Data Visualization in Data Science Institutes

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Data visualisation can be defined as data representation through graphics like animations, infographics, plots, and charts. The visual information displays help to communicate the most complex data relationships.

We can utilise data visualisation for different purposes, and one should note that this is not only applicable to data teams. Data visualisation can be used by management to convey its hierarchy and structure. It is the best way to discover and explain different trends and patterns for data scientists and analysts.

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Data visualisation can be categorised into four primary purposes:

Idea generation
Idea illustration
Visual discovery
Everyday viz

Idea generation

This is usually intended to spur the generation of ideas from different teams. They are leveraged mainly during design thinking and brainstorming sessions when a project starts. The main thing is collecting various perspectives and highlighting the most common concerns. In many cases, ideas are unrefined and unpolished, but they are important in creating a foundation for projects to ensure everyone is aligned on the problem.

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Idea illustration

In this case, data visualization helps convey ideas like processes or tactics. This is usually used in different learning settings like centers of excellence, certification courses, and tutorials. However, it may be used to represent the processes and structures of an organization to facilitate communication between individuals for some tasks. For example, it is common for project managers to use waterfalls charts and Gantt charts to show workflows. With data modeling, abstraction can be used to make a better representation to assist in understanding the data flow in an organization. This makes data architects, business analysts, and developers have an easier time understanding data warehouse and database relationships.

Visual discovery and everyday viz

These two are closely aligned with different data teams. Visual discovery is needed by data scientists and other professionals for trends and pattern identification in a data set. With everyday data viz, subsequent storytelling is supported once a new insight is found.

Data visualization in data science

In data science, data visualization is a very important step. It is one of the best ways to help individuals and teams to convey data more effectively to decision-makers and colleagues. In addition, those who manage reporting systems usually leverage a defined template to help monitor performance. That said, it is essential to note that data visualization is not limited to dashboards. In text mining, for example, analysts may use word cloud to capture concepts, hidden relationships, and trends in unstructured data. Graph structures can also illustrate relationships between those in a knowledge path. Therefore, you must appreciate this skillet’s importance to data scientists.

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Visualisation types

We can trace data visualization in its earliest form in Egypt. Here, it was mainly used for navigation. Over time, data visualization was leveraged for more health, social, and economic applications. Some of the most common types of data visualization that a data science ought to be conversant with include:

Tables: in this case, we have columns and rows used for variable comparisons. Tables can give us a lot of information in the most structured way. However, it can be overwhelming for those who want to know high-level trends.
Stacked bar and pie charts: the graphs, in this case, are divided into different sections that represent a whole. They are designed to offer a simple but effective data organization method. Each component size is closely compared.
Area and line charts: these visuals are an excellent way to show changes in different quantities. This is done by plotting data point series over a given time. These are used frequently within predictive analytics. For line graphs, lines are used to show changes. Area charts, on the other hand, connect different data points using line segments. Variables are stacked on each other, and the color is used to distinguish the variables.
Histograms: this is a graph that plots number distribution by making use of bar charts without spaces. This represents data quantity falling in a particular range. With such a visual, it becomes easier for the end users to identify a data set’s outliers.
Scatter plots: the visuals help reveal relationships between different variables. They are mainly used in regression data analysis. Sometimes, people confuse bubble charts and scatter plots. The former visualizes 3 variables through the x and y-axis.
Heat maps: the graphical representation, in this case, can be beneficial in visualizing data that is behavioral-based on location. This can be a web page and map location.
Treemaps: these are used to display hierarchical data. Nested shapes are used for this visualization which is usually rectangles. This is a good way of comparing different proportions between categories through area size.

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Best practices in data visualization

So many visualization tools can be used today, and they are readily available. However, there is still ineffective information visualization today. Visual communication needs to be deliberate and straightforward. This is the only way to ensure that it will help the target audience get the desired conclusion or insight. Some of the best practices include:

- Setting the context: in his case, you need enough background information to help the audience realize why a particular data point is needed or is essential. In the case of underperforming email rates, for example, you need to compare that to the industry performance in general. Again, this helps identify the issue with your marketing channel.
- Get to know your audience: consider the data visualization design and ensure that customer needs are met. This can only be achieved by knowing your audience better.
- Pick the right visual: different visuals fit different data sets. For example, line graphs can be great for time series, while scatter plots ate the best for variable relationships. Examine your data and what you want to chive with a visual to select the right one.
- Keep things simple: you can do so much with data visualization tools. However, keeping it simple makes it easier to relate to and understand.

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Bottom line

Data visualization is an integral part of data science that we cannot ignore. As such, you must be careful with how you represent data. It can make a big difference. Data visualization in data science in Bangalore institutes is an important area that should be covered well. This is one of the most important skillets a data scientist should have.