26 February 2018
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Analyzing data is not a goal unto itself; there is a larger objective: to provide useful information and make business recommendations, all in support of decision-making.
Thus, after having implemented the skills acquired in data analytics courses, i.e. having mined, collected, cleansed and processed the data, comes the next phase - presenting the data.
In order to achieve the goal of facilitating decision-making, the results of the data analysis must be clear and sensible. Hence: data visualization.
Data visualization is essentially the presentation of data in a visual way. It enables decision makers to see analytics visually, grasp complex concepts or identify new patterns, trends and correlations that would have gone undetected otherwise. It is an important accomplishment of any analytics course graduate.
Today's data visualization tools go far beyond the standard charts and graphs of regular spreadsheets. They can display data in various elaborate ways like:
These may presently include interactive capabilities, in order to enable users to manipulate data or drill into it for questioning and analysis.
As mentioned, data visualization is not just for data analysts. There are many data scientists, mostly graduates of a data science course such as a python data science training course, who also find a lot of value in the current tools of data visualization. The ability to convey the results of their data science research and transform them into actionable items, can help the other team members implement these conclusions into the business and processes of the company.
Data visualization is changing the way analysts work with data. They’re expected to process data with the tools at hand (statistics, python for data analysis etc.) and then respond rapidly to issues. And they’ll need to be able to hunt for more insights and look at data differently and ingeniously. Data visualization is the key for creative data exploration; it is the right tool for the job.