is used to represent a chronological display of events. Various critical events are displayed in a Timeline

Streamline Chart

It represents data flow as a result of velocity vector field through multiple field lines.

Application Areas

  • Science and Technology- Visual representation of scientific results and information is critical for the lay man or even scientists to understand them in the best way.
  • Analytics-Analytical reasoning can be easily deduced from visual analytics to make the best decisions based upon the results. Data analytics and visualization usually go hand in hand.
  • Education-Simulation/Visual modeling methods are used for teaching a specific topic as visual methods are far easier than understand than plain text.
  • Production-3D modeling of products will give a better way of understanding its specifics.
  • Systems – It integrates visual techniques with complex systems.