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Data Analyst Certification

Visual analytics: the need, model, tools and applications

It’s the age of data and the amount of data available with companies today is mind-boggling. Looking at vast excel sheets with enormous data can be daunting for the best of even top management. Also, given the time crunch situation that most top management employees are under all the time, it is highly desirable to get all the necessary information at a glance. This is possible with the help of visual analytics.

 

Understanding visual analytics

Visual analytics is putting a number of data points together in a pictorial form. This pictorial form could be bar charts, pie charts, line charts or any other pictorial representation of data. While statistical methods give accurate information, understanding them is not a piece of cake for common users and at times even top management. Too much data can befuddle the user and render them unable to take business decisions. This leads to the need for visualization. Visual analytics is thus a visual representation of data. Users can directly interact with the tool to draw insights, interpret data and take decisions.

 

What is the need?

As discussed in the introduction, this is the age of data. This data is increasing exponentially due to the proliferation of the internet and digitalization of businesses across the board. With several data touchpoints like smartphones, websites, and other smart technologies, the amount of data to read and interpret can get frustrating and confusing. Thus, there is a need for a tool that can visually represent data and can help in easily understanding the data as well as taking relevant decisions.

 

The Visual Analytics Model

When a piece of data is represented visually, it can be understood in several dimensions and through several perspectives. Several different models can be explored by the user and different kinds of information can be culled. The most important thing, though, is to decide what data sets should be considered to get the desired results. The various steps involved include cleaning of data, normalization, grouping and integrating different sources of data. The next step is to assess the modelling technique best suited to solve the problem. With visual analytics, basic modelling techniques are used and they provide statistical indicators through visualizations. These final aids in taking the decisions.

 

Big data leads to enhanced need

With big data, the amount of data is growing at unprecedented speed. These data sets are even more complex than traditional data sets. Therefore, it is difficult to handle these data sets using traditional techniques. Apart from the volume and speed of data collected, there is a requirement to make data valuable. The data is not collected from a single source but comes from multiple heterogeneous sources and hence, is not necessarily structured. The data has to be true as well as applicable to the problem being solved. All these needs combine to enhance the requirement for visual analytics.

 

Tools in Visual Analytics

One of the best platforms for data visualization in Tableau. It is simple to use with a highly efficient drag and drop function on its main interface being its key attraction. Its basic version is free to use for students and teachers at institutes which are duly accredited. The advanced features of the tool come at a price.

SAS visual analytics is another great tool and it has been around for a very long time. It is greatly helpful in advanced analytics. The tool is quick in interpreting vast and complex data sets and does so with a great degree of accuracy.

R is another tool for data visualization with its package called Shiny. This package delivers interactive dashboards. With the use of JavaScript, this tool allows for the creation of interfaces which can change dynamically.

 

Applications of visual analytics

There are several uses of visual analytics in the corporate as well as government space.

  • Criminal investigation: Visual analytics is used in the criminal investigation. For example, by creating a visual map of funds transfers across various financial institutions and transfer of funds abroad to specific accounts, criminal investigation agencies can find out the mastermind for various illegal activities such as drug trading, bomb blasts etc.
  • Retail industry: Retail industry is huge and hence a number of homogeneous and heterogeneous data touchpoints. The number of bills or invoices can be mind-boggling. With the use of visual analytics, they can easily understand the requirement for inventory, correct product mix, seasonal stock requirements and market-wise stock requirements.
  • Financial analysis: Visual analytics helps to understand growth trends, return on investment, revenue sources and the percentage revenue per source and other financial parameters with ease.
  • Other applications: Network security, high dimension analysis, subspace analysis, molecular biology etc. are some of the other examples where visual analytics finds the application.

 

Getting a data analytics certification can help you get a job in the field of analytics and/or visual analytics.

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