Visualization

1-Challenge :    The last years witnessed a continued growth of the amount of data. The data analysis and

exploration has become more and more difficult. And personally I have that big problem of  understanding and remembering all the data so I have a less concentration in my course  So, it seems  important to find means to visually represent this flood of data which can help any user to get and  understand information efficiently and implicate him/her in the data mining process .

2-Selection :

I choose the first tool :The visualization domain who proposes a large number of information

visualization techniques which have been developed over the last decade to support the

exploration of large data sets and to achieve a shared understanding of a problem space

among a diverse group of stakeholders before attempting to identify and define possible

solutions to the problem so The visualization is more powerful and useful for example

Comparing numbers presented in a flat table to find the top value is extremely difficult and

takes a long time. This is where visualizing your data through graphical representation can

make your life easier. If you plot the data pictorially using widgets, finding the top value from

a year’s worth of data is possible in a few seconds, maybe even a fraction of a second.

3-Application:

Personally I apply a visualization in my studies for example the visualization for the physical

sciences. ie introduce novel classification schemes based on application area, data dimensionality and

main challenge addressed, and apply these classifications to each contribution from the literature. my

survey helps in understanding the status of current research and serves as a useful starting point for

those interested in visualization for the physical sciences.

4-Insight & Approach :

A primary purpose of a visualization tool is to provide domain-relevant insight into the data.

Ideally, any user wants maximum information in the least possible time. In this paper we

identify several distinct characteristics of insight that enable us to recognize and quantify it.

Based on this, we empirically evaluate five popular microarray visualization tools. Our

conclusions can guide biologists in selecting the best tool for their data, and computer

scientists in developing and evaluating visualizations

And this is What I have learned from conducting a visualization :

•  Better analysis.

•  Quick action.

•  Identifying patterns.

•  Finding errors.

•  Understanding .

•  Exploring business insights.

•  Grasping the Latest Trends.    Since I discovered that the most valuable aspect is the shared insights and learning, next time I would  use the visualization activity as a starting point for a series of design thinking activities in many  domains

