FAQ About Data Visualization

Data visualization is the process of taking raw data and transforming it into graphs, charts etc.

In this way, the data becomes easy to understand for people who do not understand data science. 

First, it makes the data easier to understand. Since we can see the data as desired, we can detect trends, discover new patterns, making it easier to comment on the data. 

It can be used to highlight important parts of the data. It can be used to discover trends. It can be used to make the story contained in the data more understandable. It can also be used to discover outliers or trends. 

• Infogrphics
• Charts
• Diagrams
• Maps

In addition, each type has different varieties within itself. 

There are many tools available for data visualization. These may vary according to the individual's preference. To give a few examples; 

• Tableau
• The Atlas
• Power BI
• Python
• Flourish
• Looker
• Raw Graphs
• Qlik Sense

The first ones that come to mind are Matplotlib and Seaborn. In addition to these two libraries, there are also libraries that can be used, more precisely, that will support visualization. 

Data visualization is used to make meaningful comments about the data and to understand the data easily. Data visualization is important to better analyze and make sense of data.

There are many tools for data visualization. First of all, you can start data visualization by choosing one of these tools and learning the details about this tool.

You can also progress more easily in this area by working with data that interests you at the beginning.

You can also continue to improve yourself by joining a community.

With the increasing interest in data recently, the visualization of data has gained importance. Therefore, it can be considered as a good career path. Of course, personal attention is also important in this sense.

Although it can change according to the program you use, you can visualize data without writing any code. There are programs specifically programmed for this. Thanks to these programs, you can visualize data without writing code, just by learning the interface of the program.

Knowing how to ask the right questions is one of the top qualities. It is also important to be knowledgeable and relevant in this area in order to make sense of the data in the first place. In addition, it is important to simplify it so that even people who do not understand data can understand it. Finally, being good at storytelling can make things easier.

It can be improved by constantly researching and acquiring new information from different sources. In addition, it would be good to develop not only in terms of data, but also in terms of visualization and design in general.